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Adaptive Consistency Regularization for Semi-Supervised Transfer Learning

Adaptive Consistency Regularization for Semi-Supervised Tran...

引用

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

作者： Abuduweili, Abulikemu Li, Xingjian Shi, Humphrey Xu, Cheng-Zhong Dou, Dejing Baidu Res Big Data Lab Beijing Peoples R China Univ Oregon SHI Lab Eugene OR 97403 USA Univ Macau Dept Comp Sci State Key Lab IOTSC Taipa Macao Peoples R China

ISBN: (纸本)9781665445092

While recent studies on semi-supervised learning have shown remarkable progress in leveraging both labeled and unlabeled data, most of them presume a basic setting of the model is randomly initialized. In this work, we consider semi-supervised learning and transfer learning jointly, leading to a more practical and competitive paradigm that can utilize both powerful pre-trained models from source domain as well as labeled/unlabeled data in the target domain. To better exploit the value of both pre-trained weights and unlabeled target examples, we introduce adaptive consistency regularization that consists of two complementary components: Adaptive Knowledge Consistency (AKC) on the examples between the source and target model, and Adaptive Representation Consistency (ARC) on the target model between labeled and unlabeled examples. Examples involved in the consistency regularization are adaptively selected according to their potential contributions to the target task. We conduct extensive experiments on popular benchmarks including CIFAR-10, CUB-200, and MURA, by fine-tuning the ImageNet pre-trained ResNet-50 model. Results show that our proposed adaptive consistency regularization outperforms state-of-the-art semi-supervised learning techniques such as Pseudo Label, Mean Teacher, and FixMatch. Moreover, our algorithm is orthogonal to existing methods and thus able to gain additional improvements on top of MixMatch and Fix Match. Our code is available at https://***/Walleclipse/SemiSupervised-Transfer-Learning-Paddle.

关键词： Adaptation models computer vision Codes Transfer learning Supervised learning Semisupervised learning Benchmark testing

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FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong vision-Language models

FFF: Fixing Flawed Foundations in contrastive pre-training r...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Adrian Bulat Yassine Ouali Georgios Tzimiropoulos Samsung AI Center Cambridge UK Technical University of Iasi Romania Queen Mary University of London UK

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Despite noise and caption quality having been acknowledged as important factors impacting vision-language contrastive pre-training, in this paper, we show that the full potential of improving the training process by addressing such issues is yet to be realized. Specifically, we firstly study and analyze two issues affecting training: incorrect assignment of negative pairs, and low caption quality and diversity. Then, we devise effective solutions for addressing both problems, which essentially require training with multiple true positive pairs. Finally, we propose training with sigmoid loss to address such a requirement. We show very large gains over the current state-of-the-art for both image recognition (~ +6% on average over 11 datasets) and image retrieval (~ + 19% on Flickr30k and ~ + 15% on MSCOCO).

关键词： Training computer vision Image recognition Noise Image retrieval Field-flow fractionation pattern recognition

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Structured Scene Memory for vision-Language Navigation

Structured Scene Memory for Vision-Language Navigation

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

作者： Wang, Hanqing Wang, Wenguan Liang, Wei Xiong, Caiming Shen, Jianbing Beijing Inst Technol Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland Salesforce Res San Francisco CA USA Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i.e., entailing an agent to navigate 3D environments through following linguistic instructions. However, current VLN agents simply store their past experiences/observations as latent states in recurrent networks, failing to capture environment layouts and make long-term planning. To address these limitations, we propose a crucial architecture, called Structured Scene Memory (SSM). It is compartmentalized enough to accurately memorize the percepts during navigation. It also serves as a structured scene representation, which captures and disentangles visual and geometric cues in the environment. SSM has a collect-read controller that adaptively collects information for supporting current decision making and mimics iterative algorithms for long-range reasoning. As SSM provides a complete action space, i.e., all the navigable places on the map, a frontier-exploration based navigation decision making strategy is introduced to enable efficient and global planning. Experiment results on two VLN datasets (i.e., R2R and R4R) show that our method achieves state-of-the-art performance on several metrics.

关键词： Visualization Three-dimensional displays Recurrent neural networks Navigation Decision making Layout Memory architecture

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MR Image Super-Resolution with Squeeze and Excitation Reasoning Attention Network

MR Image Super-Resolution with Squeeze and Excitation Reason...

