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0 | Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising | Haijin Zeng, Jiezhang Cao, Kai Zhang, Yongyong Chen, Hiep Luong, Wilfried Philips | null | Hyperspectral images (HSIs) have extensive applications in various fields such as medicine agriculture and industry. Nevertheless acquiring high signal-to-noise ratio HSI poses a challenge due to narrow-band spectral filtering. Consequently the importance of HSI denoising is substantial especially for snapshot hyperspe... | [] | [] | [] | [] | 0 | |
1 | Seeing the World through Your Eyes | http://arxiv.org/abs/2306.09348 | Hadi Alzayer, Kevin Zhang, Brandon Feng, Christopher A. Metzler, Jia-Bin Huang | 2,306.09348 | The reflective nature of the human eye is an under-appreciated source of information about what the world around us looks like. By imaging the eyes of a moving person we capture multiple views of a scene outside the camera's direct line of sight through the reflections in the eyes. In this paper we reconstruct a radian... | [] | [] | [] | [] | 1 |
2 | DPMesh: Exploiting Diffusion Prior for Occluded Human Mesh Recovery | http://arxiv.org/abs/2404.01424 | Yixuan Zhu, Ao Li, Yansong Tang, Wenliang Zhao, Jie Zhou, Jiwen Lu | 2,404.01424 | The recovery of occluded human meshes poses challenges for current methods due to the difficulty in extracting effective image features under severe occlusion. In this paper we introduce DPMesh an innovative framework for occluded human mesh recovery that capitalizes on the profound knowledge about object structure and... | [] | [] | [] | [] | 2 |
3 | Ungeneralizable Examples | http://arxiv.org/abs/2404.14016 | Jingwen Ye, Xinchao Wang | 2,404.14016 | The training of contemporary deep learning models heavily relies on publicly available data posing a risk of unauthorized access to online data and raising concerns about data privacy. Current approaches to creating unlearnable data involve incorporating small specially designed noises but these methods strictly limit ... | [] | [] | [] | [] | 3 |
4 | LaneCPP: Continuous 3D Lane Detection using Physical Priors | Maximilian Pittner, Joel Janai, Alexandru P. Condurache | null | Monocular 3D lane detection has become a fundamental problem in the context of autonomous driving which comprises the tasks of finding the road surface and locating lane markings. One major challenge lies in a flexible but robust line representation capable of modeling complex lane structures while still avoiding unpre... | [] | [] | [] | [] | 4 | |
5 | CityDreamer: Compositional Generative Model of Unbounded 3D Cities | http://arxiv.org/abs/2309.00610 | Haozhe Xie, Zhaoxi Chen, Fangzhou Hong, Ziwei Liu | 2,309.0061 | 3D city generation is a desirable yet challenging task since humans are more sensitive to structural distortions in urban environments. Additionally generating 3D cities is more complex than 3D natural scenes since buildings as objects of the same class exhibit a wider range of appearances compared to the relatively co... | [] | [] | [] | [] | 5 |
6 | HEAL-SWIN: A Vision Transformer On The Sphere | Oscar Carlsson, Jan E. Gerken, Hampus Linander, Heiner Spieß, Fredrik Ohlsson, Christoffer Petersson, Daniel Persson | null | High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular... | [] | [] | [] | [] | 6 | |
7 | 3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation | http://arxiv.org/abs/2311.09571 | Dale Decatur, Itai Lang, Kfir Aberman, Rana Hanocka | 2,311.09571 | We present 3D Paintbrush a technique for automatically texturing local semantic regions on meshes via text descriptions. Our method is designed to operate directly on meshes producing texture maps which seamlessly integrate into standard graphics pipelines. We opt to simultaneously produce a localization map (to specif... | [] | [] | [] | [] | 7 |
8 | Test-Time Linear Out-of-Distribution Detection | Ke Fan, Tong Liu, Xingyu Qiu, Yikai Wang, Lian Huai, Zeyu Shangguan, Shuang Gou, Fengjian Liu, Yuqian Fu, Yanwei Fu, Xingqun Jiang | null | Out-of-Distribution (OOD) detection aims to address the excessive confidence prediction by neural networks by triggering an alert when the input sample deviates significantly from the training distribution (in-distribution) indicating that the output may not be reliable. Current OOD detection approaches explore all kin... | [] | [] | [] | [] | 8 | |
9 | Guided Slot Attention for Unsupervised Video Object Segmentation | http://arxiv.org/abs/2303.08314 | Minhyeok Lee, Suhwan Cho, Dogyoon Lee, Chaewon Park, Jungho Lee, Sangyoun Lee | 2,303.08314 | Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However the existence of complex backgrounds and multiple foreground objects make this task challenging. To address this issue we propose a guided slot attention network to reinforce spatial structural information and ... | [] | [] | [] | [] | 9 |
10 | Unsupervised Blind Image Deblurring Based on Self-Enhancement | Lufei Chen, Xiangpeng Tian, Shuhua Xiong, Yinjie Lei, Chao Ren | null | Significant progress in image deblurring has been achieved by deep learning methods especially the remarkable performance of supervised models on paired synthetic data. However real-world quality degradation is more complex than synthetic datasets and acquiring paired data in real-world scenarios poses significant chal... | [] | [] | [] | [] | 10 | |
11 | Action Detection via an Image Diffusion Process | http://arxiv.org/abs/2404.01051 | Lin Geng Foo, Tianjiao Li, Hossein Rahmani, Jun Liu | 2,404.01051 | Action detection aims to localize the starting and ending points of action instances in untrimmed videos and predict the classes of those instances. In this paper we make the observation that the outputs of the action detection task can be formulated as images. Thus from a novel perspective we tackle action detection v... | [] | [] | [] | [] | 11 |
12 | Programmable Motion Generation for Open-Set Motion Control Tasks | http://arxiv.org/abs/2405.19283 | Hanchao Liu, Xiaohang Zhan, Shaoli Huang, Tai-Jiang Mu, Ying Shan | 2,405.19283 | Character animation in real-world scenarios necessitates a variety of constraints such as trajectories key-frames interactions etc. Existing methodologies typically treat single or a finite set of these constraint(s) as separate control tasks. These methods are often specialized and the tasks they address are rarely ex... | [] | [] | [] | [] | 12 |
