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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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UC²: Universal Cross-lingual Cross-modal vision-and-Language Pre-training

UC<SUP>2</SUP>: Universal Cross-lingual Cross-modal Vision-a...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhou, Mingyang Zhou, Luowei Wang, Shuohang Cheng, Yu Li, Linjie Yu, Zhou Liu, Jingjing Univ Calif Davis Davis CA 95616 USA Microsoft Dynamics 365 AI Res Redmond WA USA

ISBN: (纸本)9781665445092

vision-and-language pre-training has achieved impressive success in learning multimodal representations between vision and language. To generalize this success to non-English languages, we introduce UC2, the first machine translation-augmented framework for cross-lingual cross-modal representation learning. To tackle the scarcity problem of multilingual captions for image datasets, we first augment existing English-only datasets with other languages via machine translation (MT). Then we extend the standard Masked Language Modeling and Image-Text Matching training objectives to multilingual setting, where alignment between different languages is captured through shared visual context (i.e., using image as pivot). To facilitate the learning of a joint embedding space of images and all languages of interest, we further propose two novel pre-training tasks, namely Masked Region-to-Token Modeling (MRTM) and Visual Translation Language Modeling (VTLM), leveraging MT-enhanced translated data. Evaluation on multilingual image-text retrieval and multilingual visual question answering benchmarks demonstrates that our proposed framework achieves new state of the art on diverse non-English benchmarks while maintaining comparable performance to monolingual pre-trained models on English tasks.

关键词： Training Visualization Benchmark testing Knowledge discovery Data models pattern recognition Machine translation

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VSpSR: Explorable Super-Resolution via Variational Sparse Representation

VSpSR: Explorable Super-Resolution via Variational Sparse Re...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhou, Hangqi Huang, Chao Gao, Shangqi Zhuang, Xiahai Fudan Univ Sch Data Sci Shanghai Peoples R China

ISBN: (纸本)9781665448994

Super-resolution (SR) is an ill-posed problem, which means that infinitely many high-resolution (HR) images can be degraded to the same low-resolution (LR) image. To study the one-to-many stochastic SR mapping, we implicitly represent the non-local self-similarity of natural images and develop a Variational Sparse framework for Super-Resolution (VSpSR) via neural networks. Since every small patch of a HR image can be well approximated by the sparse representation of atoms in an over-complete dictionary, we design a two-branch module, i.e., VSpM, to explore the SR space. Concretely, one branch of VSpM extracts patch-level basis from the LR input, and the other branch infers pixel-wise variational distributions with respect to the sparse coefficients. By repeatedly sampling coefficients, we could obtain infinite sparse representations, and thus generate diverse HR images. According to the preliminary results of NTIRE 2021 challenge on learning SR space, our team ranks 7-th in terms of released scores.

关键词： Visualization computer vision Dictionaries conferences Superresolution Neural networks Space exploration

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Training Domain-invariant Object Detector Faster with Feature Replay and Slow Learner

Training Domain-invariant Object Detector Faster with Featur...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lee, Chaehyeon Seo, Junghoon Jung, Heechul Kyungpook Natl Univ Dept Artificial Intelligence Daegu South Korea SI Analyt Co Ltd Daejeon South Korea SI Analyt Daejeon South Korea

ISBN: (纸本)9781665448994

In deep learning-based object detection on remote sensing domain, nuisance factors, which affect observed variables while not affecting predictor variables, often matters because they cause domain changes. Previously, nuisance disentangled feature transformation (NDFT) was proposed to build domain-invariant feature extractor with with knowledge of nuisance factors. However, NDFT requires enormous time in a training phase, so it has been impractical. In this paper, we introduce our proposed method, A-NDFT, which is an improvement to NDFT. A-NDFT utilizes two acceleration techniques, feature replay and slow learner. Consequently, on a large-scale UAVDT benchmark, it is shown that our framework can reduce the training time of NDFT from 31 hours to 3 hours while still maintaining the performance. The code will be made publicly available online(1).

