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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11890 条记录，以下是221-230 订阅

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3D Human Pose Perception from Egocentric Stereo Videos

3D Human Pose Perception from Egocentric Stereo Videos

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

作者： Akada, Hiroyasu Wang, Jian Golyanik, Vladislav Theobalt, Christian Max Planck Inst Informat SIC Saarbrucken Germany

ISBN: (纸本)9798350353013;9798350353006

While head-mounted devices are becoming more compact, they provide egocentric views with significant self-occlusions of the device user. Hence, existing methods often fail to accurately estimate complex 3D poses from egocentric views. In this work, we propose a new transformer-based framework to improve egocentric stereo 3D human pose estimation, which leverages the scene information and temporal context of egocentric stereo videos. Specifically, we utilize 1) depth features from our 3D scene reconstruction module with uniformly sampled windows of egocentric stereo frames, and 2) human joint queries enhanced by temporal features of the video inputs. Our method is able to accurately estimate human poses even in challenging scenarios, such as crouching and sitting. Furthermore, we introduce two new benchmark datasets, i.e., UnrealEgo2 and UnrealEgo-RW (RealWorld). The proposed datasets offer a much larger number of egocentric stereo views with a wider variety of human motions than the existing datasets, allowing comprehensive evaluation of existing and upcoming methods. Our extensive experiments show that the proposed approach significantly outperforms previous methods. UnrealEgo2, UnrealEgo-RW, and trained models are available on our project page(1) and Benchmark Challenge(2).

关键词： Egocentric 3D human pose estimation First-person view Stereo vision

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Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models

Perturbing Attention Gives You More Bang for the Buck: Subtl...

引用

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

作者： Xu, Jingyao Lu, Yuetong Li, Yandong Lu, Siyang Wang, Dongdong Wei, Xiang Beijing Jiaotong Univ Beijing Peoples R China Google Res Mountain View CA USA Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9798350353006

Diffusion models ( DMs) embark a new era of generative modeling and offer more opportunities for efficient generating high- quality and realistic data samples. However, their widespread use has also brought forth new challenges in model security, which motivates the creation of more effective adversarial attackers on DMs to understand its vulnerability. We propose CAAT, a simple but generic and efficient approach that does not require costly training to effectively fool latent diffusion models (LDMs). The approach is based on the observation that cross-attention layers exhibits higher sensitivity to gradient change, allowing for leveraging subtle perturbations on published images to significantly corrupt the generated images. We show that a subtle perturbation on an image can significantly impact the cross-attention layers, thus changing the mapping between text and image during the fine-tuning of customized diffusion models. Extensive experiments demonstrate that CAAT is compatible with diverse diffusion models and out- performs baseline attack methods in a more effective (more noise) and efficient (twice as fast as Anti-DreamBooth and Mist) manner.

关键词： Adversarial Attack computer vision

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Fair-VPT: Fair Visual Prompt Tuning for Image Classification

Fair-VPT: Fair Visual Prompt Tuning for Image Classification

引用

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

作者： Park, Sungho Byun, Hy Eran Yonsei Univ Seoul South Korea

ISBN: (纸本)9798350353006

Despite the remarkable success of vision Transformers (ViT) across diverse fields in computer vision, they have a clear drawback of expensive adaption cost for downstream tasks due to the increased scale. To address this, Visual Prompt Tuning (VPT) incorporates learnable parameters in the input space of ViT. While freezing the ViT backbone and tuning only the prompts, it exhibits superior performances to full fine-tuning. However, despite the outstanding advantage, we point out that VPT may lead to serious unfairness in downstream classification. Initially, we investigate the causes of unfairness in VPT, identifying the biasedly pre-trained ViT as a principal factor. Motivated by this observation, we propose a Fair Visual Prompt Tuning (Fair-VPT) which removes biased information in the pre-trained ViT while adapting it to downstream classification tasks. To this end, we categorize prompts into "cleaner prompts" and "target prompts". Based on this, we encode the class token in two different ways by either masking or not masking the target prompts in the self-attention process. These encoded tokens are trained with distinct objective functions, resulting in the inclusion of different information in the target and cleaner prompts. Moreover, we introduce a disentanglement loss based on contrastive learning to further decorrelate them. In experiments across diverse benchmarks, the proposed method demonstrates the most superior performance in terms of balanced classification accuracy and fairness.