13 | SCE-MAE: Selective Correspondence Enhancement with Masked Autoencoder for Self-Supervised Landmark Estimation | Kejia Yin, Varshanth Rao, Ruowei Jiang, Xudong Liu, Parham Aarabi, David B. Lindell | null | Self-supervised landmark estimation is a challenging task that demands the formation of locally distinct feature representations to identify sparse facial landmarks in the absence of annotated data. To tackle this task existing state-of-the-art (SOTA) methods (1) extract coarse features from backbones that are trained ... | [] | [] | [] | [] | 13 | |
14 | LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion | Pancheng Zhao, Peng Xu, Pengda Qin, Deng-Ping Fan, Zhicheng Zhang, Guoli Jia, Bowen Zhou, Jufeng Yang | null | Camouflaged vision perception is an important vision task with numerous practical applications. Due to the expensive collection and labeling costs this community struggles with a major bottleneck that the species category of its datasets is limited to a small number of object species. However the existing camouflaged g... | [] | [] | [] | [] | 14 | |
15 | TIGER: Time-Varying Denoising Model for 3D Point Cloud Generation with Diffusion Process | Zhiyuan Ren, Minchul Kim, Feng Liu, Xiaoming Liu | null | Recently diffusion models have emerged as a new powerful generative method for 3D point cloud generation tasks. However few works study the effect of the architecture of the diffusion model in the 3D point cloud resorting to the typical UNet model developed for 2D images. Inspired by the wide adoption of Transformers w... | [] | [] | [] | [] | 15 | |
16 | ConTex-Human: Free-View Rendering of Human from a Single Image with Texture-Consistent Synthesis | Xiangjun Gao, Xiaoyu Li, Chaopeng Zhang, Qi Zhang, Yanpei Cao, Ying Shan, Long Quan | null | In this work we propose a method to address the challenge of rendering a 3D human from a single image in a free-view manner. Some existing approaches could achieve this by using generalizable pixel-aligned implicit fields to reconstruct a textured mesh of a human or by employing a 2D diffusion model as guidance with th... | [] | [] | [] | [] | 16 | |
17 | UFineBench: Towards Text-based Person Retrieval with Ultra-fine Granularity | http://arxiv.org/abs/2312.03441 | Jialong Zuo, Hanyu Zhou, Ying Nie, Feng Zhang, Tianyu Guo, Nong Sang, Yunhe Wang, Changxin Gao | 2,312.03441 | Existing text-based person retrieval datasets often have relatively coarse-grained text annotations. This hinders the model to comprehend the fine-grained semantics of query texts in real scenarios. To address this problem we contribute a new benchmark named UFineBench for text-based person retrieval with ultra-fine gr... | [] | [] | [] | [] | 17 |
18 | Efficient Hyperparameter Optimization with Adaptive Fidelity Identification | Jiantong Jiang, Zeyi Wen, Atif Mansoor, Ajmal Mian | null | Hyperparameter Optimization and Neural Architecture Search are powerful in attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing out as a mainstream method. Extending BO into the multi-fidelity setting has been an emerging research topic in this field but faces the challenge of det... | [] | [] | [] | [] | 18 | |
19 | ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering | http://arxiv.org/abs/2312.05941 | Haokai Pang, Heming Zhu, Adam Kortylewski, Christian Theobalt, Marc Habermann | 2,312.05941 | Real-time rendering of photorealistic and controllable human avatars stands as a cornerstone in Computer Vision and Graphics. While recent advances in neural implicit rendering have unlocked unprecedented photorealism for digital avatars real-time performance has mostly been demonstrated for static scenes only. To addr... | [] | [] | [] | [] | 19 |
20 | Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training | http://arxiv.org/abs/2312.07067 | Qian Li, Yuxiao Hu, Yinpeng Dong, Dongxiao Zhang, Yuntian Chen | 2,312.07067 | Adversarial training is often formulated as a min-max problem however concentrating only on the worst adversarial examples causes alternating repetitive confusion of the model i.e. previously defended or correctly classified samples are not defensible or accurately classifiable in subsequent adversarial training. We ch... | [] | [] | [] | [] | 20 |
21 | ArtAdapter: Text-to-Image Style Transfer using Multi-Level Style Encoder and Explicit Adaptation | http://arxiv.org/abs/2312.02109 | Dar-Yen Chen, Hamish Tennent, Ching-Wen Hsu | 2,312.02109 | This work introduces ArtAdapter a transformative text-to-image (T2I) style transfer framework that transcends traditional limitations of color brushstrokes and object shape capturing high-level style elements such as composition and distinctive artistic expression. The integration of a multi-level style encoder with ou... | [] | [] | [] | [] | 21 |
22 | GoodSAM: Bridging Domain and Capacity Gaps via Segment Anything Model for Distortion-aware Panoramic Semantic Segmentation | http://arxiv.org/abs/2403.16370 | Weiming Zhang, Yexin Liu, Xu Zheng, Lin Wang | 2,403.1637 | This paper tackles a novel yet challenging problem: how to transfer knowledge from the emerging Segment Anything Model (SAM) -- which reveals impressive zero-shot instance segmentation capacity -- to learn a compact panoramic semantic segmentation model i.e. student without requiring any labeled data. This poses consid... | [] | [] | [] | [] | 22 |
23 | DYSON: Dynamic Feature Space Self-Organization for Online Task-Free Class Incremental Learning | Yuhang He, Yingjie Chen, Yuhan Jin, Songlin Dong, Xing Wei, Yihong Gong | null | In this paper we focus on a challenging Online Task-Free Class Incremental Learning (OTFCIL) problem. Different from the existing methods that continuously learn the feature space from data streams we propose a novel compute-and-align paradigm for the OTFCIL. It first computes an optimal geometry i.e. the class prototy... | [] | [] | [] | [] | 23 | |
24 | Streaming Dense Video Captioning | http://arxiv.org/abs/2404.01297 | Xingyi Zhou, Anurag Arnab, Shyamal Buch, Shen Yan, Austin Myers, Xuehan Xiong, Arsha Nagrani, Cordelia Schmid | 2,404.01297 | An ideal model for dense video captioning -- predicting captions localized temporally in a video -- should be able to handle long input videos predict rich detailed textual descriptions and be able to produce outputs before processing the entire video. Current state-of-the-art models however process a fixed number of d... | [] | [] | [] | [] | 24 |
