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Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos

Bridge-Prompt: Towards Ordinal Action Understanding in Instr...

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

作者： Li, Muheng Chen, Lei Duan, Yueqi Hu, Zhilan Feng, Jianjiang Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Huawei Technol Co Ltd Media Technol Inst Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Action recognition models have shown a promising capability to classify human actions in short video clips. In a real scenario, multiple correlated human actions commonly occur in particular orders, forming semantically meaningful human activities. Conventional action recognition approaches focus on analyzing single actions. However, they fail to fully reason about the contextual relations between adjacent actions, which provide potential temporal logic for understanding long videos. In this paper, we propose a prompt-based framework, Bridge-Prompt (Br-Prompt), to model the semantics across adjacent actions, so that it simultaneously exploits both out-of-context and contextual information from a series of ordinal actions in instructional videos. More specifically, we reformulate the individual action labels as integrated text prompts for supervision, which bridge the gap between individual action semantics. The generated text prompts are paired with corresponding video clips, and together co-train the text encoder and the video encoder via a contrastive approach. The learned vision encoder has a stronger capability for ordinal-action-related downstream tasks, e.g. action segmentation and human activity recognition. We evaluate the performances of our approach on several video datasets: Georgia Tech Egocentric Activities (GTEA), 50Salads, and the Breakfast dalaset. Br-Prompt achieves state-of-the-art on multiple benchmarks.

关键词： computer vision Codes Semantics Benchmark testing pattern recognition Task analysis Context modeling

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CaPriDe Learning: Confidential and Private Decentralized Learning based on Encryption-friendly Distillation Loss

CaPriDe Learning: Confidential and Private Decentralized Lea...

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

作者： Tastan, Nurbek Nandakumar, Karthik Mohamed bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

Large volumes of data required to train accurate deep neural networks (DNNs) are seldom available with any single entity. Often, privacy concerns prevent entities from sharing data with each other or with a third-party learning service provider. While cross-silo federated learning (FL) allows collaborative learning of large DNNs without sharing the data itself, most existing cross-silo FL algorithms have an unacceptable utility-privacy trade-off. In this work, we propose a framework called Confidential and Private Decentralized (CaPriDe) learning, which optimally leverages the power of fully homomorphic encryption (FHE) to enable collaborative learning without compromising on the confidentiality and privacy of data. In CaPridDe learning, participating entities release their private data in an encrypted form allowing other participants to perform inference in the encrypted domain. The crux of CaPriDe learning is mutual knowledge distillation between multiple local models through a novel distillation loss, which is an approximation of the Kullback-Leibler (KL) divergence between the local predictions and encrypted inferences of other participants on the same data that can be computed in the encrypted domain. Extensive experiments on three datasets show that CaPriDe learning can improve the accuracy of local models without any central coordination, provide strong guarantees of data confidentiality and privacy, and has the ability to handle statistical heterogeneity. Constraints on the model architecture (arising from the need to be FHE-friendly), limited scalability, and computational complexity of encrypted domain inference are the main limitations of the proposed approach. The code can be found at https://***/tnurbek/capride-learning.

关键词： accountability ethics in vision fairness privacy Transparency

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Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation 32

Competitive Collaboration: Joint Unsupervised Learning of De...

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

作者： Ranjan, Anurag Jampani, Varun Balles, Lukas Kim, Kihwan Sun, Deqing Wulff, Jonas Black, Michael J. Max Planck Inst Intelligent Syst Stuttgart Germany NVIDIA Santa Clara CA USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9781728132938

We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions. Our key insight is that these four fundamental vision problems are coupled through geometric constraints. Consequently, learning to solve them together simplifies the problem because the solutions can reinforce each other. We go beyond previous work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. To that end, we introduce Competitive Collaboration, a framework that facilitates the coordinated training of multiple specialized neural networks to solve complex problems. Competitive Collaboration works much like expectation-maximization, but with neural networks that act as both competitors to explain pixels that correspond to static or moving regions, and as collaborators through a moderator that assigns pixels to be either static or independently moving. Our novel method integrates all these problems in a common framework and simultaneously reasons about the segmentation of the scene into moving objects and the static background, the camera motion, depth of the static scene structure, and the optical flow of moving objects. Our model is trained without any supervision and achieves state-of-the-art performance among joint unsupervised methods on all sub-problems.

关键词： Deep Learning Low-level vision

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Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective

Solving Oscillation Problem in Post-Training Quantization Th...

