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CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input

CABM: Content-Aware Bit Mapping for Single Image Super-Resol...

引用

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

作者： Tian, Senmao Lu, Ming Liu, Jiaming Guo, Yandong Chen, Yurong Zhang, Shunli Beijing Jiaotong Univ Beijing Peoples R China OPPO Res Inst Shenzhen Peoples R China Intel Labs China Beijing Peoples R China Peking Univ Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350301298

With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K/8K). To reduce the computational and memory cost, current methods first split the large input into local patches and then merge the SR patches into the output. These methods adaptively allocate a subnet for each patch. Quantization is a very important technique for network acceleration and has been used to design the subnets. Current methods train an MLP bit selector to determine the propoer bit for each layer. However, they uniformly sample subnets for training, making simple subnets overfitted and complicated subnets underfitted. Therefore, the trained bit selector fails to determine the optimal bit. Apart from this, the introduced bit selector brings additional cost to each layer of the SR network. In this paper, we propose a novel method named Content-Aware Bit Mapping (CABM), which can remove the bit selector without any performance loss. CABM also learns a bit selector for each layer during training. After training, we analyze the relation between the edge information of an input patch and the bit of each layer. We observe that the edge information can be an effective metric for the selected bit. Therefore, we design a strategy to build an Edge-to-Bit lookup table that maps the edge score of a patch to the bit of each layer during inference. The bit configuration of SR network can be determined by the lookup tables of all layers. Our strategy can find better bit configuration, resulting in more efficient mixed precision networks. We conduct detailed experiments to demonstrate the generalization ability of our method. The code will be released.

关键词： Low-level vision

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GLAD: Global-Local View Alignment and Background Debiasing for Unsupervised Video Domain Adaptation with Large Domain Gap

GLAD: Global-Local View Alignment and Background Debiasing f...

引用

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

作者： Lee, Hyogun Bae, Kyungho Ha, Seong Jong Ko, Yumin Park, Gyeong-Moon Choi, Jinwoo Kyung Hee Univ Seoul South Korea CJ Corp AI Ctr Seoul South Korea NCSOFT Seongnam South Korea

ISBN: (纸本)9798350318920;9798350318937

In this work, we tackle the challenging problem of unsupervised video domain adaptation (UVDA) for action recognition. We specifically focus on scenarios with a substantial domain gap, in contrast to existing works primarily deal with small domain gaps between labeled source domains and unlabeled target domains. To establish a more realistic setting, we introduce a novel UVDA scenario, denoted as Kinetics -> BABEL, with a more considerable domain gap in terms of both temporal dynamics and background shifts. To tackle the temporal shift, i.e., action duration difference between the source and target domains, we propose a global-local view alignment approach. To mitigate the background shift, we propose to learn temporal order sensitive representations by temporal order learning and background invariant representations by background augmentation. We empirically validate that the proposed method shows significant improvement over the existing methods on the Kinetics -> BABEL dataset with a large domain gap. The code is available at https://***/KHU-VLL/GLAD.

关键词： Algorithms Algorithms and algorithms formulations Machine learning architectures Video recognition and understanding

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Human Hands as Probes for Interactive Object Understanding

Human Hands as Probes for Interactive Object Understanding

引用

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

作者： Goyal, Mohit Modi, Sahil Goyal, Rishabh Gupta, Saurabh Univ Illinois Champaign IL 61820 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Interactive object understanding, or what we can do to objects and how is a long-standing goal of computer vision. In this paper, we tackle this problem through observation of human hands in in-the-wild egocentric videos. We demonstrate that observation of what human hands interact with and how can provide both the relevant data and the necessary supervision. Attending to hands, readily localizes and stabilizes active objects for learning and reveals places where interactions with objects occur. Analyzing the hands shows what we can do to objects and how. We apply these basic principles on the EPIC-KITCHENS dataset, and successfully learn state-sensitive features, and object affordances (regions of interaction and afforded grasps), purely by observing hands in egocentric videos.

关键词： computer vision Affordances pattern recognition Probes Unsupervised learning Videos

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Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification

Contrastive Viewpoint-aware Shape Learning for Long-term Per...

引用

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

作者： Nguyen, Vuong D. Khaldi, Khadija Dung Nguyen Mantini, Pranav Shah, Shishir Univ Houston Houston TX 77004 USA Hanoi Univ Sci & Technol Hanoi Vietnam

ISBN: (纸本)9798350318920;9798350318937

Traditional approaches for Person Re-identification (ReID) rely heavily on modeling the appearance of persons. This measure is unreliable over longer durations due to the possibility for changes in clothing or biometric information. Furthermore, viewpoint changes significantly degrade the matching ability of these methods. In this paper, we propose "Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-Identification" (CVSL) to address these challenges. Our method robustly extracts local and global texture-invariant human body shape cues from 2D pose using the Relational Shape Embedding branch, which consists of a pose estimator and a shape encoder built on a Graph Attention Network. To enhance the discriminability of the shape and appearance of identities under viewpoint variations, we propose Contrastive Viewpoint-aware Losses (CVL). CVL leverages contrastive learning to simultaneously minimize the intra-class gap under different viewpoints and maximize the inter-class gap under the same viewpoint. Extensive experiments demonstrate that our proposed framework outperforms state-of-the-art methods on long-term person Re-ID benchmarks.