25 | Rethinking Inductive Biases for Surface Normal Estimation | http://arxiv.org/abs/2403.00712 | Gwangbin Bae, Andrew J. Davison | 2,403.00712 | Despite the growing demand for accurate surface normal estimation models existing methods use general-purpose dense prediction models adopting the same inductive biases as other tasks. In this paper we discuss the inductive biases needed for surface normal estimation and propose to (1) utilize the per-pixel ray directi... | [] | [] | [] | [] | 25 |
26 | Event-based Structure-from-Orbit | http://arxiv.org/abs/2405.06216 | Ethan Elms, Yasir Latif, Tae Ha Park, Tat-Jun Chin | 2,405.06216 | Event sensors offer high temporal resolution visual sensing which makes them ideal for perceiving fast visual phenomena without suffering from motion blur. Certain applications in robotics and vision-based navigation require 3D perception of an object undergoing circular or spinning motion in front of a static camera s... | [] | [] | [] | [] | 26 |
27 | LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising | http://arxiv.org/abs/2405.19718 | Yuxing Duan | 2,405.19718 | Event camera has significant advantages in capturingdynamic scene information while being prone to noise interferenceparticularly in challenging conditions like lowthreshold and low illumination. However most existing researchfocuses on gentle situations hindering event cameraapplications in realistic complex scenarios... | [] | [] | [] | [] | 27 |
28 | Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity | http://arxiv.org/abs/2405.16585 | Yuhang Chen, Wenke Huang, Mang Ye | 2,405.16585 | Federated learning (FL) has emerged as a new paradigm for privacy-preserving collaborative training. Under domain skew the current FL approaches are biased and face two fairness problems. 1) Parameter Update Conflict: data disparity among clients leads to varying parameter importance and inconsistent update directions.... | [] | [] | [] | [] | 28 |
29 | Activity-Biometrics: Person Identification from Daily Activities | Shehreen Azad, Yogesh Singh Rawat | null | In this work we study a novel problem which focuses on person identification while performing daily activities. Learning biometric features from RGB videos is challenging due to spatio-temporal complexity and presence of appearance biases such as clothing color and background. We propose ABNet a novel framework which l... | [] | [] | [] | [] | 29 | |
30 | Z*: Zero-shot Style Transfer via Attention Reweighting | Yingying Deng, Xiangyu He, Fan Tang, Weiming Dong | null | Despite the remarkable progress in image style transfer formulating style in the context of art is inherently subjective and challenging. In contrast to existing methods this study shows that vanilla diffusion models can directly extract style information and seamlessly integrate the generative prior into the content i... | [] | [] | [] | [] | 30 | |
31 | HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding | http://arxiv.org/abs/2312.03050 | Trong-Thuan Nguyen, Pha Nguyen, Khoa Luu | 2,312.0305 | Visual interactivity understanding within visual scenes presents a significant challenge in computer vision. Existing methods focus on complex interactivities while leveraging a simple relationship model. These methods however struggle with a diversity of appearance situation position interaction and relation in videos... | [] | [] | [] | [] | 31 |
32 | OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising | http://arxiv.org/abs/2404.02227 | Haichao Zhang, Yi Xu, Hongsheng Lu, Takayuki Shimizu, Yun Fu | 2,404.02227 | Trajectory prediction is fundamental in computer vision and autonomous driving particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observational data neglecting the challenges associated with out-of-view objects a... | [] | [] | [] | [] | 32 |
33 | FADES: Fair Disentanglement with Sensitive Relevance | Taeuk Jang, Xiaoqian Wang | null | Learning fair representation in deep learning is essential to mitigate discriminatory outcomes and enhance trustworthiness. However previous research has been commonly established on inappropriate assumptions prone to unrealistic counterfactuals and performance degradation. Although some proposed alternative approaches... | [] | [] | [] | [] | 33 | |
34 | Learning Continuous 3D Words for Text-to-Image Generation | http://arxiv.org/abs/2402.08654 | Ta-Ying Cheng, Matheus Gadelha, Thibault Groueix, Matthew Fisher, Radomir Mech, Andrew Markham, Niki Trigoni | 2,402.08654 | Current controls over diffusion models (e.g. through text or ControlNet) for image generation fall short in recognizing abstract continuous attributes like illumination direction or non-rigid shape change. In this paper we present an approach for allowing users of text-to-image models to have fine-grained control of se... | [] | [] | [] | [] | 34 |
35 | MarkovGen: Structured Prediction for Efficient Text-to-Image Generation | http://arxiv.org/abs/2308.10997 | Sadeep Jayasumana, Daniel Glasner, Srikumar Ramalingam, Andreas Veit, Ayan Chakrabarti, Sanjiv Kumar | 2,308.10997 | Modern text-to-image generation models produce high-quality images that are both photorealistic and faithful to the text prompts. However this quality comes at significant computational cost: nearly all of these models are iterative and require running sampling multiple times with large models. This iterative process i... | [] | [] | [] | [] | 35 |
36 | Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations | http://arxiv.org/abs/2403.13261 | Kewei Wang, Yizheng Wu, Jun Cen, Zhiyu Pan, Xingyi Li, Zhe Wang, Zhiguo Cao, Guosheng Lin | 2,403.13261 | The perception of motion behavior in a dynamic environment holds significant importance for autonomous driving systems wherein class-agnostic motion prediction methods directly predict the motion of the entire point cloud. While most existing methods rely on fully-supervised learning the manual labeling of point cloud ... | [] | [] | [] | [] | 36 |
37 | HashPoint: Accelerated Point Searching and Sampling for Neural Rendering | Jiahao Ma, Miaomiao Liu, David Ahmedt-Aristizabal, Chuong Nguyen | null | In this paper we address the problem of efficient point searching and sampling for volume neural rendering. Within this realm two typical approaches are employed: rasterization and ray tracing. The rasterization-based methods enable real-time rendering at the cost of increased memory and lower fidelity. In contrast the... | [] | [] | [] | [] | 37 | |