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

作者： Ma, Yuexiao Li, Huixia Zheng, Xiawu Xiao, Xuefeng Wang, Rui Wen, Shilei Pan, Xin Chao, Fei Ji, Rongrong Xiamen Univ Minist Educ China Key Lab Multimedia Trusted Percept & Efficient Co Sch Informat Xiamen 361005 Peoples R China ByteDance Inc Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Xiamen Univ Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically, benefitting from its data privacy and low computation costs. We argue that an overlooked problem of oscillation is in the PTQ methods. In this paper, we take the initiative to explore and present a theoretical proof to explain why such a problem is essential in PTQ. And then, we try to solve this problem by introducing a principled and generalized framework theoretically. In particular, we first formulate the oscillation in PTQ and prove the problem is caused by the difference in module capacity. To this end, we define the module capacity (ModCap) under data-dependent and data-free scenarios, where the differentials between adjacent modules are used to measure the degree of oscillation. The problem is then solved by selecting top-k differentials, in which the corresponding modules are jointly optimized and quantized. Extensive experiments demonstrate that our method successfully reduces the performance drop and is generalized to different neural networks and PTQ methods. For example, with 2/4 bit ResNet-50 quantization, our method surpasses the previous state-of-the-art method by 1.9%. It becomes more significant on small model quantization, e.g. surpasses BRECQ method by 6.61% on MobileNetV2 x0.5.

关键词： Efficient and scalable vision

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Learning to Exploit Temporal Structure for Biomedical vision-Language Processing

Learning to Exploit Temporal Structure for Biomedical Vision...

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

作者： Bannur, Shruthi Hyland, Stephanie Liu, Qianchu Perez-Garcia, Fernando Ilse, Maximilian Castro, Daniel C. Boecking, Benedikt Sharma, Harshita Bouzid, Kenza Thieme, Anja Schwaighofer, Anton Wetscherek, Maria Lungren, Matthew P. Nori, Aditya Alvarez-Valle, Javier Oktay, Ozan Microsoft Hlth Futures Redmond WA 98052 USA

ISBN: (纸本)9798350301298

Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data.

关键词： cell microscopy Medical and biological vision

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S4Net: Single Stage Salient-Instance Segmentation 32

S4Net: Single Stage Salient-Instance Segmentation

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

作者： Fan, Ruochen Cheng, Ming-Ming Hou, Qibin Mu, Tai-Jiang Wang, Jingdong Hu, Shi-Min Tsinghua Univ Beijing Peoples R China BNRist Beijing Peoples R China Nankai Univ Tianjin Peoples R China MSRA Beijing Peoples R China

ISBN: (纸本)9781728132938

We consider an interesting problem-salient instance segmentation in this paper. Other than producing bounding boxes, our network also outputs high-quality instance-level segments. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch regards not only local context inside each detection window but also its surrounding context, enabling us to distinguish the instances in the same scope even with obstruction. Our network is end-to-end trainable and runs at a fast speed (40 fps when processing an image with resolution 320 x 320). We evaluate our approach on a public available benchmark and show that it outperforms other alternative solutions. We also provide a thorough analysis of the design choices to help readers better understand the functions of each part of our network. The source code can be found at https://***/S4Net.

关键词： Categorization Deep Learning Low-level vision recognition: Detection Retrieval vision Applications and Systems

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ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing

ConZIC: Controllable Zero-shot Image Captioning by Sampling-...

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

作者： Zeng, Zequn Zhang, Hao Lu, Ruiying Wang, Dongsheng Chen, Bo Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9798350301298

Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data. As a good start and the only existing outcome of zero-shot image captioning (IC), ZeroCap abandons supervised training and sequentially searches every word in the caption using the knowledge of large-scale pre-trained models. Though effective, its autoregressive generation and gradient-directed searching mechanism limit the diversity of captions and inference speed, respectively. Moreover, ZeroCap does not consider the controllability issue of zero-shot IC. To move forward, we propose a framework for Controllable Zero-shot IC, named ConZIC. The core of ConZIC is a novel sampling-based non-autoregressive language model named GibbsBERT, which can generate and continuously polish every word. Extensive quantitative and qualitative results demonstrate the superior performance of our proposed ConZIC for both zero-shot IC and controllable zero-shot IC. Especially, ConZIC achieves about 5x faster generation speed than ZeroCap, and about 1.5x higher diversity scores, with accurate generation given different control signals. Our code is available at https://***/joeyz0z/ConZIC.