关键词： Training computer vision Vehicle detection conferences Object detection Detectors Benchmark testing

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Comparing Representations in Tracking for Event Camera-based SLAM

Comparing Representations in Tracking for Event Camera-based...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jiao, Jianhao Huang, Huaiyang Li, Liang He, Zhijian Zhu, Yilong Liu, Ming Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665448994

This paper investigates two typical image-type representations for event camera-based tracking: time surface (TS) and event map (EM). Based on the original TS-based tracker, we make use of these two representations' complementary strengths to develop an enhanced version. The proposed tracker consists of a general strategy to evaluate the optimization problem's degeneracy online and then switch proper representations. Both TS and EM are motion- and scene-dependent, and thus it is important to figure out their limitations in tracking. We develop six tracker variations and conduct a thorough comparison of them on sequences covering various scenarios and motion complexities. We release our implementations and detailed results to benefit the research community on event cameras: https://***/gogojjh/ESVO_extension.

关键词： Tracking loops computer vision Simultaneous localization and mapping Tracking conferences Switches Trajectory

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Rethinking the Domain Gap in Near-infrared Face recognition

Rethinking the Domain Gap in Near-infrared Face Recognition

引用

IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Michail Tarasiou Jiankang Deng Stefanos Zafeiriou Imperial College London

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Heterogeneous face recognition (HFR) involves the intricate task of matching face images across the visual domains of visible (VIS) and near-infrared (NIR). While much of the existing literature on HFR identifies the domain gap as a primary challenge and directs efforts towards bridging it at either the input or feature level, our work deviates from this trend. We observe that large neural networks, unlike their smaller counterparts, when pretrained on large scale homogeneous VIS data, demonstrate exceptional zero-shot performance in HFR, suggesting that the domain gap might be less pronounced than previously believed. By approaching the HFR problem as one of low-data fine-tuning, we introduce a straightforward framework: comprehensive pre-training, succeeded by a regularized fine-tuning strategy, that matches or surpasses the current state-of-the-art on four publicly available benchmarks. Given its simplicity and demonstrably strong performance, our method could be used as a practical solution for adjusting face recognition models to HFR as well as a new baseline for future HFR research. Corresponding training and evaluation codes can be found at https://***/michaeltrs/RethinkNIRVIS.

关键词： Training Visualization computer vision Codes Face recognition conferences Refining

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Inaccuracy of State-Action Value Function For Non-Optimal Actions in Adversarially Trained Deep Neural Policies

Inaccuracy of State-Action Value Function For Non-Optimal Ac...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Korkmaz, Ezgi KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781665448994

The introduction of deep neural networks as function approximator for the state-action value function has led to the creation of a new research area for self-learning systems that explore policies from high dimensional input. While the success of deep neural policies has resulted in the deployment of these policies in diversified application domains, there are significant concerns regarding their robustness towards specifically crafted malicious perturbations introduced to their inputs. Several studies have focused on making deep neural policies resistant to such perturbations via training with the existence of these perturbations (i.e. adversarial training). In this paper we focus on conducting an investigation on the state-action value function learned by state-of-the-art adversarially trained deep neural policies and vanilla trained deep neural policies. We perform several experiments in the OpenAI Baselines and we show that the state-action value functions learned by vanilla trained deep neural policies have better estimates for the non-optimal actions than the state-of-the-art adversarially trained deep neural policies. We believe our study lays out intriguing properties of adversarial training and could be critical step towards obtaining robust and reliable policies.

关键词： Training Resistance Deep learning computer vision Systematics Perturbation methods conferences

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Leveraging Line-point Consistence to Preserve Structures forWide Parallax Image Stitching

Leveraging Line-point Consistence to Preserve Structures for...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jia, Qi Li, ZhengJun Fan, Xin Zhao, Haotian Teng, Shiyu Ye, Xinchen Latecki, Longin Jan Dalian Univ Technol Int Sch Informat Sci & Engn Dalian Peoples R China Temple Univ Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9781665445092

Generating high-quality stitched images with natural structures is a challenging task in computer vision. In this paper, we succeed in preserving both local and global geometric structures for wide parallax images, while reducing artifacts and distortions. A projective invariant, Characteristic Number, is used to match co-planar local sub-regions for input images. The homography between these well-matched sub-regions produces consistent line and point pairs, suppressing artifacts in overlapping areas. We explore and introduce global collinear structures into an objective function to specify and balance the desired characters for image warping, which can preserve both local and global structures while alleviating distortions. We also develop comprehensive measures for stitching quality to quantify the collinearity of points and the discrepancy of matched line pairs by considering the sensitivity to linear structures for human vision. Extensive experiments demonstrate the superior performance of the proposed method over the state-of-the-art by presenting sharp textures and preserving prominent natural structures in stitched images. Especially, our method not only exhibits lower errors but also the least divergence across all test images. Code is available at https://***/dut-media-lab/ImageStitching.

