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共 21179 条记录，以下是301-310 订阅

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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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Deep Factorized Metric Learning

Deep Factorized Metric Learning

引用

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

作者： Wang, Chengkun Zheng, Wenzhao Li, Junlong Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Learning a generalizable and comprehensive similarity metric to depict the semantic discrepancies between images is the foundation of many computer vision tasks. While existing methods approach this goal by learning an ensemble of embeddings with diverse objectives, the backbone network still receives a mix of all the training signals. Differently, we propose a deep factorized metric learning (DFML) method to factorize the training signal and employ different samples to train various components of the backbone network. We factorize the network to different sub-blocks and devise a learnable router to adaptively allocate the training samples to each sub-block with the objective to capture the most information. The metric model trained by DFML capture different characteristics with different sub-blocks and constitutes a generalizable metric when using all the subblocks. The proposed DFML achieves state-of-the-art performance on all three benchmarks for deep metric learning including CUB-200-2011, Cars196, and Stanford Online Products. We also generalize DFML to the image classification task on ImageNet-1K and observe consistent improvement in accuracy/computation trade-off. Specifically, we improve the performance of ViT-B on ImageNet (+0.2% accuracy) with less computation load (-24% FLOPs). (1)

关键词： detection recognition: Categorization retrieval

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Random Entangled Tokens for Adversarially Robust vision Transformer

Random Entangled Tokens for Adversarially Robust Vision Tran...

引用

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

作者： Gong, Huihui Dong, Mingjing Mao, Siqi Camtepe, Seyit Nepal, Surya Xu, Chang Univ Sydney Sydney NSW Australia CSIRO Data61 Eveleigh Australia City Univ Hong Kong Hong Kong Peoples R China Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9798350353006

vision Transformers (ViTs) have emerged as a compelling alternative to Convolutional Neural Networks ( CNNs) in the realm of computer vision, showcasing tremendous potential. However, recent research has unveiled a susceptibility of ViTs to adversarial attacks, akin to their CNN counterparts. Adversarial training and randomization are two representative effective defenses for CNNs. Some researchers have attempted to apply adversarial training to ViTs and achieved comparable robustness to CNNs, while it is not easy to directly apply randomization to ViTs because of the architecture difference between CNNs and ViTs. In this paper, we delve into the structural intricacies of ViTs and propose a novel defense mechanism termed Random entangled image Transformer (ReiT), which seamlessly integrates adversarial training and randomization to bolster the adversarial robustness of ViTs. Recognizing the challenge posed by the structural disparities between ViTs and CNNs, we introduce a novel module, input-independent random entangled self-attention (II-ReSA). This module optimizes random entangled tokens that lead to "dissimilar" self-attention outputs by leveraging model parameters and the sampled random tokens, thereby synthesizing the self-attention module outputs and random entangled tokens to diminish adversarial similarity. ReiT incorporates two distinct random entangled tokens and employs dual randomization, offering an effective countermeasure against adversarial examples while ensuring comprehensive deduction guarantees. Through extensive experiments conducted on various ViT variants and benchmarks, we substantiate the superiority of our proposed method in enhancing the adversarial robustness of vision Transformers.

关键词： Adversarial Robustness Randomized Defence Self-Attention Mechanism vision Transformers

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Learning from One Continuous Video Stream

Learning from One Continuous Video Stream

引用

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

作者： Carreira, Joao King, Michael Patraucean, Viorica Gokal, Dilara Ionescu, Cristian Yang, Yi Zoran, Daniel Heyward, Joseph Doersch, Carl Aytar, Yusuf Damen, Di Liu Zisserman, Andrew Google DeepMind London 1 England Univ Bristol Bristol Avon England Univ Oxford Oxford England