关键词： FAI Fair-VPT Fairness Large vision Model vision Transformer Visual Prompt Tuning ViT VPT

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Revisiting Counterfactual Problems in Referring Expression Comprehension

Revisiting Counterfactual Problems in Referring Expression C...

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

作者： Yu, Zhihan Li, Ruifan Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350353006

Traditional referring expression comprehension (REC) aims to locate the target referent in an image guided by a text query. Several previous methods have studied on the Counterfactual problem in REC (C-REC) where the objects for a given query cannot be found in the image. However, these methods focus on the overall image-text or specific attribute mismatch only. In this paper, we address the C-REC problem from a deep perspective of fine-grained attributes. To this aim, we first propose a fine-grained counterfactual sample generation method to construct C-REC datasets. Specifically, we leverage pre-trained language model such as BERT to modify the attribute words in the queries, obtaining the corresponding counterfactual samples. Furthermore, we propose a C-REC framework. We first adopt three encoders to extract image, text and attribute features. Then, our dual-branch attentive fusion module fuses these crossmodal features with two branches by an attention mechanism. At last, two prediction heads generate a bounding box and a counterfactual label, respectively. In addition, we incorporate contrastive learning with the generated counterfactual samples as negatives to enhance the counterfactual perception. Extensive experiments show that our framework achieves promising performance on both public REC datasets RefCOCO/+/g and our constructed C-REC datasets C-RefCOCO/+/g. The code and data are available at https://***/Glacier0012/CREC.

关键词： Counterfactual Generation Referring Expression Comprehension vision and Language

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Action-slot: Visual Action-centric Representations for Multi-label Atomic Activity recognition in Traffic Scenes

Action-slot: Visual Action-centric Representations for Multi...

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

作者： Kung, Chi-Hsi Lu, Shu-Wei Tsai, Yi-Hsuan Chen, Yi-Ting Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan Google Mountain View CA USA

ISBN: (纸本)9798350353006

In this paper, we study multi-label atomic activity recognition. Despite the notable progress in action recognition, it is still challenging to recognize atomic activities due to a deficiency in holistic understanding of both multiple road users' motions and their contextual information. In this paper, we introduce Action-slot, a slot attention-based approach that learns visual action-centric representations, capturing both motion and contextual information. Our key idea is to design action slots that are capable of paying attention to regions where atomic activities occur, without the need for explicit perception guidance. To further enhance slot attention, we introduce a background slot that competes with action slots, aiding the training process in avoiding unnecessary focus on background regions devoid of activities. Yet, the imbalanced class distribution in the existing dataset hampers the assessment of rare activities. To address the limitation, we collect a synthetic dataset called TACO, which is four times larger than OATS and features a balanced distribution of atomic activities. To validate the effectiveness of our method, we conduct comprehensive experiments and ablation studies against various action recognition baselines. We also show that the performance of multi-label atomic activity recognition on real-world datasets can be improved by pretraining representations on TACO. Our source code, dataset, and visualization videos are available at https://***/Action-slot/

关键词： Action recognition Autonomous Driving Slot Attention Traffic Activity Video Understanding

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Sharingan: A Transformer Architecture for Multi-Person Gaze Following

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

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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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MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning

MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-T...

引用

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

作者： Agiza, Ahmed Neseem, Marina Reda, Sherief Brown Univ Providence RI 02912 USA

ISBN: (纸本)9798350353006

Adapting models pre-trained on large-scale datasets to a variety of downstream tasks is a common strategy in deep learning. Consequently, parameter-efficient fine-tuning methods have emerged as a promising way to adapt pre-trained models to different tasks while training only a minimal number of parameters. While most of these methods are designed for single-task adaptation, parameter-efficient training in Multi-Task Learning (MTL) architectures is still unexplored. In this paper, we introduce MTLoRA, a novel framework for parameter-efficient training of MTL models. MTLoRA employs Task-Agnostic and Task-Specific Low-Rank Adaptation modules, which effectively disentangle the parameter space in MTL fine-tuning, thereby enabling the model to adeptly handle both task specialization and interaction within MTL contexts. We applied MTLoRA to hierarchical-transformer-based MTL architectures, adapting them to multiple downstream dense prediction tasks. Our extensive experiments on the PASCAL dataset show that MTLoRA achieves higher accuracy on downstream tasks compared to fully fine-tuning the MTL model while reducing the number of trainable parameters by 3.6x. Furthermore, MTLoRA establishes a Pareto-optimal trade-off between the number of trainable parameters and the accuracy of the downstream tasks, outperforming current state-of-the-art parameter-efficient training methods in both accuracy and efficiency. Our code is publicly available.(1)

关键词： efficient multi-task learning hierarchical transformers LoRA low-rank adapatation multi-task learning parameter-efficient training PEFT vision transformers

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Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation

Boosting Object Detection with Zero-Shot Day-Night Domain Ad...