38 | MFP: Making Full Use of Probability Maps for Interactive Image Segmentation | http://arxiv.org/abs/2404.18448 | Chaewon Lee, Seon-Ho Lee, Chang-Su Kim | 2,404.18448 | In recent interactive segmentation algorithms previous probability maps are used as network input to help predictions in the current segmentation round. However despite the utilization of previous masks useful information contained in the probability maps is not well propagated to the current predictions. In this paper... | [] | [] | [] | [] | 38 |
39 | CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection | http://arxiv.org/abs/2403.19278 | Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli, Robby T. Tan | 2,403.19278 | Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using the semi-supervised student-teacher framework. However a fundamental issue arises from the class imbalance in the labelled training set whic... | [] | [] | [] | [] | 39 |
40 | StyLitGAN: Image-Based Relighting via Latent Control | Anand Bhattad, James Soole, D.A. Forsyth | null | We describe a novel method StyLitGAN for relighting and resurfacing images in the absence of labeled data. StyLitGAN generates images with realistic lighting effects including cast shadows soft shadows inter-reflections and glossy effects without the need for paired or CGI data. StyLitGAN uses an intrinsic image method... | [] | [] | [] | [] | 40 | |
41 | An Empirical Study of Scaling Law for Scene Text Recognition | Miao Rang, Zhenni Bi, Chuanjian Liu, Yunhe Wang, Kai Han | null | The laws of model size data volume computation and model performance have been extensively studied in the field of Natural Language Processing (NLP). However the scaling laws in Scene Text Recognition (STR) have not yet been investigated. To address this we conducted comprehensive studies that involved examining the co... | [] | [] | [] | [] | 41 | |
42 | Text2Loc: 3D Point Cloud Localization from Natural Language | http://arxiv.org/abs/2311.15977 | Yan Xia, Letian Shi, Zifeng Ding, Joao F. Henriques, Daniel Cremers | 2,311.15977 | We tackle the problem of 3D point cloud localization based on a few natural linguistic descriptions and introduce a novel neural network Text2Loc that fully interprets the semantic relationship between points and text. Text2Loc follows a coarse-to-fine localization pipeline: text-submap global place recognition followe... | [] | [] | [] | [] | 42 |
43 | SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective | http://arxiv.org/abs/2305.14912 | Yu-Bang Zheng, Xi-Le Zhao, Junhua Zeng, Chao Li, Qibin Zhao, Heng-Chao Li, Ting-Zhu Huang | 2,305.14912 | Tensor network (TN) representation is a powerful technique for computer vision and machine learning. TN structure search (TN-SS) aims to search for a customized structure to achieve a compact representation which is a challenging NP-hard problem. Recent "sampling-evaluation"-based methods require sampling an extensive ... | [] | [] | [] | [] | 43 |
44 | Decomposing Disease Descriptions for Enhanced Pathology Detection: A Multi-Aspect Vision-Language Pre-training Framework | http://arxiv.org/abs/2403.07636 | Vu Minh Hieu Phan, Yutong Xie, Yuankai Qi, Lingqiao Liu, Liyang Liu, Bowen Zhang, Zhibin Liao, Qi Wu, Minh-Son To, Johan W. Verjans | 2,403.07636 | Medical vision language pre-training (VLP) has emerged as a frontier of research enabling zero-shot pathological recognition by comparing the query image with the textual descriptions for each disease. Due to the complex semantics of biomedical texts current methods struggle to align medical images with key pathologica... | [] | [] | [] | [] | 44 |
45 | MoMask: Generative Masked Modeling of 3D Human Motions | http://arxiv.org/abs/2312.00063 | Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, Li Cheng | 2,312.00063 | We introduce MoMask a novel masked modeling framework for text-driven 3D human motion generation. In MoMask a hierarchical quantization scheme is employed to represent human motion as multi-layer discrete motion tokens with high-fidelity details. Starting at the base layer with a sequence of motion tokens obtained by v... | [] | [] | [] | [] | 45 |
46 | Inverse Rendering of Glossy Objects via the Neural Plenoptic Function and Radiance Fields | http://arxiv.org/abs/2403.16224 | Haoyuan Wang, Wenbo Hu, Lei Zhu, Rynson W.H. Lau | 2,403.16224 | Inverse rendering aims at recovering both geometry and materials of objects. It provides a more compatible reconstruction for conventional rendering engines compared with the neural radiance fields (NeRFs). On the other hand existing NeRF-based inverse rendering methods cannot handle glossy objects with local light int... | [] | [] | [] | [] | 46 |
47 | Split to Merge: Unifying Separated Modalities for Unsupervised Domain Adaptation | http://arxiv.org/abs/2403.06946 | Xinyao Li, Yuke Li, Zhekai Du, Fengling Li, Ke Lu, Jingjing Li | 2,403.06946 | Large vision-language models (VLMs) like CLIP have demonstrated good zero-shot learning performance in the unsupervised domain adaptation task. Yet most transfer approaches for VLMs focus on either the language or visual branches overlooking the nuanced interplay between both modalities. In this work we introduce a Uni... | [] | [] | [] | [] | 47 |
48 | Fitting Flats to Flats | Gabriel Dogadov, Ugo Finnendahl, Marc Alexa | null | Affine subspaces of Euclidean spaces are also referred to as flats. A standard task in computer vision or more generally in engineering and applied sciences is fitting a flat to a set of points which is commonly solved using the PCA. We generalize this technique to enable fitting a flat to a set of other flats possibly... | [] | [] | [] | [] | 48 | |
49 | Fusing Personal and Environmental Cues for Identification and Segmentation of First-Person Camera Wearers in Third-Person Views | Ziwei Zhao, Yuchen Wang, Chuhua Wang | null | As wearable cameras become more popular an important question emerges: how to identify camera wearers within the perspective of conventional static cameras. The drastic difference between first-person (egocentric) and third-person (exocentric) camera views makes this a challenging task. We present PersonEnvironmentNet ... | [] | [] | [] | [] | 49 | |