关键词： language reasoning vision

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BTS: A Bi-lingual Benchmark for Text Segmentation in the Wild

BTS: A Bi-lingual Benchmark for Text Segmentation in the Wil...

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

作者： Xu, Xixi Qi, Zhongang Ma, Jianqi Zhang, Honglun Shan, Ying Qie, Xiaohu ARC Lab Cdmx Mexico Tencent PCG Palo Alto CA USA Tsinghua Univ Beijing Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

As a prerequisite of many text-related tasks such as text erasing and text style transfer, text segmentation arouses more and more attention recently. Current researches mainly focus on only English characters and digits, while few work studies Chinese characters due to the lack of public large-scale and high-quality Chinese datasets, which limits the practical application scenarios of text segmentation. Different from English which has a limited alphabet of letters, Chinese has much more basic characters with complex structures, making the problem more difficult to deal with. To better analyze this problem, we propose the Bilingual Text Segmentation (BTS) dataset, a benchmark that covers various common Chinese scenes including 14, 250 diverse and fine-annotated text images. BTS mainly focuses on Chinese characters, and also contains English words and digits. We also introduce Prior Guided Text Segmentation Network (PGTSNet), the first baseline to handle bilingual and complex-structured text segmentation. A plug-in text region highlighting module and a text perceptual discriminator are proposed in PGTSNet to supervise the model with text prior, and guide for more stable and finer text segmentation. A variation loss is also employed for suppressing background noise under complex scene. Extensive experiments are conducted not only to demonstrate the necessity and superiority of the proposed dataset BTS, but also to show the effectiveness of the proposed PGTSNet compared with a variety of state-of-the-art text segmentation methods.

关键词： Training Image segmentation computer vision Protocols Annotations Benchmark testing Character recognition

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Beyond Grobner Bases: Basis Selection for Minimal Solvers 31

Beyond Grobner Bases: Basis Selection for Minimal Solvers

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Larsson, Viktor Oskarsson, Magnus Astrom, Kalle Wallis, Alge Kukelova, Zuzana Pajdla, Tomas Lund Univ Lund Sweden Univ Bath Bath Avon England FEE Copenhagen Denmark CIIRC Bengaluru India Czech Tech Univ Prague Czech Republic

ISBN: (纸本)9781538664209

Many computer vision applications require robust estimation of the underlying geometry, in terms of camera motion and 3D structure of the scene. These robust methods often rely on running minimal solvers in a RANSAC framework. In this paper we show how we can make polynomial solvers based on the action matrix method faster, by careful selection of the monomial bases. These monomial bases have traditionally been based on a Grobner basis for the polynomial ideal. Here we describe how we can enumerate all such bases in an efficient way. We also show that going beyond Grobner bases leads to more efficient solvers in many cases. We present a novel basis sampling scheme that we evaluate on a number of problems.

关键词： Fans computer vision Generators Standards Sensors Estimation Mathematical model

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Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives

Primitive3D: 3D Object Dataset Synthesis from Randomly Assem...

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

作者： Li, Xinke Ding, Henghui Tong, Zekun Wu, Yuwei Chee, Yeow Meng Natl Univ Singapore Singapore Singapore ByteDance Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Numerous advancements in deep learning can be attributed to the access to large-scale and well-annotated datasets. However, such a dataset is prohibitively expensive in 3D computer vision due to the substantial collection cost. To alleviate this issue, we propose a cost-effective method for automatically generating a large amount of 3D objects with annotations. In particular, we synthesize objects simply by assembling multiple random primitives. These objects are thus auto-annotated with part labels originating from primitives. This allows us to perform multi-task learning by combining the supervised segmentation with unsupervised reconstruction. Considering the large overhead of learning on the generated dataset, we further propose a dataset distillation strategy to remove redundant samples regarding a target dataset. We conduct extensive experiments for the downstream tasks of 3D object classification. The results indicate that our dataset, together with multitask pretraining on its annotations, achieves the best performance compared to other commonly used datasets. Further study suggests that our strategy can improve the model performance by pretraining and fine-tuning scheme, especially for the dataset with a small scale. In addition, pretraining with the proposed dataset distillation method can save 86% of the pretraining time with negligible performance degradation. We expect that our attempt provides a new datacentric perspective for training 3D deep models.

关键词： Training Solid modeling computer vision Three-dimensional displays Annotations Computational modeling Soft sensors

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