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Monocular Total Capture: Posing Face, Body, and Hands in the Wild 32

Monocular Total Capture: Posing Face, Body, and Hands in the...

引用

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

作者： Xiang, Donglai Joo, Hanbyul Sheikh, Yaser Carnegie Mellon Univ Pittsburgh PA 15213 USA Facebook AI Res FAIR Menlo Pk CA USA

ISBN: (纸本)9781728132938

We present the first method to capture the 3D total motion of a target person from a monocular view input. Given an image or a monocular video, our method reconstructs the motion from body, face, and fingers represented by a 3D deformable mesh model. We use an efficient representation called 3D Part Orientation Fields (POFs), to encode the 3D orientations of all body parts in the common 2D image space. POFs are predicted by a Fully Convolutional Network, along with the joint confidence maps. To train our network, we collect a new 3D human motion dataset capturing diverse total body motion of 40 subjects in a multiview system. We leverage a 3D deformable human model to reconstruct total body pose from the CNN outputs with the aid of the pose and shape prior in the model. We also present a texture-based tracking method to obtain temporally coherent motion capture output. We perform thorough quantitative evaluations including comparison with the existing body-specific and hand-specific methods, and performance analysis on camera viewpoint and human pose changes. Finally, we demonstrate the results of our total body motion capture on various challenging in-the-wild videos.

关键词： 3D from Single Image And Body Pose Face Gesture vision + Graphics

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Bridging Video-text Retrieval with Multiple Choice Questions

Bridging Video-text Retrieval with Multiple Choice Questions

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

作者： Ge, Yuying Ge, Yixiao Liu, Xihui Li, Dian Shan, Ying Qie, Xiaohu Luo, Ping Univ Hong Kong Hong Kong Peoples R China ARC Lab San Francisco CA USA Tencent PCG Shenzhen Guangdong Peoples R China Content Understanding Ctr Austin TX USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Pre-training a model to learn transferable video-text representation for retrieval has attracted a lot of attention in recent years. Previous dominant works mainly adopt two separate encoders for efficient retrieval, but ignore local associations between videos and texts. Another line of research uses a joint encoder to interact video with texts, but results in low efficiency since each text-video pair needs to be fed into the model. In this work, we enable fine-grained video-text interactions while maintaining high efficiency for retrieval via a novel pretext task, dubbed as Multiple Choice Questions (MCQ), where a parametric module BridgeFormer is trained to answer the "questions" constructed by the text features via resorting to the video features. Specifically, we exploit the rich semantics of text (i.e., nouns and verbs) to build questions, with which the video encoder can be trained to capture more regional content and temporal dynamics. In the form of questions and answers, the semantic associations between local video-text features can be properly established. BridgeFormer is able to be removed for downstream retrieval, rendering an efficient and flexible model with only two encoders. Our method outperforms state-of-the-art methods on the popular text-to-video retrieval task in five datasets with different experimental setups (i.e., zero-shot and fine-tune), including HowTo100M (one million videos). We further conduct zero-shot action recognition, which can be cast as video-to-text retrieval, and our approach also significantly surpasses its counterparts. As an additional benefit, our method achieves competitive results with much shorter pre-training videos on single-modality downstream tasks, e.g., action recognition with linear evaluation.

关键词： computer vision Semantics Rendering (computer graphics) pattern recognition Task analysis Videos

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Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks

Towards Practical Deployment-Stage Backdoor Attack on Deep N...

