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Cross-view and Cross-pose Completion for 3D Human Understanding

Cross-view and Cross-pose Completion for 3D Human Understand...

引用

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

作者： Armando, Matthieu Galaaoui, Salma Baradel, Fabien Lucas, Thomas Leroy, Vincent Bregier, Romain Weinzaepfel, Philippe Rogez, Gregory NAVER LABS Europe Meylan France

ISBN: (纸本)9798350353013;9798350353006

Human perception and understanding is a major domain of computer vision which, like many other vision subdomains recently, stands to gain from the use of large models pre-trained on large datasets. We hypothesize that the most common pre-training strategy of relying on general purpose, object-centric image datasets such as ImageNet, is limited by an important domain shift. On the other hand, collecting domain-specific ground truth such as 2D or 3D labels does not scale well. Therefore, we propose a pre-training approach based on self-supervised learning that works on human-centric data using only images. Our method uses pairs of images of humans: the first is partially masked and the model is trained to reconstruct the masked parts given the visible ones and a second image. It relies on both stereoscopic (cross-view) pairs, and temporal (cross-pose) pairs taken from videos, in order to learn priors about 3D as well as human motion. We pre-train a model for body-centric tasks and one for hand-centric tasks. With a generic transformer architecture, these models outperform existing self-supervised pre-training methods on a wide set of human-centric downstream tasks, and obtain state-of-the-art performance for instance when fine-tuning for model-based and model-free human mesh recovery.

关键词： hand-centric human-centric pretraining representation learning vision tranformer

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Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained vision Transfomers

Not All Prompts Are Secure: A Switchable Backdoor Attack Aga...

引用

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

作者： Yang, Sheng Bai, Jiawang Gao, Kuofeng Yang, Yong Li, Yiming Xia, Shu-Tao Tsinghua Univ Beijing Peoples R China Tencent Secur Platform Dept Shenzhen Peoples R China Zhejiang Univ Hangzhou Peoples R China Peng Cheng Lab Res Ctr Artificial Intelligence Shenzhen Peoples R China

ISBN: (纸本)9798350353006

Given the power of vision transformers, a new learning paradigm, pretraining and then prompting, makes it more efficient and effective to address downstream visual recognition tasks. In this paper, we identify a novel security threat towards such a paradigm from the perspective of backdoor attacks. Specifically, an extra prompt token, called the switch token in this work, can turn the backdoor mode on, i.e., converting a benign model into a backdoored one. Once under the backdoor mode, a specific trigger can force the model to predict a target class. It poses a severe risk to the users of cloud API, since the malicious behavior can not be activated and detected under the benign mode, thus making the attack very stealthy. To attack a pretrained model, our proposed attack, named SWARM, learns a trigger and prompt tokens including a switch token. They are optimized with the clean loss which encourages the model always behaves normally even the trigger presents, and the backdoor loss that ensures the backdoor can be activated by the trigger when the switch is on. Besides, we utilize the crossmode feature distillation to reduce the effect of the switch token on clean samples. The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack, i.e., achieving 95%+ attack success rate, and also being hard to be detected and removed. Our code is available at https://***/20000yshust/SWARM.

关键词： Backdoor computer vision Parameter-Efficient Fine Tuning Switchable vision Transformers Visual Prompting

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CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor

CLIP as RNN: Segment Countless Visual Concepts without Train...

引用

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

作者： Sun, Shuyang Li, Runjia Torr, Philip Gu, Xiuye Li, Siyang Univ Oxford Oxford England Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350353006

Existing open-vocabulary image segmentation methods require a fine-tuning step on mask labels and/or image-text datasets. Mask labels are labor-intensive, which limits the number of categories in segmentation datasets. Consequently, the vocabulary capacity of pre-trained VLMs is severely reduced after fine-tuning. However, without fine-tuning, VLMs trained under weak image-text supervision tend to make suboptimal mask predictions. To alleviate these issues, we introduce a novel recurrent framework that progressively filters out irrelevant texts and enhances mask quality without training efforts. The recurrent unit is a two-stage segmenter built upon a frozen VLM. Thus, our model retains the VLM's broad vocabulary space and equips it with segmentation ability. Experiments show that our method outperforms not only the training-free counterparts, but also those fine-tuned with millions of data samples, and sets the new state-of-the-art records for both zero-shot semantic and referring segmentation. Concretely, we improve the current record by 28.8, 16.0, and 6.9 mIoU on Pascal VOC, COCO Object, and Pascal Context.

