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Domain-Specific Batch Normalization for Unsupervised Domain Adaptation 32

Domain-Specific Batch Normalization for Unsupervised Domain ...

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

作者： Chang, Woong-Gi You, Tackgeun Seo, Seonguk Kwak, Suha Han, Bohyung Seoul Natl Univ Comp Vis Lab ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea POSTECH Comp Vis Lab CSE Pohang South Korea

ISBN: (纸本)9781728132938

We propose a novel unsupervised domain adaptation framework based on domain-specific batch normalization in deep neural networks. We aim to adapt to both domains by specializing batch normalization layers in convolutional neural networks while allowing them to share all other model parameters, which is realized by a two-stage algorithm. In the first stage, we estimate pseudo-labels for the examples in the target domain using an external unsupervised domain adaptation algorithm-for example, MSTN [17] or CPUA [14]-integrating the proposed domain-specific batch normalization. The second stage learns the final models using a multi-task classification loss for the source and target domains. Note that the two domains have separate batch normalization layers in both stages. Our framework can be easily incorporated into the domain adaptation techniques based on deep neural networks with batch normalization layers. We also present that our approach can be extended to the problem with multiple source domains. The proposed algorithm is evaluated on multiple benchmark datasets and achieves the state-of-the-art accuracy in the standard setting and the multi-source domain adaption scenario.

关键词： Categorization Deep Learning recognition: Detection Retrieval

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Towards Noiseless Object Contours for Weakly Supervised Semantic Segmentation

Towards Noiseless Object Contours for Weakly Supervised Sema...

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

作者： Li, Jing Fan, Junsong Zhang, Zhaoxiang Chinese Acad Sci CASIA Inst Automat Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China HKISI CAS Ctr Artificial Intelligence & Robot Beijing Peoples R China Natl Lab Pattern Recognit NLPR Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image-level label based weakly supervised semantic segmentation has attracted much attention since image labels are very easy to obtain. Existing methods usually generate pseudo labels from class activation map (CAM) and then train a segmentation model. CAM usually highlights partial objects and produce incomplete pseudo labels. Some methods explore object contour by training a contour model with CAM seed label supervision and then propagate CAM score from discriminative regions to non-discriminative regions with contour guidance. The propagation process suffers from the noisy intra-object contours, and inadequate propagation results produce incomplete pseudo labels. This is because the coarse CAM seed label lacks sufficient precise semantic information to suppress contour noise. In this paper, we train a SANCE model which utilizes an auxiliary segmentation module to supplement high-level semantic information for contour training by backbone feature sharing and online label supervision. The auxiliary segmentation module also provides more accurate localization map than CAM for pseudo label generation. We evaluate our approach on Pascal VOC 2012 and MS COCO 2014 benchmarks and achieve state-of-the-art performance, demonstrating the effectiveness of our method. The source code can be found at https://***/BraveGroup/SANCE

关键词： Training Location awareness Image segmentation computer vision Semantics Refining Predictive models

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Hyperspectral Image Reconstruction Using a Deep Spatial-Spectral Prior 32

Hyperspectral Image Reconstruction Using a Deep Spatial-Spec...

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

作者： Wang, Lizhi Sun, Chen Fu, Ying Kim, Min H. Huang, Hua Beijing Inst Technol Beijing Peoples R China Korea Adv Inst Sci & Technol Seoul South Korea

ISBN: (纸本)9781728132938

Regularization is a fundamental technique to solve an illposed optimizationproblem robustly and is essential to reconstruct compressive hyperspectralimages. Various handcrafted priors have been employed as a regularizer but are often insufficient to handle the wide variety of spectra of natural hyperspectral images, resulting in poor reconstruction quality. Moreover the prior-regularizedoptimization requires manual tweaking of its weight parameters to achieve a balance between the spatialand spectral fidelity of result images. In thispaper we presenta novel hyperspectralimage reconstructionalgorithm that substitutes the traditionalhand-craftedpriorwith a data-drivenprior, based on an optimization-inspirednetwork. Our method consists of two main parts: First, we learn a novel datadriven priorthat regularizes the optimizationproblem with a goal to boost the *** data-driven prior learns both local coherence and dynamic characteristics of natural hyperspectral images. Second, we combine our regularizerwith an optimization-inspirednetwork to overcome the heavy computation problem in the traditional iterative optimization methods. We learn the complete parameters in the network through end-to-end training, enabling robust performance with high accuracy. Extensive simulation and hardware experiments validate the superiorperformance of our method over the state-of-theartmethods.