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

作者： Qi, Xiangyu Xie, Tinghao Pan, Ruizhe Zhu, Jifeng Yang, Yong Bu, Kai Princeton Univ Princeton NJ 08544 USA Zhejiang Univ Hangzhou Peoples R China Tencent Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

One major goal of the AI security community is to securely and reliably produce and deploy deep learning models for real-world applications. To this end, data poisoning based backdoor attacks on deep neural networks (DNNs) in the production stage (or training stage) and corresponding defenses are extensively explored in recent years. Ironically, backdoor attacks in the deployment stage, which can often happen in unprofessional users' devices and are thus arguably far more threatening in real-world scenarios, draw much less attention of the community. We attribute this imbalance of vigilance to the weak practicality of existing deployment-stage backdoor attack algorithms and the insufficiency of real-world attack demonstrations. To fill the blank, in this work, we study the realistic threat of deployment-stage backdoor attacks on DNNs. We base our study on a commonly used deployment-stage attack paradigm - adversarial weight attack, where adversaries selectively modify model weights to embed backdoor into deployed DNNs. To approach realistic practicality, we propose the first gray-box and physically realizable weights attack algorithm for backdoor injection, namely subnet replacement attack (SRA), which only requires architecture information of the victim model and can support physical triggers in the real world. Extensive experimental simulations and system-level real-world attack demonstrations are conducted. Our results not only suggest the effectiveness and practicality of the proposed attack algorithm, but also reveal the practical risk of a novel type of computer virus that may widely spread and stealthily inject backdoor into DNN models in user devices. By our study, we call for more attention to the vulnerability of DNNs in the deployment stage.

关键词： Deep learning Training computer vision computer viruses Computational modeling Neural networks Production

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Autofocus for Event Cameras

Autofocus for Event Cameras

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

作者： Lin, Shijie Zhang, Yinqiang Yu, Lei Zhou, Bin Luo, Xiaowei Pan, Jia Univ Hong Kong Hong Kong Peoples R China Wuhan Univ Wuhan Peoples R China Beihang Univ Beijing Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Focus control (FC) is crucial for cameras to capture sharp images in challenging real-world scenarios. The autofocus (AF) facilitates the FC by automatically adjusting the focus settings. However, due to the lack of effective At.' methods for the recently introduced event cameras, their FC still relies on naive AF like manual focus adjustments, leading to poor adaptation in challenging real-world conditions. In particular, the inherent differences between event and frame data in terms of sensing modality, noise, temporal resolutions, etc., bring many challenges in designing an effective AF method for event cameras. To address these challenges, we develop a novel event-based autofocus framework consisting of an event-specific focus measure called event rate (ER) and a robust search strategy called event-based golden search (EGS). To verify the performance of our method, we have collected an event-based autofocus dataset (EAD) containing well-synchronized frames, events, and focal positions in a wide variety of challenging scenes with severe lighting and motion conditions. The experiments on this dataset and additional real-world scenarios demonstrated the superiority of our method over state-of-the-art approaches in terms of efficiency and accuracy.

关键词： Photography computer vision Robot vision systems Lighting Manuals Cameras Search problems

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Segment and Caption Anything

Segment and Caption Anything

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

作者： Huang, Xiaoke Wang, Jianfeng Tang, Yansong Zhang, Zheng Hue, Han Lu, Jiwen Wang, Lijuan Liu, Zicheng Tsinghua Univ Shenzhen Int Grad Sch Shenzhen Peoples R China Microsoft Shanghai Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Adv Micro Devices Inc Beijing Peoples R China

ISBN: (纸本)9798350353006

We propose a method to efficiently equip the Segment Anything Model ( SAM) with the ability to generate regional captions. SAM presents strong generalizability to segment anything while is short for semantic understanding. By introducing a lightweight query-based feature mixer, we align the region-specific features with the embedding space of language models for later caption generation. As the number of trainable parameters is small (typically in the order of tens of millions), it costs less computation, less memory usage, and less communication bandwidth, resulting in both fast and scalable training. To address the scarcity problem of regional caption data, we propose to first pretrain our model on objection detection and segmentation tasks. We call this step weak supervision pretraining since the pretraining data only contains category names instead of full-sentence descriptions. The weak supervision pretraining allows us to leverage many publicly available object detection and segmentation datasets. We conduct extensive experiments to demonstrate the superiority of our method and validate each design choice. This work serves as a stepping stone towards scaling up regional captioning data and sheds light on exploring efficient ways to augment SAM with regional semantics. The project page, along with the associated code, can be accessed via the following link.