ISBN: (纸本)9798350353006

We introduce a framework for online learning from a single continuous video stream - the way people and animals learn, without mini-batches, data augmentation or shuffling. This poses great challenges given the high correlation between consecutive video frames and there is very little prior work on it. Our framework allows us to do a first deep dive into the topic and includes a collection of streams and tasks composed from two existing video datasets, plus methodology for performance evaluation that considers both adaptation and generalization. We employ pixel-to-pixel modelling as a practical and flexible way to switch between pre-training and single-stream evaluation as well as between arbitrary tasks, without ever requiring changes to models and always using the same pixel loss. Equipped with this framework we obtained large single-stream learning gains from pre-training with a novel family of future prediction tasks, found that momentum hurts, and that the pace of weight updates matters. The combination of these insights leads to matching the performance of IID learning with batch size 1, when using the same architecture and without costly replay buffers. An overview of the paper is available online at https://***/view/one-stream-video.

关键词： computer vision continual learning developmental learning

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Connecting NeRFs, Images, and Text

Connecting NeRFs, Images, and Text

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

作者： Ballerini, Francesco Ramirez, Pierluigi Zama Mirabella, Roberto Salti, Samuele Di Stefano, Luigi Univ Bologna Bologna Italy

ISBN: (纸本)9798350365474

Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage. Concurrently, significant progress has been made in multimodal representation learning for text and image data. This paper explores a novel research direction that aims to connect the NeRF modality with other modalities, similar to established methodologies for images and text. To this end, we propose a simple framework that exploits pre-trained models for NeRF representations alongside multimodal models for text and image processing. Our framework learns a bidirectional mapping between NeRF embeddings and those obtained from corresponding images and text. This mapping unlocks several novel and useful applications, including NeRF zero-shot classification and NeRF retrieval from images or text.

关键词： 3D computer vision Neural Fields

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Weak-to-Strong 3D Object Detection with X-Ray Distillation

Weak-to-Strong 3D Object Detection with X-Ray Distillation

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

作者： Gambashidze, Alexander Dadukin, Aleksandr Golyadkin, Maxim Razzhivina, Maria Makarov, Ilya Artificial Intelligence Res Inst Barcelona Spain HSE Univ Moscow Russia ISP RAS Moscow Russia

ISBN: (纸本)9798350353006

This paper addresses the critical challenges of sparsity and occlusion in LiDAR-based 3D object detection. Current methods often rely on supplementary modules or specific architectural designs, potentially limiting their applicability to new and evolving architectures. To our knowledge, we are the first to propose a versatile technique that seamlessly integrates into any existing framework for 3D Object Detection, marking the first instance of Weak-to-Strong generalization in 3D computer vision. We introduce a novel framework, X-Ray Distillation with Object-Complete Frames, suitable for both supervised and semi-supervised settings, that leverages the temporal aspect of point cloud sequences. This method extracts crucial information from both previous and subsequent LiDAR frames, creating Object-Complete frames that represent objects from multiple viewpoints, thus addressing occlusion and sparsity. Given the limitation of not being able to generate Object-Complete frames during online inference, we utilize Knowledge Distillation within a Teacher-Student framework. This technique encourages the strong Student model to emulate the behavior of the weaker Teacher, which processes simple and informative Object-Complete frames, effectively offering a comprehensive view of objects as if seen through X-ray vision. Our proposed methods surpass state-of-the-art in semi-supervised learning by 1-1.5 mAP and enhance the performance of five established supervised models by 1-2 mAP on standard autonomous driving datasets, even with default hyperparameters. Code for Object-Complete frames is available here: https://***/sakharok13/X-Ray-TeacherPatching-Tools.

关键词： 3D detection autonomous driving computer vision

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DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection

DiffAM: Diffusion-based Adversarial Makeup Transfer for Faci...