50 | Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching | Matteo Bastico, Etienne Decencière, Laurent Corté, Yannick Tillier, David Ryckelynck | null | Point cloud matching a crucial technique in computer vision medical and robotics fields is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios emphasizing local differences is crucial for accurately identifying a correct match thereby enhancing the overa... | [] | [] | [] | [] | 50 | |
51 | Overcoming Generic Knowledge Loss with Selective Parameter Update | http://arxiv.org/abs/2308.12462 | Wenxuan Zhang, Paul Janson, Rahaf Aljundi, Mohamed Elhoseiny | 2,308.12462 | Foundation models encompass an extensive knowledge base and offer remarkable transferability. However this knowledge becomes outdated or insufficient over time. The challenge lies in continuously updating foundation models to accommodate novel information while retaining their original capabilities. Leveraging the fact... | [] | [] | [] | [] | 51 |
52 | Desigen: A Pipeline for Controllable Design Template Generation | http://arxiv.org/abs/2403.09093 | Haohan Weng, Danqing Huang, Yu Qiao, Zheng Hu, Chin-Yew Lin, Tong Zhang, C. L. Philip Chen | 2,403.09093 | Templates serve as a good starting point to implement a design (e.g. banner slide) but it takes great effort from designers to manually create. In this paper we present Desigen an automatic template creation pipeline which generates background images as well as harmonious layout elements over the background. Different ... | [] | [] | [] | [] | 52 |
53 | Diff-BGM: A Diffusion Model for Video Background Music Generation | Sizhe Li, Yiming Qin, Minghang Zheng, Xin Jin, Yang Liu | null | When editing a video a piece of attractive background music is indispensable. However video background music generation tasks face several challenges for example the lack of suitable training datasets and the difficulties in flexibly controlling the music generation process and sequentially aligning the video and music... | [] | [] | [] | [] | 53 | |
54 | Looking Similar Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning | http://arxiv.org/abs/2304.05600 | Nikhil Singh, Chih-Wei Wu, Iroro Orife, Mahdi Kalayeh | 2,304.056 | Audiovisual representation learning typically relies on the correspondence between sight and sound. However there are often multiple audio tracks that can correspond with a visual scene. Consider for example different conversations on the same crowded street. The effect of such counterfactual pairs on audiovisual repre... | [] | [] | [] | [] | 54 |
55 | Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision Transformers | http://arxiv.org/abs/2403.10030 | Sanghyeok Lee, Joonmyung Choi, Hyunwoo J. Kim | 2,403.1003 | Vision Transformer (ViT) has emerged as a prominent backbone for computer vision. For more efficient ViTs recent works lessen the quadratic cost of the self-attention layer by pruning or fusing the redundant tokens. However these works faced the speed-accuracy trade-off caused by the loss of information. Here we argue ... | [] | [] | [] | [] | 55 |
56 | Towards HDR and HFR Video from Rolling-Mixed-Bit Spikings | Yakun Chang, Yeliduosi Xiaokaiti, Yujia Liu, Bin Fan, Zhaojun Huang, Tiejun Huang, Boxin Shi | null | The spiking cameras offer the benefits of high dynamic range (HDR) high temporal resolution and low data redundancy. However reconstructing HDR videos in high-speed conditions using single-bit spikings presents challenges due to the limited bit depth. Increasing the bit depth of the spikings is advantageous for boostin... | [] | [] | [] | [] | 56 | |
57 | Scaling Up Video Summarization Pretraining with Large Language Models | http://arxiv.org/abs/2404.03398 | Dawit Mureja Argaw, Seunghyun Yoon, Fabian Caba Heilbron, Hanieh Deilamsalehy, Trung Bui, Zhaowen Wang, Franck Dernoncourt, Joon Son Chung | 2,404.03398 | Long-form video content constitutes a significant portion of internet traffic making automated video summarization an essential research problem. However existing video summarization datasets are notably limited in their size constraining the effectiveness of state-of-the-art methods for generalization. Our work aims t... | [] | [] | [] | [] | 57 |
58 | Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real World | Huiyuan Fu, Fei Peng, Xianwei Li, Yejun Li, Xin Wang, Huadong Ma | null | Most current arbitrary-scale image super-resolution (SR) methods has commonly relied on simulated data generated by simple synthetic degradation models (e.g. bicubic downsampling) at continuous various scales thereby falling short in capturing the complex degradation of real-world images. This limitation hinders the vi... | [] | [] | [] | [] | 58 | |
59 | Sharingan: A Transformer Architecture for Multi-Person Gaze Following | Samy Tafasca, Anshul Gupta, Jean-Marc Odobez | null | Gaze is a powerful form of non-verbal communication that humans develop from an early age. As such modeling this behavior is an important task that can benefit a broad set of application domains ranging from robotics to sociology. In particular the gaze following task in computer vision is defined as the prediction of ... | [] | [] | [] | [] | 59 | |
60 | ViewFusion: Towards Multi-View Consistency via Interpolated Denoising | http://arxiv.org/abs/2402.18842 | Xianghui Yang, Yan Zuo, Sameera Ramasinghe, Loris Bazzani, Gil Avraham, Anton van den Hengel | 2,402.18842 | Novel-view synthesis through diffusion models has demonstrated remarkable potential for generating diverse and high-quality images. Yet the independent process of image generation in these prevailing methods leads to challenges in maintaining multiple-view consistency. To address this we introduce ViewFusion a novel tr... | [] | [] | [] | [] | 60 |
61 | SketchINR: A First Look into Sketches as Implicit Neural Representations | http://arxiv.org/abs/2403.09344 | Hmrishav Bandyopadhyay, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Aneeshan Sain, Tao Xiang, Timothy Hospedales, Yi-Zhe Song | 2,403.09344 | We propose SketchINR to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimension that implicitly encodes the underlying shape as a function of time and strokes. The learned function predicts the xy point coordinates i... | [] | [] | [] | [] | 61 |