关键词： Image Captioning Parameter-Efficient Fine-Tuning Regional Captioning Segmentation vision Language Learning

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Generalized Few-shot Semantic Segmentation

Generalized Few-shot Semantic Segmentation

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

作者： Tian, Zhuotao Lai, Xin Jiang, Li Liu, Shu Shu, Michelle Zhao, Hengshuang Jia, Jiaya CUHK Hong Kong Peoples R China MPI Informat Saarbrucken Germany SmartMore Hong Kong Peoples R China Cornell Univ Ithaca NY USA HKU Hong Kong Peoples R China MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Training semantic segmentation models requires a large amount of finely annotated data, making it hard to quickly adapt to novel classes not satisfying this condition. FewShot Segmentation (FS-Seg) tackles this problem with many constraints. In this paper, we introduce a new benchmark, called Generalized Few-Shot Semantic Segmentation (GFS-Seg), to analyze the generalization ability of simultaneously segmenting the novel categories with very few examples and the base categories with sufficient examples. It is the first study showing that previous representative state-of-the-art FS-Seg methods fall short in GFS-Seg and the performance discrepancy mainly comes from the constrained setting of FS-Seg. To make GFS-Seg tractable, we set up a GFS-Seg baseline that achieves decent performance without structural change on the original model. Then, since context is essential for semantic segmentation, we propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the classifier, conditioned on the content of each query image. Both two contributions are experimentally manifested for their substantial practical merit. Extensive experiments on Pascal-VOC and COCO also show that CAPL generalizes well to FS-Seg by achieving competitive performance. Code is available at https: // ***/dvlab-research/GFS-Seg.

关键词： Training Image segmentation computer vision Codes Shape Semantics Prototypes

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EcoNAS: Finding Proxies for Economical Neural Architecture Search

EcoNAS: Finding Proxies for Economical Neural Architecture S...

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

作者： Zhou, Dongzhan Zhou, Xinchi Zhang, Wenwei Loy, Chen Change Yi, Shuai Zhang, Xuesen Ouyang, Wanli Univ Sydney SenseTime Comp Vis Res Grp Sydney NSW Australia Nanyang Technol Univ Singapore Singapore SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781728171685

Neural Architecture Search (NAS) achieves significant progress in many computer vision tasks. While many methods have been proposed to improve the efficiency of NAS, the search progress is still laborious because training and evaluating plausible architectures over large search space is time-consuming. Assessing network candidates under a proxy (i.e., computationally reduced setting) thus becomes inevitable. In this paper, we observe that most existing proxies exhibit different behaviors in maintaining the rank consistency among network candidates. In particular, some proxies can be more reliable - the rank of candidates does not differ much comparing their reduced setting performance and final performance. In this paper, we systematically investigate some widely adopted reduction factors and report our observations. Inspired by these observations, we present a reliable proxy and further formulate a hierarchical proxy strategy. The strategy spends more computations on candidate networks that are potentially more accurate, while discards unpromising ones in early stage with a fast proxy. This leads to an economical evolutionary-based NAS (EcoNAS), which achieves an impressive 400x search time reduction in comparison to the evolutionary-based state of the art [19] (8 vs. 3150 GPU days). Some new proxies led by our observations can also be applied to accelerate other NAS methods while still able to discover good candidate networks with performance matching those found by previous proxy strategies.

关键词： computer vision

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Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies

Collaborative Noisy Label Cleaner: Learning Scene-aware Trai...