关键词： image segmentation open-vocabulary referring segmentation training-free methods vision-language models

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HomoFormer: Homogenized Transformer for Image Shadow Removal

HomoFormer: Homogenized Transformer for Image Shadow Removal

引用

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

作者： Xiao, Jie Fu, Xueyang Zhu, Yurui Li, Dong Huang, Jie Zhu, Kai Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350353006

The spatial non-uniformity and diverse patterns of shadow degradation conflict with the weight sharing manner of dominant models, which may lead to an unsatisfactory compromise. To tackle with this issue, we present a novel strategy from the view of shadow transformation in this paper: directly homogenizing the spatial distribution of shadow degradation. Our key design is the random shuffle operation and its corresponding inverse operation. Specifically, random shuffle operation stochastically rear-ranges the pixels across spatial space and the inverse operation recovers the original order. After randomly shuffling, the shadow diffuses in the whole image and the degradation appears in a homogenized way, which can be effectively processed by the local self-attention layer. Moreover, we further devise a new feed forward network with position modeling to exploit image structural information. Based on these elements, we construct the final local window based transformer named HomoFormer for image shadow removal. Our HomoFormer can enjoy the linear complexity of local transformers while bypassing challenges of non-uniformity and diversity of shadow. Extensive experiments are conducted to verify the superiority of our HomoFormer across public datasets. Code is available at https://***/jiexiaou/HomoFormer.

关键词： Image Restoration Image Shadow Removal vision Transformer

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Exploring Regional Clues in CLIP for Zero-Shot Semantic Segmentation

Exploring Regional Clues in CLIP for Zero-Shot Semantic Segm...

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

作者： Zhang, Yi Guo, Meng-Hao Wang, Miao Hu, Shi-Min Beihang Univ State Key Lab Virtual Real Technol & Syst SCSE Beijing Peoples R China Tsinghua Univ Dept Comp Sci & Technol BNRist Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

CLIP has demonstrated marked progress in visual recognition due to its powerful pre-training on large-scale image-text pairs. However, it still remains a critical challenge: how to transfer image-level knowledge into pixel-level understanding tasks such as semantic segmentation. In this paper, to solve the mentioned challenge, we analyze the gap between the capability of the CLIP model and the requirement of the zero-shot semantic segmentation task. Based on our analysis and observations, we propose a novel method for zero-shot semantic segmentation, dubbed CLIP-RC (CLIP with Regional Clues), bringing two main insights. On the one hand, a region-level bridge is necessary to provide fine-grained semantics. On the other hand, overfitting should be mitigated during the training stage. Benefiting from the above discoveries, CLIP-RC achieves state-of-the-art performance on various zero-shot semantic segmentation benchmarks, including PASCAL VOC, PASCAL Context, and COCO-Stuff 164K. Code will be available at https://***/Jittor/JSeg.

关键词： computer vision semantic segmentation zero-shot semantic segmentation

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PELA: Learning Parameter-Efficient Models with Low-Rank Approximation

PELA: Learning Parameter-Efficient Models with Low-Rank Appr...

引用

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

作者： Guo, Yangyang Wang, Guangzhi Kankanhalli, Mohan Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798350353006

Applying a pre-trained large model to downstream tasks is prohibitive under resource-constrained conditions. Re-cent dominant approaches for addressing efficiency issues involve adding a few learnable parameters to the fixed backbone model. This strategy, however, leads to more challenges in loading large models for downstream fine-tuning with limited resources. In this paper, we propose a novel method for increasing the parameter efficiency of pre-trained models by introducing an intermediate pre-training stage. To this end, we first employ low-rank approximation to compress the original large model and then devise a feature distillation module and a weight perturbation regularization module. These modules are specifically designed to enhance the low-rank model. In particular, we update only the low-rank model while freezing the backbone parameters during pre-training. This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks. The proposed method achieves both efficiencies in terms of required parameters and computation time while maintaining comparable results with minimal modifications to the backbone architecture. Specifically, when applied to three vision-only and one vision-language Transformer models, our approach often demonstrates a merely similar to 0.6 point decrease in performance while reducing the original parameter size by 1/3 to 2/3. We release our code at link.

关键词： Knowledge Distillation Low-rank Approximation vision-Language

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Unlocking the Potential of Pre-trained vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors

Unlocking the Potential of Pre-trained Vision Transformers f...