62 | Open-Vocabulary Segmentation with Semantic-Assisted Calibration | http://arxiv.org/abs/2312.04089 | Yong Liu, Sule Bai, Guanbin Li, Yitong Wang, Yansong Tang | 2,312.04089 | This paper studies open-vocabulary segmentation (OVS) through calibrating in-vocabulary and domain-biased embedding space with generalized contextual prior of CLIP. As the core of open-vocabulary understanding alignment of visual content with the semantics of unbounded text has become the bottleneck of this field. To a... | [] | [] | [] | [] | 62 |
63 | MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images | Junwen Huang, Hao Yu, Kuan-Ting Yu, Nassir Navab, Slobodan Ilic, Benjamin Busam | null | Recent learning methods for object pose estimation require resource-intensive training for each individual object instance or category hampering their scalability in real applications when confronted with previously unseen objects. In this paper we propose MatchU a Fuse-Describe-Match strategy for 6D pose estimation fr... | [] | [] | [] | [] | 63 | |
64 | Towards a Perceptual Evaluation Framework for Lighting Estimation | http://arxiv.org/abs/2312.04334 | Justine Giroux, Mohammad Reza Karimi Dastjerdi, Yannick Hold-Geoffroy, Javier Vazquez-Corral, Jean-François Lalonde | 2,312.04334 | Progress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene i... | [] | [] | [] | [] | 64 |
65 | Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality Assessment | http://arxiv.org/abs/2405.04167 | Aobo Li, Jinjian Wu, Yongxu Liu, Leida Li | 2,405.04167 | The annotation of blind image quality assessment (BIQA) is labor-intensive and time-consuming especially for authentic images. Training on synthetic data is expected to be beneficial but synthetically trained models often suffer from poor generalization in real domains due to domain gaps. In this work we make a key obs... | [] | [] | [] | [] | 65 |
66 | Coherent Temporal Synthesis for Incremental Action Segmentation | http://arxiv.org/abs/2403.06102 | Guodong Ding, Hans Golong, Angela Yao | 2,403.06102 | Data replay is a successful incremental learning technique for images. It prevents catastrophic forgetting by keeping a reservoir of previous data original or synthesized to ensure the model retains past knowledge while adapting to novel concepts. However its application in the video domain is rudimentary as it simply ... | [] | [] | [] | [] | 66 |
67 | HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting | http://arxiv.org/abs/2312.03461 | Yuheng Jiang, Zhehao Shen, Penghao Wang, Zhuo Su, Yu Hong, Yingliang Zhang, Jingyi Yu, Lan Xu | 2,312.03461 | We have recently seen tremendous progress in photo-real human modeling and rendering. Yet efficiently rendering realistic human performance and integrating it into the rasterization pipeline remains challenging. In this paper we present HiFi4G an explicit and compact Gaussian-based approach for high-fidelity human perf... | [] | [] | [] | [] | 67 |
68 | G-FARS: Gradient-Field-based Auto-Regressive Sampling for 3D Part Grouping | Junfeng Cheng, Tania Stathaki | null | This paper proposes a novel task named "3D part grouping". Suppose there is a mixed set containing scattered parts from various shapes. This task requires algorithms to find out every possible combination among all the parts. To address this challenge we propose the so called Gradient Field-based Auto-Regressive Sampli... | [] | [] | [] | [] | 68 | |
69 | Towards High-fidelity Artistic Image Vectorization via Texture-Encapsulated Shape Parameterization | Ye Chen, Bingbing Ni, Jinfan Liu, Xiaoyang Huang, Xuanhong Chen | null | We develop a novel vectorized image representation scheme accommodating both shape/geometry and texture in a decoupled way particularly tailored for reconstruction and editing tasks of artistic/design images such as Emojis and Cliparts. In the heart of this representation is a set of sparsely and unevenly located 2D co... | [] | [] | [] | [] | 69 | |
70 | On Exact Inversion of DPM-Solvers | http://arxiv.org/abs/2311.18387 | Seongmin Hong, Kyeonghyun Lee, Suh Yoon Jeon, Hyewon Bae, Se Young Chun | 2,311.18387 | Diffusion probabilistic models (DPMs) are a key component in modern generative models. DPM-solvers have achieved reduced latency and enhanced quality significantly but have posed challenges to find the exact inverse (i.e. finding the initial noise from the given image). Here we investigate the exact inversions for DPM-... | [] | [] | [] | [] | 70 |
71 | EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything | http://arxiv.org/abs/2312.00863 | Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu, Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest Iandola, Raghuraman Krishnamoorthi, Vikas Chandra | 2,312.00863 | Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high versatility is a super large Transformer model trained on the extensive high-quality SA-1B dataset. While beneficial the huge computation c... | [] | [] | [] | [] | 71 |
72 | ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles | http://arxiv.org/abs/2405.14062 | Jiawei Zhang, Chejian Xu, Bo Li | 2,405.14062 | We present ChatScene a Large Language Model (LLM)-based agent that leverages the capabilities of LLMs to generate safety-critical scenarios for autonomous vehicles. Given unstructured language instructions the agent first generates textually described traffic scenarios using LLMs. These scenario descriptions are subseq... | [] | [] | [] | [] | 72 |
73 | CAMEL: CAusal Motion Enhancement Tailored for Lifting Text-driven Video Editing | Guiwei Zhang, Tianyu Zhang, Guanglin Niu, Zichang Tan, Yalong Bai, Qing Yang | null | Text-driven video editing poses significant challenges in exhibiting flicker-free visual continuity while preserving the inherent motion patterns of original videos. Existing methods operate under a paradigm where motion and appearance are intricately intertwined. This coupling leads to the network either over-fitting ... | [] | [] | [] | [] | 73 | |
74 | Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Multi-Scale Aggregation and Anthropic Prior Knowledge | Bo Zou, Shaofeng Wang, Hao Liu, Gaoyue Sun, Yajie Wang, FeiFei Zuo, Chengbin Quan, Youjian Zhao | null | Teeth localization segmentation and labeling in 2D images have great potential in modern dentistry to enhance dental diagnostics treatment planning and population-based studies on oral health. However general instance segmentation frameworks are incompetent due to 1) the subtle differences between some teeth' shapes (e... | [] | [] | [] | [] | 74 | |