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

作者： Gan, Bei Shu, Xiujun Qiao, Ruizhi Wu, Haoqian Chen, Keyu Li, Hanjun Ren, Bo Tencent YouTu Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Movie highlights stand out of the screenplay for efficient browsing and play a crucial role on social media platforms. Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations. (2) Besides previous supervised or unsupervised settings, some existing video corpora can be useful, e.g., trailers, but they are often noisy and incomplete to cover the full highlights. In this work, we study a more practical and promising setting, i.e., reformulating highlight detection as "learning with noisy labels". This setting does not require time-consuming manual annotations and can fully utilize existing abundant video corpora. First, based on movie trailers, we leverage scene segmentation to obtain complete shots, which are regarded as noisy labels. Then, we propose a Collaborative noisy Label Cleaner (CLC) framework to learn from noisy highlight moments. CLC consists of two modules: augmented cross-propagation (ACP) and multi-modality cleaning (MMC). The former aims to exploit the closely related audio-visual signals and fuse them to learn unified multi-modal representations. The latter aims to achieve cleaner highlight labels by observing the changes in losses among different modalities. To verify the effectiveness of CLC, we further collect a large-scale highlight dataset named MovieLights. Comprehensive experiments on MovieLights and YouTube Highlights datasets demonstrate the effectiveness of our approach. Code has been made available at: https://***/TencentYoutuResearch/HighlightDetection-CLC.

关键词： vision applications and systems

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LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of vision & Language Models

LASP: Text-to-Text Optimization for Language-Aware Soft Prom...

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

作者： Bulat, Adrian Tzimiropoulos, Georgios Samsung AI Cambridge Toronto ON Canada Tech Univ Iasi Iasi Romania Qucen Maty Univ London London England

ISBN: (纸本)9798350301298

Soft prompt learning has recently emerged as one of the methods of choice for adapting V&L models to a downstream task using a few training examples. However, current methods significantly overfit the training data, suffering from large accuracy degradation when tested on unseen classes from the same domain. To this end, in this paper, we make the following 4 contributions: (1) To alleviate base class overfitting, we propose a novel Language-Aware Soft Prompting (LASP) learning method by means of a text-to-text cross-entropy loss that maximizes the probability of the learned prompts to be correctly classified with respect to pre-defined hand-crafted textual prompts. (2) To increase the representation capacity of the prompts, we propose grouped LASP where each group of prompts is optimized with respect to a separate subset of textual prompts. (3) We identify a visual-language misalignment introduced by prompt learning and LASP, and more importantly, propose a re-calibration mechanism to address it. (4) We show that LASP is inherently amenable to including, during training, virtual classes, i.e. class names for which no visual samples are available, further increasing the robustness of the learned prompts. Through evaluations on 11 datasets, we show that our approach (a) significantly outperforms all prior works on soft prompting, and (b) matches and surpasses, for the first time, the accuracy on novel classes obtained by hand-crafted prompts and CLIP for 8 out of 11 test datasets. Code will be made available here.

关键词： continual low-shot meta or long-tail learning Transfer

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A Simple Pooling-Based Design for Real-Time Salient Object Detection 32

A Simple Pooling-Based Design for Real-Time Salient Object D...

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Jiang-Jiang Hou, Qibin Cheng, Ming-Ming Feng, Jiashi Jiang, Jianmin Nankai Univ Coll CS TKLNDST Tianjin Peoples R China NUS Singapore Singapore Shenzhen Univ Shenzhen Peoples R China

ISBN: (纸本)9781728132938

We solve the problem of salient object detection by investigating how to expand the role of pooling in convolutional neural networks. Based on the U-shape architecture, we first build a global guidance module (GGM) upon the bottom-up pathway, aiming at providing layers at different feature levels the location information of potential salient objects. We further design a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down pathway. By adding FAMs after the fusion operations in the top-down pathway, coarse-level features from the GGM can be seamlessly merged with features at various scales. These two pooling-based modules allow the high-level semantic features to be progressively refined, yielding detail enriched saliency maps. Experiment results show that our proposed approach can more accurately locate the salient objects with sharpened details and hence substantially improve the performance compared to the previous state-of-the-arts. Our approach is fast as well and can run at a speed of more than 30 FPS when processing a 300 x 400 image. Code can be found at http://***/poolnet/.

关键词： Deep Learning Low-level vision

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