关键词： computer vision Sensitivity Codes Distortion Linear programming pattern recognition Distortion measurement

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Occlusion Guided Scene Flow Estimation on 3D Point Clouds

Occlusion Guided Scene Flow Estimation on 3D Point Clouds

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ouyang, Bojun Raviv, Dan Tel Aviv Univ Tel Aviv Israel

ISBN: (纸本)9781665448994

3D scene flow estimation is a vital tool in perceiving our environment given depth or range sensors. Unlike optical flow, the data is usually sparse and in most cases partially occluded in between two temporal samplings. Here we propose a new scene flow architecture called OGSF-Net which tightly couples the learning for both flow and occlusions between frames. Their coupled symbiosis results in a more accurate prediction of flow in space. Unlike a traditional multi-action network, our unified approach is fused throughout the network, boosting performances for both occlusion detection and flow estimation. Our architecture is the first to gauge the occlusion in 3D scene flow estimation on point clouds. In key datasets such as Flyingthings3D and KITTI, we achieve the state-of-the-art results.(1,2)

关键词： Symbiosis Measurement Deep learning Three-dimensional displays Estimation computer architecture Tools

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Learning Melanocytic Proliferation Segmentation in Histopathology Images from Imperfect Annotations

Learning Melanocytic Proliferation Segmentation in Histopath...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Kechun Mokhtari, Mojgan Li, Beibin Nofallah, Shima May, Caitlin Chang, Oliver Knezevich, Stevan Elmore, Joann Shapiro, Linda Univ Washington Seattle WA 98195 USA Isfahan Univ Med Sci Esfahan Iran Dermatopathol Northwest Bellevue WA USA VA Puget Sound Seattle WA USA Pathol Associates Inc Clovis CA USA Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9781665448994

Melanoma is the third most common type of skin cancer and is responsible for the most skin cancer deaths. A diagnosis of melanoma is made by the visual interpretation of tissue sections by a pathologist, a challenging task given the complexity and breadth of melanocytic lesions and the subjective nature of biopsy interpretation. We leverage advances in computer vision to aid melanoma diagnosis by segmenting potential regions of lesions on digital images of whole slide skin biopsies. In this study, we demonstrate a Mask-R-CNN-based segmentation framework for such a purpose. To alleviate the cost of data annotation, we leverage a sparse annotation pipeline. Our model can be trained on sparse and noisy labels and achieves state-of-the-art performance in identifying melanocytic proliferations, producing a segmentation with Dice score 0.719, mIOU 0.740 and overall pixel accuracy 0.927.

关键词： Image segmentation computer vision Visualization Annotations Biopsy Pipelines Melanoma

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On the Application of Binary Neural Networks in Oblivious Inference

On the Application of Binary Neural Networks in Oblivious In...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Samragh, Mohammad Hussain, Siam Zhang, Xinqiao Huang, Ke Koushanfar, Farinaz Univ Calif San Diego La Jolla CA 92093 USA San Diego State Univ San Diego CA 92182 USA

ISBN: (纸本)9781665448994

This paper explores the application of Binary Neural Networks (BNN) in oblivious inference, a service provided by a server to mistrusting clients. Using this service, a client can obtain the inference result on her data by a trained model held by the server without disclosing the data or leaning the model parameters. We make two contributions to this field. First, we devise light-weight cryptographic protocols designed specifically to exploit the unique characteristics of BNNs. Second, we present dynamic exploration of the runtime -accuracy tradeoff of BNNs in a single -shot training process. While previous works trained multiple BNNs with different computational complexities (which is cumbersome due to the slow convergence of BNNs), we train a single BNN that can perform inference under different computational budgets. Compared to CryptFlow2, the state-of-the-art in oblivious inference of non binary DNNs, our approach reaches 2x faster inference at the same accuracy. Compared to XONN, the state-of-the-art in oblivious inference of binary networks, we achieve 2x to llx faster inference while obtaining higher accuracy.

关键词： Training computer vision Data analysis Computational modeling Face recognition Neural networks Data models

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