关键词： Algorithms Algorithms Biometrics body pose face gesture Image recognition and understanding

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Random Walks for Temporal Action Segmentation with Timestamp Supervision

Random Walks for Temporal Action Segmentation with Timestamp...

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

作者： Hirsch, Roy Cohen, Regev Golany, Tomer Freedman, Daniel Rivlin, Ehud Verily AI Tel Aviv Israel

ISBN: (纸本)9798350318920;9798350318937

Temporal action segmentation relates to high-level video understanding, commonly formulated as frame-wise classification of untrimmed videos into predefined actions. Fully-supervised deep-learning approaches require dense video annotations which are time and money consuming. Furthermore, the temporal boundaries between consecutive actions typically are not well-defined, leading to inherent ambiguity and inter-rater disagreement. A promising approach to remedy these limitations is timestamp supervision, requiring only one labeled frame per action instance in a training video. In this work, we reformulate the task of temporal segmentation as a graph segmentation problem with weakly-labeled vertices. We introduce an efficient segmentation method based on random walks on graphs, obtained by solving a sparse system of linear equations. Furthermore, the proposed technique can be employed in any one or combination of the following forms: (1) as a standalone solution for generating dense pseudo-labels from timestamps;(2) as a training loss;(3) as a smoothing mechanism given intermediate predictions. Extensive experiments with three datasets (50Salads, Breakfast, GTEA) show that our method competes with state-of-the-art, and allows the identification of regions of uncertainty around action boundaries.

关键词： Algorithms Algorithms and algorithms formulations Machine learning architectures Video recognition and understanding

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DeVOS: Flow-Guided Deformable Transformer for Video Object Segmentation

DeVOS: Flow-Guided Deformable Transformer for Video Object S...

引用

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

作者： Fedynyak, Volodymyr Romanus, Yaroslav Hlovatskyi, Bohdan Sydor, Bohdan Dobosevych, Oles Babin, Igor Riazantsev, Roman Ukrainian Cathol Univ Lvov Ukraine ADVA Soft Dresden Germany

ISBN: (纸本)9798350318920;9798350318937

The recent works on Video Object Segmentation achieved remarkable results by matching dense semantic and instance-level features between the current and previous frames for long-time propagation. Nevertheless, global feature matching ignores scene motion context, failing to satisfy temporal consistency. Even though some methods introduce local matching branch to achieve smooth propagation, they fail to model complex appearance changes due to the constraints of the local window. In this paper, we present DeVOS (Deformable VOS), an architecture for Video Object Segmentation that combines memory-based matching with motion-guided propagation resulting in stable long-term modeling and strong temporal consistency. For short-term local propagation, we propose a novel attention mechanism ADVA (Adaptive Deformable Video Attention), allowing the adaption of similarity search region to query-specific semantic features, which ensures robust tracking of complex shape and scale changes. DeVOS employs an optical flow to obtain scene motion features which are further injected to deformable attention as strong priors to learnable offsets. Our method achieves top-rank performance on DAVIS 2017 val and test-dev (88.1%, 83.0%), YouTube-VOS 2019 val (86.6%) while featuring consistent run-time speed and stable memory consumption.

关键词： Algorithms Algorithms Applications Image recognition and understanding Remote Sensing Video recognition and understanding

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Detecting Content Segments from Online Sports Streaming Events: Challenges and Solutions

Detecting Content Segments from Online Sports Streaming Even...

引用

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

作者： Liu, Zongyi Feng, Yarong Luo, Shunyan Ling, Yuan Dong, Shujing Wang, Shuyi Amazon Com Customer Experience & Business Trends 2121 7th Ave Seattle WA 98121 USA

ISBN: (纸本)9798350318920;9798350318937

Developing a client-side segmentation algorithm for online sports streaming holds significant importance. For instance, in order to assess the video quality from an end-user perspective such as artifact detection, it is important to initially segment the content within the streaming playback. The challenge lies in localizing the content due to the intricate scene changes between content and non-content sections in popular sports like football, tennis, baseball, and more. Client-side content detection can be implemented in two ways: intrusively, involving the interception of network traffic and parsing service provider data and logs, or non-intrusively, which entails capturing streamed videos from content providers and subjecting them to analysis using computer vision technologies. In this paper, we introduce a non-intrusive framework that leverages a combination of traditional machine learning algorithms and deep neural networks (DNN) to distinguish content sections from non-content sections across various online sports streaming services. Our algorithm has demonstrated a remarkable level of accuracy and effectiveness in sports broadcasting events, effectively overcoming the complexities introduced by intricate non-content insertion methods during the games.

