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Zero-Reference Low-Light Enhancement via Physical Quadruple Priors

Zero-Reference Low-Light Enhancement via Physical Quadruple ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Wenjing Yang, Huan Fu, Jianlong Liu, Jiaying Peking Univ Beijing Peoples R China 01 AI Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350353006

Understanding illumination and reducing the need for supervision pose a significant challenge in low-light enhancement. Current approaches are highly sensitive to data usage during training and illumination-specific hyper-parameters, limiting their ability to handle unseen scenarios. In this paper, we propose a new zero-reference low-light enhancement framework trainable solely with normal light images. To accomplish this, we devise an illumination-invariant prior inspired by the theory of physical light transfer. This prior serves as the bridge between normal and low-light images. Then, we develop a prior-to-image framework trained without low-light data. During testing, this frame-work is able to restore our illumination-invariant prior back to images, automatically achieving low-light enhancement. Within this framework, we leverage a pretrained generative diffusion model for model ability, introduce a bypass decoder to handle detail distortion, as well as offer a lightweight version for practicality. Extensive experiments demonstrate our framework's superiority in various scenarios as well as good interpretability, robustness, and efficiency. Code is available on our project homepage.

关键词： diffusion image processing low-level vision Low-light enhancement zero-reference

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Dual-View Visual Contextualization for Web Navigation

Dual-View Visual Contextualization for Web Navigation

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kil, Jihyung Song, Chan Hee Zheng, Boyuan Deng, Xiang Su, Yu Chao, We-Lun Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9798350353006

Automatic web navigation aims to build a web agent that can follow language instructions to execute complex and diverse tasks on real-world websites. Existing work primarily takes HTML documents as input, which define the contents and action spaces (i.e., actionable elements and operations) of webpages. Nevertheless, HTML documents may not provide a clear task-related context for each element, making it hard to select the right (sequence of) actions. In this paper, we propose to contextualize HTML elements through their "dual views" in webpage screenshots: each HTML element has its corresponding bounding box and visual content in the screenshot. We build upon the insight-web developers tend to arrange task-related elements nearby on webpages to enhance user experiences-and propose to contextualize each element with its neighbor elements, using both textual and visual features. The resulting representations of HTML elements are more informative for the agent to take action. We validate our method on the recently released Mind2Web dataset, which features diverse navigation domains and tasks on real-world websites. Our method consistently outperforms the baseline in all the scenarios, including cross-task, cross-website, and cross-domain ones.

关键词： AI Agents Multimodal Agents Multimodal Learning vision-Language Web Agents Web Navigation

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What Affects Learned Equivariance in Deep Image recognition Models?

What Affects Learned Equivariance in Deep Image Recognition ...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Bruintjes, Robert-Jan Motyka, Tomasz Van Gemert, Jan Delft University of Technology Computer Vision Lab Netherlands Synerise

ISBN: (纸本)9798350302493

Equivariance w.r.t. geometric transformations in neural networks improves data efficiency, parameter efficiency and robustness to out-of-domain perspective shifts. When equivariance is not designed into a neural network, the network can still learn equivariant functions from the data. We quantify this learned equivariance, by proposing an improved measure for equivariance. We find evidence for a correlation between learned translation equivariance and validation accuracy on ImageNet. We therefore investigate what can increase the learned equivariance in neural networks, and find that data augmentation, reduced model capacity and inductive bias in the form of convolutions induce higher learned equivariance in neural networks. © 2023 ieee.

关键词： Efficiency

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Ethical Impact Identification of a Dementia Behaviour Monitoring System 18

Ethical Impact Identification of a Dementia Behaviour Monito...

引用

18th International conference on Automatic Face and Gesture recognition (FG)

作者： Ballester, Irene Kampel, Martin TU Wien Comp Vision Lab Favoritenstr 9-193-1 A-1040 Vienna Austria

ISBN: (纸本)9798350394948;9798350394955

The identification of ethical impacts is the first phase of Ethical Impact Assessments, which are used to evaluate the ethical implications of new technologies. Using a structured methodology, this paper explores the identification of ethical impacts of a video-based tool for monitoring dementia-related behaviours. By reflecting on autonomy, dignity, non-maleficence, beneficence, justice and privacy, our work contributes to a broader understanding of the ethical landscape surrounding the use of Artificial Intelligence (AI) and computer vision in healthcare. Our contribution goes beyond the specific aspects of this system as we address consent, privacy and autonomy, factors that are relevant to any technology for people with dementia. We call for the integration of ethical considerations into the design and implementation of AI technologies, ensuring that innovations not only advance clinical care, but also respect fundamental ethical principles.