75 | FocSAM: Delving Deeply into Focused Objects in Segmenting Anything | http://arxiv.org/abs/2405.18706 | You Huang, Zongyu Lan, Liujuan Cao, Xianming Lin, Shengchuan Zhang, Guannan Jiang, Rongrong Ji | 2,405.18706 | The Segment Anything Model (SAM) marks a notable milestone in segmentation models highlighted by its robust zero-shot capabilities and ability to handle diverse prompts. SAM follows a pipeline that separates interactive segmentation into image preprocessing through a large encoder and interactive inference via a lightw... | [] | [] | [] | [] | 75 |
76 | DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement Learning | Haoran Xu, Peixi Peng, Guang Tan, Yuan Li, Xinhai Xu, Yonghong Tian | null | We explore visual reinforcement learning (RL) using two complementary visual modalities: frame-based RGB camera and event-based Dynamic Vision Sensor (DVS). Existing multi-modality visual RL methods often encounter challenges in effectively extracting task-relevant information from multiple modalities while suppressing... | [] | [] | [] | [] | 76 | |
77 | DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models | http://arxiv.org/abs/2405.14881 | Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood, Karthik Nandakumar | 2,405.14881 | Recently a number of image-mixing-based augmentation techniques have been introduced to improve the generalization of deep neural networks. In these techniques two or more randomly selected natural images are mixed together to generate an augmented image. Such methods may not only omit important portions of the input i... | [] | [] | [] | [] | 77 |
78 | PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models | http://arxiv.org/abs/2402.08714 | Fei Deng, Qifei Wang, Wei Wei, Tingbo Hou, Matthias Grundmann | 2,402.08714 | Reward finetuning has emerged as a promising approach to aligning foundation models with downstream objectives. Remarkable success has been achieved in the language domain by using reinforcement learning (RL) to maximize rewards that reflect human preference. However in the vision domain existing RL-based reward finetu... | [] | [] | [] | [] | 78 |
79 | FREE: Faster and Better Data-Free Meta-Learning | http://arxiv.org/abs/2405.00984 | Yongxian Wei, Zixuan Hu, Zhenyi Wang, Li Shen, Chun Yuan, Dacheng Tao | 2,405.00984 | Data-Free Meta-Learning (DFML) aims to extract knowledge from a collection of pre-trained models without requiring the original data presenting practical benefits in contexts constrained by data privacy concerns. Current DFML methods primarily focus on the data recovery from these pre-trained models. However they suffe... | [] | [] | [] | [] | 79 |
80 | Bayesian Diffusion Models for 3D Shape Reconstruction | http://arxiv.org/abs/2403.06973 | Haiyang Xu, Yu Lei, Zeyuan Chen, Xiang Zhang, Yue Zhao, Yilin Wang, Zhuowen Tu | 2,403.06973 | We present Bayesian Diffusion Models (BDM) a prediction algorithm that performs effective Bayesian inference by tightly coupling the top-down (prior) information with the bottom-up (data-driven) procedure via joint diffusion processes. We demonstrate the application of BDM on the 3D shape reconstruction task. Compared ... | [] | [] | [] | [] | 80 |
81 | Task-Customized Mixture of Adapters for General Image Fusion | http://arxiv.org/abs/2403.12494 | Pengfei Zhu, Yang Sun, Bing Cao, Qinghua Hu | 2,403.12494 | General image fusion aims at integrating important information from multi-source images. However due to the significant cross-task gap the respective fusion mechanism varies considerably in practice resulting in limited performance across subtasks. To handle this problem we propose a novel task-customized mixture of ad... | [] | [] | [] | [] | 81 |
82 | Bi-SSC: Geometric-Semantic Bidirectional Fusion for Camera-based 3D Semantic Scene Completion | Yujie Xue, Ruihui Li, Fan Wu, Zhuo Tang, Kenli Li, Mingxing Duan | null | Camera-based Semantic Scene Completion (SSC) is to infer the full geometry of objects and scenes from only 2D images. The task is particularly challenging for those invisible areas due to the inherent occlusions and lighting ambiguity. Existing works ignore the information missing or ambiguous in those shaded and occlu... | [] | [] | [] | [] | 82 | |
83 | CrossKD: Cross-Head Knowledge Distillation for Object Detection | http://arxiv.org/abs/2306.11369 | Jiabao Wang, Yuming Chen, Zhaohui Zheng, Xiang Li, Ming-Ming Cheng, Qibin Hou | 2,306.11369 | Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper we present a general and effective prediction mimicking distillation scheme cal... | [] | [] | [] | [] | 83 |
84 | Bi-level Learning of Task-Specific Decoders for Joint Registration and One-Shot Medical Image Segmentation | Xin Fan, Xiaolin Wang, Jiaxin Gao, Jia Wang, Zhongxuan Luo, Risheng Liu | null | One-shot medical image segmentation (MIS) aims to cope with the expensive time-consuming and inherent human bias annotations. One prevalent method to address one-shot MIS is joint registration and segmentation (JRS) with a shared encoder which mainly explores the voxel-wise correspondence between the labeled data and u... | [] | [] | [] | [] | 84 | |
85 | Parameter Efficient Self-Supervised Geospatial Domain Adaptation | Linus Scheibenreif, Michael Mommert, Damian Borth | null | As large-scale foundation models become publicly available for different domains efficiently adapting them to individual downstream applications and additional data modalities has turned into a central challenge. For example foundation models for geospatial and satellite remote sensing applications are commonly trained... | [] | [] | [] | [] | 85 | |
86 | Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay | http://arxiv.org/abs/2404.01828 | Yuhang Zhou, Zhongyun Hua | 2,404.01828 | Deep neural networks have demonstrated susceptibility to adversarial attacks. Adversarial defense techniques often focus on one-shot setting to maintain robustness against attack. However new attacks can emerge in sequences in real-world deployment scenarios. As a result it is crucial for a defense model to constantly ... | [] | [] | [] | [] | 86 |
87 | EscherNet: A Generative Model for Scalable View Synthesis | http://arxiv.org/abs/2402.03908 | Xin Kong, Shikun Liu, Xiaoyang Lyu, Marwan Taher, Xiaojuan Qi, Andrew J. Davison | 2,402.03908 | We introduce EscherNet a multi-view conditioned diffusion model for view synthesis. EscherNet learns implicit and generative 3D representations coupled with a specialised camera positional encoding allowing precise and continuous relative control of the camera transformation between an arbitrary number of reference and... | [] | [] | [] | [] | 87 |