引用

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

作者： Sun, Yuhao Yu, Lingyun Xie, Hongtao Li, Jiaming Zhang, Yongdong Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350353006

With the rapid development of face recognition (FR) systems, the privacy of face images on social media is facing severe challenges due to the abuse of unauthorized FR systems. Some studies utilize adversarial attack techniques to defend against malicious FR systems by generating adversarial examples. However, the generated adversarial examples, i.e., the protected face images, tend to suffer from subpar visual quality and low transferability. In this paper, we propose a novel face protection approach, dubbed DiffAM, which leverages the powerful generative ability of diffusion models to generate high-quality protected face images with adversarial makeup transferred from reference images. To be specific, we first introduce a makeup removal module to generate non-makeup images utilizing a fine-tuned diffusion model with guidance of textual prompts in CLIP space. As the inverse process of makeup transfer, makeup removal can make it easier to establish the deterministic relationship between makeup domain and non-makeup domain regardless of elaborate text prompts. Then, with this relationship, a CLIP-based makeup loss along with an ensemble attack strategy is introduced to jointly guide the direction of adversarial makeup domain, achieving the generation of protected face images with natural-looking makeup and high black-box transferability. Extensive experiments demonstrate that DiffAM achieves higher visual quality and attack success rates with a gain of 12.98% under black-box setting compared with the state of the arts. The code will be available at https://***/HansSunY/DiffAM.

关键词： adversarial attack diffusion models face recognition

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Contextual Augmented Global Contrast for Multimodal Intent recognition

Contextual Augmented Global Contrast for Multimodal Intent R...

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

作者： Sun, Kaili Xie, Zhiwen Ye, Mang Zhang, Huyin Wuhan Univ Sch Comp Sci Wuhan Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9798350353006

Multimodal intent recognition (MIR) aims to perceive the human intent polarity via language, visual, and acoustic modalities. The inherent intent ambiguity makes it challenging to recognize in multimodal scenarios. Existing MIR methods tend to model the individual video independently, ignoring global contextual information across videos. This learning manner inevitably introduces perception biases, exacerbated by the inconsistencies of the multimodal representation, amplifying the intent uncertainty. This challenge motivates us to explore effective global context modeling. Thus, we propose a context-augmented global contrast (CAGC) method to capture rich global context features by mining both intra-and cross-video context interactions for MIR. Concretely, we design a context-augmented transformer module to extract global context dependencies across videos. To further alleviate error accumulation and interference, we develop a cross-video bank that retrieves effective video sources by considering both intentional tendency and video similarity. Furthermore, we introduce a global context-guided contrastive learning scheme, designed to mitigate inconsistencies arising from global context and individual modalities in different feature spaces. This scheme incorporates global cues as the supervision to capture robust the multimodal intent representation. Experiments demonstrate CAGC obtains superior performance than state-of-the-art MIR methods. We also generalize our approach to a closely related task, multimodal sentiment analysis, achieving the comparable performance.

关键词： Contrastive learning Global contextual information Multimodal intent recognition

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EventDance: Unsupervised Source-free Cross-modal Adaptation for Event-based Object recognition

EventDance: Unsupervised Source-free Cross-modal Adaptation ...

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

作者： Zheng, Xu Wang, Lin HKUST GZ AI Thrust Guangzhou Peoples R China HKUST Dept CSE Guangzhou Peoples R China

ISBN: (纸本)9798350353006

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 distinct modality gap between images and events. In particular, as only the source model is available, a hurdle is how to extract the knowledge from the source model by only using the unlabeled target event data while achieving knowledge transfer. To this end, we propose a novel framework, dubbed EventDance for this unsupervised source-free cross-modal adaptation problem. Importantly, inspired by event-to-video reconstruction methods, we propose a reconstruction-based modality bridging (RMB) module, which reconstructs intensity frames from events in a self-supervised manner. This makes it possible to build up the surrogate images to extract the knowledge (i.e., labels) from the source model. We then propose a multi-representation knowledge adaptation ( MKA) module that transfers the knowledge to target models learning events with multiple representation types for fully exploring the spatiotemporal information of events. The two modules connecting the source and target models are mutually updated so as to achieve the best performance. Experiments on three benchmark datasets with two adaption settings show that EventDance is on par with prior methods utilizing the source data.

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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 ...

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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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