关键词： Algorithms Algorithms Applications Smartphones / end user devices Video recognition and understanding vision + language and/or other modalities

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SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation

SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework...

引用

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

作者： Shin, Hyungseob Kim, Hyeongyu Kim, Sewon Jun, Yohan Eo, Taejoon Hwang, Dosik Yonsei Univ Sch Elect & Elect Engn Seoul South Korea Yonsei Univ Dept Oral & Maxillofacial Radiol Coll Dent Seoul South Korea Yonsei Univ Dept Radiol Seoul South Korea Yonsei Univ Ctr Clin Imaging Data Sci Coll Med Seoul South Korea Naver AI Lab Bundangdong South Korea Naver Cloud Seongnam South Korea Probe Med Inc Seoul South Korea Martinos Ctr Biomed Imaging Boston MA USA Harvard Med Sch Boston MA USA Korea Inst Sci & Technol Ctr Healthcare Robot Seoul South Korea

ISBN: (纸本)9798350301298

Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical imaging fields. Unsupervised domain adaptation (UDA) can alleviate this problem, which makes it possible to use annotated data in one imaging modality to train a network that can successfully perform segmentation on target imaging modality with no labels. In this work, we propose SDC-UDA, a simple yet effective volumetric UDA framework for Slice-Direction Continuous cross-modality medical image segmentation which combines intra-and inter-slice self-attentive image translation, uncertainty-constrained pseudo-label refinement, and volumetric self-training. Our method is distinguished from previous methods on UDA for medical image segmentation in that it can obtain continuous segmentation in the slice direction, thereby ensuring higher accuracy and potential in clinical practice. We validate SDC-UDA with multiple publicly available cross-modality medical image segmentation datasets and achieve state-of-the-art segmentation performance, not to mention the superior slice-direction continuity of prediction compared to previous studies.

关键词： cell microscopy Medical and biological vision

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STYLIP: Multi-Scale Style-Conditioned Prompt Learning for CLIP-based Domain Generalization

STYLIP: Multi-Scale Style-Conditioned Prompt Learning for CL...

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

作者： Bose, Shirsha Jha, Ankit Fini, Enrico Singha, Mainak Ricci, Elisa Banerjee, Biplab Tech Univ Munich Munich Germany Indian Inst Technol Mumbai Maharashtra India Univ Trento Trento Italy

ISBN: (纸本)9798350318920;9798350318937

Large-scale foundation models, such as CLIP, have demonstrated impressive zero-shot generalization performance on downstream tasks, leveraging well-designed language prompts. However, these prompt learning techniques often struggle with domain shift, limiting their generalization capabilities. In our study, we tackle this issue by proposing STYLIP, a novel approach for Domain Generalization (DG) that enhances CLIP's classification performance across domains. Our method focuses on a domain-agnostic prompt learning strategy, aiming to disentangle the visual style and content information embedded in CLIP's pre-trained vision encoder, enabling effortless adaptation to novel domains during inference. To achieve this, we introduce a set of style projectors that directly learn the domain-specific prompt tokens from the extracted multi-scale style features. These generated prompt embeddings are subsequently combined with the multi-scale visual content features learned by a content projector. The projectors are trained in a contrastive manner, utilizing CLIP's fixed vision and text backbones. Through extensive experiments conducted in five different DG settings on multiple benchmark datasets, we consistently demonstrate that STYLIP outperforms the current state-of-the-art (SOTA) methods.

关键词： Algorithms Algorithms Image recognition and understanding vision + language and/or other modalities

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Self-Supervised Learning for Visual Relationship Detection through Masked Bounding Box Reconstruction

Self-Supervised Learning for Visual Relationship Detection t...

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

作者： Anastasakis, Zacharias Mallis, Dimitrios Diomataris, Markos Alexandridis, George Kollias, Stefanos Pitsikalis, Vassilis Deeplab Athens Greece Univ Luxembourg SnT Luxembourg Luxembourg ETH Zurich Switzerland Natl Tech Univ Athens Athens Greece

ISBN: (纸本)9798350318920;9798350318937

We present a novel self-supervised approach for representation learning, particularly for the task of Visual Relationship Detection (VRD). Motivated by the effectiveness of Masked Image Modeling (MIM), we propose Masked Bounding Box Reconstruction (MBBR), a variation of MIM where a percentage of the entities/objects within a scene are masked and subsequently reconstructed based on the unmasked objects. The core idea is that, through object-level masked modeling, the network learns context-aware representations that capture the interaction of objects within a scene and thus are highly predictive of visual object relationships. We extensively evaluate learned representations, both qualitatively and quantitatively, in a few-shot setting and demonstrate the efficacy of MBBR for learning robust visual representations, particularly tailored for VRD. The proposed method is able to surpass state-of-the-art VRD methods on the Predicate Detection (PredDet) evaluation setting, using only a few annotated samples. We make our code available at https://github. com/ deeplabai/SelfSupervisedVRD.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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