关键词： Computational Photography Physics-based vision and Shape-from-X

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Source-Free Domain Adaptation via Distribution Estimation

Source-Free Domain Adaptation via Distribution Estimation

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

作者： Ding, Ning Xu, Yixing Tang, Yehui Xu, Chao Wang, Yunhe Tao, Dacheng Peking Univ Sch Artificial Intelligence Key Lab Machine Percept MOE Beijing Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China JD Explore Acad Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Domain Adaptation aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain whose data distributions are different. However, the training data in source domain required by most of the existing methods is usually unavailable in real-world applications due to privacy preserving policies. Recently, Source-Free Domain Adaptation (SFDA) has drawn much attention, which tries to tackle domain adaptation problem without using source data. In this work, we propose a novel framework called SFDA-DE to address SFDA task via source Distribution Estimation. Firstly, we produce robust pseudo-labels for target data with spherical k-means clustering, whose initial class centers are the weight vectors (anchors) learned by the classifier of pretrained model. Furthermore, we propose to estimate the class-conditioned feature distribution of source domain by exploiting target data and corresponding anchors. Finally, we sample surrogate features from the estimated distribution, which are then utilized to align two domains by minimizing a contrastive adaptation loss function. Extensive experiments show that the proposed method achieves state-of-the-art performance on multiple DA benchmarks, and even outperforms traditional DA methods which require plenty of source data.

关键词： Representation learning Data privacy computer vision Estimation Training data Benchmark testing Data models

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EDTER: Edge Detection with Transformer

EDTER: Edge Detection with Transformer

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

作者： Pu, Mengyang Huang, Yaping Liu, Yuming Guan, Qingji Ling, Haibin Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China Shenzhen Urban Transport Planning Ctr Co Ltd Shenzhen Peoples R China SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Convolutional neural networks have made significant progresses in edge detection by progressively exploring the context and semantic features. However, local details are gradually suppressed with the enlarging of receptive fields. Recently, vision transformer has shown excellent capability in capturing long-range dependencies. Inspired by this, we propose a novel transformer-based edge detector, Edge Detection TransformER (EDTLR), to extract clear and crisp object boundaries and meaningful edges by exploiting the full image context information and detailed local cues simultaneously. EDTER works in two stages. In Stage I, a global transformer encoder is used to capture long-range global context on coarse-grained image patches. Then in Stage II, a local transformer encoder works on fine-grained patches to excavate the short-range local cues. Each transformer encoder is followed by an elaborately designed Bi-directional Multi-Level Aggregation decoder to achieve high-resolution features. Finally, the global context and local cues are combined by a Feature Fusion Module and fed into a decision head for edge prediction. Extensive experiments on BSDS500, NYUDv2, and Multicue demonstrate the superiority of EDTER in comparison with state-of-the-arts.

关键词： computer vision Head Image edge detection Semantics Detectors Transformers Feature extraction

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TubeR: Tubelet Transformer for Video Action Detection

TubeR: Tubelet Transformer for Video Action Detection

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

作者： Zhao, Jiaojiao Zhang, Yanyi Li, Xinyu Chen, Hao Shuai, Bing Xu, Mingze Liu, Chunhui Kundu, Kaustav Xiong, Yuanjun Modolo, Davide Marsic, Ivan Snoek, Cees G. M. Tighe, Joseph Univ Amsterdam Amsterdam Netherlands Rutgers State Univ New Brunswick NJ USA AWS AI Labs Palo Alto CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose TubeR: a simple solution for spatio-temporal video action detection. Different from existing methods that depend on either an off-line actor detector or hand-designed actor-positional hypotheses like proposals or anchors, we propose to directly detect an action tubelet in a video by simultaneously performing action localization and recognition from a single representation. TubeR learns a set of tubelet-queries and utilizes a tubelet-attention module to model the dynamic spatio-temporal nature of a video clip, which effectively reinforces the model capacity compared to using actor-positional hypotheses in the spatio-temporal space. For videos containing transitional states or scene changes, we propose a context aware classification head to utilize short-term and long-term context to strengthen action classification, and an action switch regression head for detecting the precise temporal action extent. TubeR directly produces action tubelets with variable lengths and even maintains good results for long video clips. TubeR outperforms the previous state-of-the-art on commonly used action detection datasets AVA, UCF101-24 and JHMDB51-21. Code will be available on GluonCV(https://***/).

关键词： Location awareness Context-aware services computer vision Codes Switches Detectors Transformers

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Frame-wise Action Representations for Long Videos via Sequence Contrastive Learning

Frame-wise Action Representations for Long Videos via Sequen...