88 | MeaCap: Memory-Augmented Zero-shot Image Captioning | http://arxiv.org/abs/2403.03715 | Zequn Zeng, Yan Xie, Hao Zhang, Chiyu Chen, Bo Chen, Zhengjue Wang | 2,403.03715 | Zero-shot image captioning (IC) without well-paired image-text data can be categorized into two main types: training-free and text-only-training methods. While both types integrate pre-trained vision-language models such as CLIP for image-text similarity evaluation and a pre-trained language model (LM) for caption gene... | [] | [] | [] | [] | 88 |
89 | Artist-Friendly Relightable and Animatable Neural Heads | http://arxiv.org/abs/2312.03420 | Yingyan Xu, Prashanth Chandran, Sebastian Weiss, Markus Gross, Gaspard Zoss, Derek Bradley | 2,312.0342 | An increasingly common approach for creating photo-realistic digital avatars is through the use of volumetric neural fields. The original neural radiance field (NeRF) allowed for impressive novel view synthesis of static heads when trained on a set of multi-view images and follow up methods showed that these neural rep... | [] | [] | [] | [] | 89 |
90 | Elite360D: Towards Efficient 360 Depth Estimation via Semantic- and Distance-Aware Bi-Projection Fusion | Hao Ai, Lin Wang | null | 360 depth estimation has recently received great attention for 3D reconstruction owing to its omnidirectional field of view (FoV). Recent approaches are predominantly focused on cross-projection fusion with geometry-based re-projection: they fuse 360 images with equirectangular projection (ERP) and another projection t... | [] | [] | [] | [] | 90 | |
91 | From Feature to Gaze: A Generalizable Replacement of Linear Layer for Gaze Estimation | Yiwei Bao, Feng Lu | null | Deep-learning-based gaze estimation approaches often suffer from notable performance degradation in unseen target domains. One of the primary reasons is that the Fully Connected layer is highly prone to overfitting when mapping the high-dimensional image feature to 3D gaze. In this paper we propose Analytical Gaze Gene... | [] | [] | [] | [] | 91 | |
92 | Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation | http://arxiv.org/abs/2404.11998 | Qiyuan Dai, Sibei Yang | 2,404.11998 | Referring image segmentation (RIS) aims to precisely segment referents in images through corresponding natural language expressions yet relying on cost-intensive mask annotations. Weakly supervised RIS thus learns from image-text pairs to pixel-level semantics which is challenging for segmenting fine-grained masks. A n... | [] | [] | [] | [] | 92 |
93 | EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object Recognition | http://arxiv.org/abs/2403.14082 | Xu Zheng, Lin Wang | 2,403.14082 | In this paper we make the first attempt at achieving the cross-modal (i.e. image-to-events) adaptation for event-based object recognition without accessing any labeled source image data owning to privacy and commercial issues. Tackling this novel problem is non-trivial due to the novelty of event cameras and the distin... | [] | [] | [] | [] | 93 |
94 | CycleINR: Cycle Implicit Neural Representation for Arbitrary-Scale Volumetric Super-Resolution of Medical Data | http://arxiv.org/abs/2404.04878 | Wei Fang, Yuxing Tang, Heng Guo, Mingze Yuan, Tony C. W. Mok, Ke Yan, Jiawen Yao, Xin Chen, Zaiyi Liu, Le Lu, Ling Zhang, Minfeng Xu | 2,404.04878 | In the realm of medical 3D data such as CT and MRI images prevalent anisotropic resolution is characterized by high intra-slice but diminished inter-slice resolution. The lowered resolution between adjacent slices poses challenges hindering optimal viewing experiences and impeding the development of robust downstream a... | [] | [] | [] | [] | 94 |
95 | Boosting Image Restoration via Priors from Pre-trained Models | http://arxiv.org/abs/2403.06793 | Xiaogang Xu, Shu Kong, Tao Hu, Zhe Liu, Hujun Bao | 2,403.06793 | Pre-trained models with large-scale training data such as CLIP and Stable Diffusion have demonstrated remarkable performance in various high-level computer vision tasks such as image understanding and generation from language descriptions. Yet their potential for low-level tasks such as image restoration remains relati... | [] | [] | [] | [] | 95 |
96 | VRetouchEr: Learning Cross-frame Feature Interdependence with Imperfection Flow for Face Retouching in Videos | Wen Xue, Le Jiang, Lianxin Xie, Si Wu, Yong Xu, Hau San Wong | null | Face Video Retouching is a complex task that often requires labor-intensive manual editing. Conventional image retouching methods perform less satisfactorily in terms of generalization performance and stability when applied to videos without exploiting the correlation among frames. To address this issue we propose a Vi... | [] | [] | [] | [] | 96 | |
97 | Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity Training | Di Ming, Peng Ren, Yunlong Wang, Xin Feng | null | Deep neural networks (DNNs) are vulnerable to highly transferable adversarial attacks. Especially many studies have shown that sparse attacks pose a significant threat to DNNs on account of their exceptional imperceptibility. Current sparse attack methods mostly limit only the magnitude and number of perturbations whil... | [] | [] | [] | [] | 97 | |
98 | Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models | Xinpeng Ding, Jianhua Han, Hang Xu, Xiaodan Liang, Wei Zhang, Xiaomeng Li | null | The rise of multimodal large language models (MLLMs) has spurred interest in language-based driving tasks. However existing research typically focuses on limited tasks and often omits key multi-view and temporal information which is crucial for robust autonomous driving. To bridge these gaps we introduce NuInstruct a n... | [] | [] | [] | [] | 98 | |
99 | Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder | http://arxiv.org/abs/2403.10255 | Jinseok Kim, Tae-Kyun Kim | 2,403.10255 | Super-resolution (SR) and image generation are important tasks in computer vision and are widely adopted in real-world applications. Most existing methods however generate images only at fixed-scale magnification and suffer from over-smoothing and artifacts. Additionally they do not offer enough diversity of output ima... | [] | [] | [] | [] | 99 |
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