关键词： Neurodegenerative diseases

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MovieChat: From Dense Token to Sparse Memory for Long Video Understanding

MovieChat: From Dense Token to Sparse Memory for Long Video ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Song, Enxin Chai, Wenhao Wang, Guanhong Zhang, Yucheng Zhou, Haoyang Wu, Feiyang Chi, Haozhe Guo, Xun Ye, Tian Zhang, Yanting Lu, Yan Hwang, Jenq-Neng Wang, Gaoang Zhejiang Univ Hangzhou Peoples R China Univ Washington Seattle WA 98195 USA Microsoft Res Asia Florence Italy Hong Kong Univ Sci & Technol GZ Hong Kong Peoples R China Donghua Univ Shanghai Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

Recently, integrating video foundation models and large language models to build a video understanding system can overcome the limitations of specific pre-defined vision tasks. Yet, existing systems can only handle videos with very few frames. For long videos, the computation complexity, memory cost, and long-term temporal connection impose additional challenges. Taking advantage of the Atkinson-Shiffrin memory model, with tokens in Transformers being employed as the carriers of memory in combination with our specially designed memory mechanism, we propose the MovieChat to overcome these challenges. MovieChat achieves state-of-the-art performance in long video understanding, along with the released MovieChat-1K bench-mark with 1K long video and 14K manual annotations for validation of the effectiveness of our method. The code, models and data can be found in https://***/MovieChat.

关键词： computer simulation languages

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Multimodal Shape Completion via Implicit Maximum Likelihood Estimation

Multimodal Shape Completion via Implicit Maximum Likelihood ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Arora, Himanshu Mishra, Saurabh Peng, Shichong Li, Ke Mahdavi-Amiri, Ali Simon Fraser Univ Burnaby BC Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Shape completion is the problem of completing partial input shapes such as partial scans. This problem finds important applications in computer vision and robotics due to issues such as occlusion or sparsity in real-world data. However, most of the existing research related to shape completion has been focused on completing shapes by learning a one-to-one mapping which limits the diversity and creativity of the produced results. We propose a novel multimodal shape completion technique that is effectively able to learn a one-to-many mapping and generates diverse complete shapes. Our approach is based on the conditional Implicit Maximum Likelihood Estimation (IMLE) technique wherein we condition our inputs on partial 3D point clouds. We extensively evaluate our approach by comparing it to various baselines both quantitatively and qualitatively. We show that our method is superior to alternatives in terms of completeness and diversity of shapes.

关键词： Point cloud compression Maximum likelihood estimation computer vision Three-dimensional displays Shape conferences pattern recognition

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Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine vision-Language Model

Jack of All Tasks, Master of Many: Designing General-purpose...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Pramanick, Shraman Han, Guangxing Hou, Rui Nag, Sayan Lim, Ser-Nam Ballas, Nicolas Wang, Qifan Chellappa, Rama Almahairi, Amjad Johns Hopkins Univ Baltimore MD 21218 USA Meta New York NY 10003 USA Univ Toronto Toronto ON Canada Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9798350353006

The ability of large language models (LLMs) to process visual inputs has given rise to general-purpose vision systems, unifying various vision-language (VL) tasks by instruction tuning. However, due to the enormous diversity in input-output formats in the vision domain, existing general-purpose models fail to successfully integrate segmentation and multi-image inputs with coarse-level tasks into a single framework. In this work, we introduce VistaLLM, a powerful visual system that addresses coarse- and fine-grained VL tasks over single and multiple input images using a unified framework. VistaLLM utilizes an instruction-guided image tokenizer that filters global embeddings using task descriptions to extract compressed and refined features from numerous images. Moreover, VistaLLM employs a gradient-aware adaptive sampling technique to represent binary segmentation masks as sequences, significantly improving over previously used uniform sampling. To bolster the desired capability of VistaLLM, we curate CoinIt, a comprehensive coarse-to-fine instruction tuning dataset with 6.8M samples. We also address the lack of multi-image grounding datasets by introducing a novel task, AttCoSeg (Attribute-level Co-Segmentation), which boosts the model's reasoning and grounding capability over multiple input images. Extensive experiments on a wide range of V- and VL tasks demonstrate the effectiveness of VistaLLM by achieving consistent state-of-the-art performance over strong base-lines across many downstream tasks. Our project page can be found at https://***/VistaLLM/.