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

作者： Chen, Minghao Wei, Fangyun Li, Chong Cai, Deng Zhejiang Univ Coll Comp Sci State Key Lab CAD&CG Hangzhou Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Prior works on action representation learning mainly focus on designing various architectures to extract the global representations for short video clips. In contrast, many practical applications such as video alignment have strong demand for learning dense representations for long videos. In this paper, we introduce a novel contrastive action representation learning (CARL) framework to learn frame-wise action representations, especially for long videos, in a selfsupervised manner. Concretely, we introduce a simple yet efficient video encoder that considers spatio-temporal context to extract frame-wise representations. Inspired by the recent progress of self-supervised learning, we present a novel sequence contrastive loss (SCL) applied on two correlated views obtained through a series of spatio-temporal data augmentations. SCL optimizes the embedding space by minimizing the KL-divergence between the sequence similarity of two augmented views and a prior Gaussian distribution of timestamp distance. Experiments on FineGym, PennAction and Pouring datasets show that our method outperforms previous state-of-the-art by a large margin for downstream fine-grained action classification. Surprisingly, although without training on paired videos, our approach also shows outstanding performance on video alignment and fine-grained frame retrieval tasks. Code and models are available at https:// github. com/ minghchen/CARL_code.

关键词： Representation learning Training computer vision Codes Self-supervised learning Gaussian distribution pattern recognition

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VizWiz Grand Challenge: Answering Visual Questions from Blind People 31

VizWiz Grand Challenge: Answering Visual Questions from Blin...

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

作者： Gurari, Danna Li, Qing Stangl, Abigale J. Guo, Anhong Lin, Chi Grauman, Kristen Luo, Jiebo Bigham, Jeffrey P. Univ Texas Austin Austin TX 78712 USA Univ Sci & Technol China Hefei Anhui Peoples R China Univ Colorado Boulder Boulder CO USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781538664209

The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings. We propose VizWiz, the first goal-oriented VQA dataset arising from a natural VQA setting. VizWiz consists of over 31,000 visual questions originating from blind people who each took a picture using a mobile phone and recorded a spoken question about it, together with 10 crowdsourced answers per visual question. VizWiz differs from the many existing VQA datasets because (I) images are captured by blind photographers and so are often poor quality, (2) questions are spoken and so are more conversational, and (3) often visual questions cannot be answered. Evaluation of modern algorithms for answering visual questions and deciding if a visual question is answerable reveals that VizWiz is a challenging dataset. We introduce this dataset to encourage a larger community to develop more generalized algorithms that can assist blind people.

关键词： Visualization Blindness Prediction algorithms Lighting computer vision Mobile handsets Shape

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Learning Context Graph for Person Search 32

Learning Context Graph for Person Search

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

作者： Yan, Yichao Zhang, Qiang Ni, Bingbing Zhang, Wendong Xu, Minghao Yang, Xiaokang Shanghai Jiao Tong Univ Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Tencent YouTu Lab Shenzhen Guangdong Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781728132938

Person re-identification has achieved great progress with deep convolutional neural networks. However, most previous methods focus on learning individual appearance feature embedding, and it is hard for the models to handle difficult situations with different illumination, large pose variance and occlusion. In this work, we take a step further and consider employing context information for person search. For a probe-gallery pair, we first propose a contextual instance expansion module, which employs a relative attention module to search and filter useful context information in the scene. We also build a graph learning framework to effectively employ context pairs to update target similarity. These two modules are built on top of a joint detection and instance feature learning framework, which improves the discriminativeness of the learned features. The proposed framework achieves state-of-the-art performance on two widely used person search datasets.

关键词： Categorization recognition: Detection Retrieval Visual Reasoning

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MLSLT: Towards Multilingual Sign Language Translation

MLSLT: Towards Multilingual Sign Language Translation

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

作者： Yin, Aoxiong Zhao, Zhou Jin, Weike Zhang, Meng Zeng, Xingshan He, Xiaofei Zhejiang Univ Hangzhou Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Most of the research to date focuses on bilingual sign language translation (BSLT). However, such models are inefficient in building multilingual sign language translation systems. To solve this problem, we introduce the multilingual sign language translation (MSLT) task. It aims to use a single model to complete the translation between multiple sign languages and spoken languages. Then, we propose MLSLT, the first MSLT model, which contains two novel dynamic routing mechanisms for controlling the degree of parameter sharing between different languages. Intra-layer language-specific routing controls the proportion of data flowing through shared parameters and language-specific parameters from the token level through a soft gate within the layer, and inter-layer language-specific routing controls and learns the data flow path of different languages at the language level through a soft gate between layers. In order to evaluate the performance of MLSLT, we collect the first publicly available multilingual sign language understanding dataset, Spreadthesign-Ten (SP-10), which contains up to 100 language pairs, e.g., ***, ***. Experimental results show that the average performance of MLSLT outperforms the baseline MSLT model and the combination of multiple BSLT models in many cases. In addition, we also explore zero-shot translation in sign language and find that our model can achieve comparable performance to the supervised BSLT model on some language pairs. Dataset and more details are at https: // ***/.

关键词： computer vision Buildings Gesture recognition Assistive technologies Logic gates Routing Task analysis

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