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

作者： Du, Zhipeng Shi, Miaojing Deng, Jiankang Kings Coll London Dept Informat London England Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China Imperial Coll London Dept Comp London England Huawei London Res London England

ISBN: (纸本)9798350353006

Detecting objects in low-light scenarios presents a persistent challenge, as detectors trained on well-lit data exhibit significant performance degradation on low-light data due to low visibility. Previous methods mitigate this issue by exploring image enhancement or object detection techniques with real low-light image datasets. However, the progress is impeded by the inherent difficulties about collecting and annotating low-light images. To address this challenge, we propose to boost low-light object detection with zero-shot day-night domain adaptation, which aims to generalize a detector from well-lit scenarios to low-light ones without requiring real low-light data. Revisiting Retinex theory in the low-level vision, we first design a reflectance representation learning module to learn Retinex-based illumination invariance in images with a carefully designed illumination invariance reinforcement strategy. Next, an interchange-redecomposition-coherence procedure is introduced to improve over the vanilla Retinex image decomposition process by performing two sequential image decompositions and introducing a redecomposition cohering loss. Extensive experiments on ExDark, DARK FACE, and CODaN datasets show strong low-light generalizability of our method. Our code is available at https://***/ZPDu/DAI-Net.

关键词： Low-light vision Object Detction Zero-shot Domain Adaptation

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GenZI: Zero-Shot 3D Human-Scene Interaction Generation

GenZI: Zero-Shot 3D Human-Scene Interaction Generation

引用

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

作者： Li, Lei Dai, Angela Tech Univ Munich Munich Germany

ISBN: (纸本)9798350353006

Can we synthesize 3D humans interacting with scenes without learning from any 3D human-scene interaction data? We propose GenZI(1), the first zero-shot approach to generating 3D human-scene interactions. Key to GenZI is our distillation of interaction priors from large vision-language models (VLMs), which have learned a rich semantic space of 2D human-scene compositions. Given a natural language description and a coarse point location of the desired interaction in a 3D scene, we first leverage VLMs to imagine plausible 2D human interactions inpainted into multiple rendered views of the scene. We then formulate a robust iterative optimization to synthesize the pose and shape of a 3D human model in the scene, guided by consistency with the 2D interaction hypotheses. In contrast to existing learning-based approaches, GenZI circumvents the conventional need for captured 3D interaction data, and allows for flexible control of the 3D interaction synthesis with easy-to-use text prompts. Extensive experiments show that our zero-shot approach has high flexibility and generality, making it applicable to diverse scene types, including both indoor and outdoor environments.

关键词： Human-Scene Interaction vision-Language Models Zero-Shot

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ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting

ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangli...

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

作者： Jiang, Yankai Huang, Zhongzhen Zhang, Rongzhao Zhang, Xiaofan Zhang, Shaoting Shanghai AI Lab Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China SenseTime Res Shanghai Peoples R China

ISBN: (纸本)9798350353006

The long-tailed distribution problem in medical image analysis reflects a high prevalence of common conditions and a low prevalence of rare ones, which poses a significant challenge in developing a unified model capable of identifying rare or novel tumor categories not encountered during training. In this paper, we propose a new Zero-shot Pan-Tumor segmentation framework (ZePT) based on query-disentangling and self-prompting to segment unseen tumor categories beyond the training set. ZePT disentangles the object queries into two subsets and trains them in two stages. Initially, it learns a set of fundamental queries for organ segmentation through an object-aware feature grouping strategy, which gathers organ-level visual features. Subsequently, it refines the other set of advanced queries that focus on the auto-generated visual prompts for unseen tumor segmentation. Moreover, we introduce query-knowledge alignment at the feature level to enhance each query's discriminative representation and generalizability. Extensive experiments on various tumor segmentation tasks demonstrate the performance superiority of ZePT, which surpasses the previous counterparts and evidences the promising ability for zero-shot tumor segmentation in real-world settings.

关键词： Medical Image Segmentation vision-Language Model

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