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Forecasting of 3D Whole-body Human Poses with Grasping Objects

Forecasting of 3D Whole-body Human Poses with Grasping Objec...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Yan, Haitao Cui, Qiongjie Xie, Jiexin Guo, Shijie Fudan Univ Acad Engn & Technol Shanghai Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In the context of computer vision and human-robot interaction, forecasting 3D human poses is crucial for understanding human behavior and enhancing the predictive capabilities of intelligent systems. While existing methods have made significant progress, they often focus on predicting major body joints, overlooking fine-grained gestures and their interaction with objects. Human hand movements, particularly during object interactions, play a pivotal role and provide more precise expressions of human poses. This work fills this gap and introduces a novel paradigm: forecasting 3D whole-body human poses with a focus on grasping objects. This task involves predicting activities across all joints in the body and hands, encompassing the complexities of internal heterogeneity and external interactivity. To tackle these challenges, we also propose a novel approach: C3HOST, cross-context cross-modal consolidation for 3D whole-body pose forecasting, effectively handles the complexities of internal heterogeneity and external interactivity. C3HOST involves distinct steps, including the heterogeneous content encoding and alignment, and cross-modal feature learning and interaction. These enable us to predict activities across all body and hand joints, ensuring high-precision whole-body human pose prediction, even during object grasping. Extensive experiments on two benchmarks demonstrate that our model significantly enhances the accuracy of whole-body human motion prediction. The project page is available at https://***/view/c3host.

关键词： 3D computer vision cross-modal learning human motion analysis human motion prediction

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Sharingan: A Transformer Architecture for Multi-Person Gaze Following

Sharingan: A Transformer Architecture for Multi-Person Gaze ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tafasca, Samy Gupta, Anshul Odobez, Jean-Marc Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798350353013;9798350353006

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 the 2D pixel coordinates where a person in the image is looking. Previous attempts in this area have primarily centered on CNN-based architectures, but they have been constrained by the need to process one person at a time, which proves to be highly inefficient. In this paper, we introduce a novel and effective multi-person transformer-based architecture for gaze prediction. While there exist prior works using transformers for multi-person gaze prediction [38, 39], they use a fixed set of learnable embeddings to decode both the person and its gaze target, which requires a matching step afterward to link the predictions with the annotations. Thus, it is difficult to quantitatively evaluate these methods reliably with the available benchmarks, or integrate them into a larger human behavior understanding system. Instead, we are the first to propose a multi-person transformer-based architecture that maintains the original task formulation and ensures control over the people fed as input. Our main contribution lies in encoding the person-specific information into a single controlled token to be processed alongside image tokens and using its output for prediction based on a novel multiscale decoding mechanism. Our new architecture achieves state-of-the-art results on the GazeFollow, VideoAttentionTarget, and ChildPlay datasets and outper-forms comparable multi-person architectures with a notable margin. Our code, checkpoints, and data extractions will be made publicly available soon.

关键词： computer vision deep learning gaze following

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StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On

StableVITON: Learning Semantic Correspondence with Latent Di...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kim, Jeongho Gu, Gyojung Park, Minho Park, Sunghyun Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the clothing image. In this work, we aim to expand the applicability of the pre-trained diffusion model so that it can be utilized independently for the virtual try-on task. The main challenge is to preserve the clothing details while effectively utilizing the robust generative capability of the pre-trained model. In order to tackle these issues, we propose StableVITON, learning the semantic correspondence between the clothing and the human body within the latent space of the pre-trained diffusion model in an end-to-end manner. Our proposed zero cross-attention blocks not only preserve the clothing details by learning the semantic correspondence but also generate high-fidelity images by utilizing the inherent knowledge of the pre-trained model in the warping process. Through our proposed novel attention total variation loss and applying augmentation, we achieve the sharp attention map, resulting in a more precise representation of clothing details. StableVITON out-performs the baselines in qualitative and quantitative eval-uation, showing promising quality in arbitrary person images. Our code is available at https://***/ rlawjdghek/StableVITON.

关键词： computer vision Diffusion Model Virtual Try-On

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