引用

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

作者： Zhou, Ziqin Xu, Hai-Ming Shu, Yangyang Liu, Lingqiao Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350353013;9798350353006

The recent advent of pre-trained vision transformers has unveiled a promising property: their inherent capability to group semantically related visual concepts. In this paper, we explore to harnesses this emergent feature to tackle few-shot semantic segmentation, a task focused on classifying pixels in a test image with a few example data. A critical hurdle in this endeavor is preventing overfitting to the limited classes seen during training the few-shot segmentation model. As our main discovery, we find that the concept of "relationship descriptors", initially conceived for enhancing the CLIP model for zero-shot semantic segmentation, offers a potential solution. We adapt and refine this concept to craft a relationship descriptor construction tailored for few-shot semantic segmentation, extending its application across multiple layers to enhance performance. Building upon this adaptation, we proposed a few-shot semantic segmentation framework that is not only easy to implement and train but also effectively scales with the number of support examples and categories. Through rigorous experimentation across various datasets, including PASCAL-5(i) and COCO-20(i), we demonstrate a clear advantage of our method in diverse few-shot semantic segmentation scenarios, and a range of pre-trained vision transformer models. The findings clearly show that our method significantly outperforms current state-of-the-art techniques, highlighting the effectiveness of harnessing the emerging capabilities of vision transformers for few-shot semantic segmentation. We release the code at https://***/ZiqinZhou66/***.

关键词： Semantic Segmentation

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Attentive Sensing for Long-Range Face recognition

Attentive Sensing for Long-Range Face Recognition

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23rd ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Perroni Filho, Helio Trajcevski, Aleksander Bhargava, Kartikeya Javed, Nizwa Elder, James H. York Univ Ctr AI & Soc Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada

ISBN: (纸本)9798350320565

To be effective, a social robot must reliably detect and recognize people in all visual directions and in both near and far fields. A major challenge is the resolution/field-of-view tradeoff;here we propose and evaluate a novel attentive sensing solution. Panoramic low-resolution preattentive sensing is provided by an array of wide-angle cameras, while attentive sensing is achieved with a high-resolution, narrow field-of-view camera and a mirror-based gaze deflection system. Quantitative evaluation on a novel dataset shows that this attentive sensing strategy can yield good panoramic face recognition performance in the wild out to distances of similar to 35m.

关键词： Visualization computer vision Face recognition conferences Social robots Robot vision systems Cameras

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Grounded Question-Answering in Long Egocentric Videos

Grounded Question-Answering in Long Egocentric Videos

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

作者： Di, Shangzhe Xie, Weidi Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

Existing approaches to video understanding, mainly designed for short videos from a third-person perspective, are limited in their applicability in certain fields, such as robotics. In this paper, we delve into open-ended question-answering (QA) in long, egocentric videos, which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges, including where did I put lettuce? Choices: (A) pantry (B) refrigerator (C) cupboard (D) draw 20-50s Answer: in the fridge / (B) refrigerator the complexity of temporally grounding queries within extensive video content, the high resource demands for precise data annotation, and the inherent difficulty of evaluating open-ended answers due to their ambiguous nature. Our proposed approach tackles these challenges by (i) integrating query grounding and answering within a unified model to reduce error propagation;(ii) employing large language models for efficient and scalable data synthesis;and (iii) introducing a close-ended QA task for evaluation, to manage answer ambiguity. Extensive experiments demonstrate the effectiveness of our method, which also achieves state-of-the-art performance on the QAEgo4D and Ego4D-NLQ benchmarks. Code, data, and models are open-sourced (1).

关键词： egocentric vision video grounding video question answering

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Stronger, Fewer, & Superior: Harnessing vision Foundation Models for Domain Generalized Semantic Segmentation

Stronger, Fewer, & Superior: Harnessing Vision Foundation Mo...

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

作者： Wei, Zhixiang Chen, Lin Jin, Yi Ma, Xiaoxiao Liu, Tianle Ling, Pengyang Wang, Ben Chen, Huaian Zheng, Jinjin Univ Sci & Technol China Hefei Peoples R China Shanghai Ai Lab Shanghai Peoples R China

ISBN: (纸本)9798350353006

In this paper, we first assess and harness various vision Foundation Models (VFMs) in the context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability, we introduce a robust fine-tuning approach, namely "Rein", to parameter-efficiently harness VFMs for DGSS. Built upon a set of trainable tokens, each linked to distinct instances, Rein precisely refines and forwards the feature maps from each layer to the next layer within the backbone. This process produces diverse refinements for different categories within a single image. With fewer trainable parameters, Rein efficiently fine-tunes VFMs for DGSS tasks, surprisingly surpassing full parameter fine-tuning. Extensive experiments across various settings demonstrate that Rein significantly outperforms state-of-the-art methods. Remarkably, with just an extra 1% of trainable parameters within the frozen backbone, Rein achieves a mIoU of 78.4% on the Cityscapes, without accessing any real urban-scene datasets. Code is available at https://***/w1oves/***.

关键词： Domain Generalized Semantic Segmentation parameter-efficient fine-tuning Semantic Segmentation vision Foundation Models

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