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

作者： Zhang, Yulun Li, Kai Li, Kunpeng Fu, Yun Northeastern Univ Dept ECE Boston MA 02115 USA Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA

ISBN: (纸本)9781665445092

High-quality high-resolution (HR) magnetic resonance (MR) images afford more detailed information for reliable diagnosis and quantitative image analyses. Deep convolutional neural networks (CNNs) have shown promising ability for MR image super-resolution (SR) given low-resolution (LR) MR images. The LR MR images usually share some visual characteristics: repeating patterns, relatively simpler structures, and less informative background. Most previous CNN-based SR methods treat the spatial pixels (including the background) equally. They also fail to sense the entire space of the input, which is critical for high-quality MR image SR. To address those problems, we propose squeeze and excitation reasoning attention networks (SERAN) for accurate MR image SR. We propose to squeeze attention from global spatial information of the input and obtain global descriptors. Such global descriptors enhance the network's ability to focus on more informative regions and structures in MR images. We further build relationship among those global descriptors and propose primitive relationship reasoning attention. The global descriptors are further refined with learned attention. To fully make use of the aggregated information, we adaptively recalibrate feature responses with learned adaptive attention vectors. These attention vectors select a subset of global descriptors to complement each spatial location for accurate details and texture reconstruction. We propose squeeze and excitation attention with residual scaling, which not only stabilizes the training but also makes it flexible to other basic networks. Extensive experiments show the effectiveness of our proposed SERAN, which clearly surpasses state-of-the-art methods on benchmarks quantitatively and visually.

关键词： Training Visualization computer vision Adaptation models Computational modeling Superresolution Cognition

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Semantic Line Combination Detector

Semantic Line Combination Detector

引用

conference on computer vision and pattern recognition (CVPR)

作者： Jinwon Ko Dongkwon Jin Chang-Su Kim Korea University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

A novel algorithm, called semantic line combination detector (SLCD), to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. First, we generate various line combinations from reliable lines. Second, we estimate the score of each line combination and determine the best one. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets. More-over, it is shown that SLCD can be applied effectively to three vision tasks of vanishing point detection, symmetry axis detection, and composition-based image retrieval. Our codes are available at https://***/Jinwon-Ko/SLCD.

关键词： computer vision Codes Semantics Image retrieval Detectors pattern recognition Reliability

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ED-DCFNet: an unsupervised encoder-decoder neural model for event-driven feature extraction and object tracking

ED-DCFNet: an unsupervised encoder-decoder neural model for ...

引用

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

作者： Raz Ramon Hadar Cohen-Duwek Elishai Ezra Tsur Department of Mathematics and Computer Science Neuro-Biomorphic Engineering Lab (NBEL) The Open University of Israel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neuromorphic cameras feature asynchronous event-based pixel-level processing and are particularly useful for object tracking in dynamic environments. Current approaches for feature extraction and optical flow with high-performing hybrid RGB-events vision systems require large computational models and supervised learning, which impose challenges for embedded vision and require annotated datasets. In this work, we propose ED-DCFNet, a small and efficient (< 72k) unsupervised multi-domain learning framework, which extracts events-frames shared features without requiring annotations, with comparable performance. Furthermore, we introduce an open-sourced event and frame-based dataset that captures indoor scenes with various lighting and motion-type conditions in realistic scenarios, which can be used for model building and evaluation. The dataset is available at https://***/NBELab/UnsupervisedTracking.

关键词： Training Neuromorphics Computational modeling Machine vision Supervised learning Feature extraction pattern recognition

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Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association

Seeking the Shape of Sound: An Adaptive Framework for Learni...

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

作者： Wen, Peisong Xu, Qianqian Jiang, Yangbangyan Yang, Zhiyong He, Yuan Huang, Qingming Chinese Acad Sci Inst Comput Tech Key Lab Intel Info Proc Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Tech Beijing Peoples R China Chinese Acad Sci Inst Info Engn State Key Lab Info Secur SKLOIS Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Alibaba Grp Beijing Peoples R China Univ Chinese Acad Sci BDKM Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Nowadays, we have witnessed the early progress on learning the association between voice and face automatically, which brings a new wave of studies to the computer vision community. However, most of the prior arts along this line (a) merely adopt local information to perform modality alignment and (b) ignore the diversity of learning difficulty across different subjects. In this paper, we propose a novel framework to jointly address the above-mentioned issues. Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered. Compared with the existing methods, we introduce a global loss into the modality alignment process. The global component of the loss is driven by the identity classification. Theoretically, we show that minimizing the loss could maximize the distance between embeddings across different identities while minimizing the distance between embeddings belonging to the same identity, in a global sense (instead of a mini-batch). Targeting at (b), we propose a dynamic reweighting scheme to better explore the hard but valuable identities while filtering out the unlearnable identities. Experiments show that the proposed method outperforms the previous methods in multiple settings, including voice-face matching, verification and retrieval.

关键词： computer vision Matched filters Art Shape Filtering Face recognition pattern matching

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Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report

Real-Time Quantized Image Super-Resolution on Mobile NPUs, M...

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

作者： Ignatov, Andrey Timofte, Radu Denna, Maurizio Younes, Abdel Lek, Andrew Ayazoglu, Mustafa Liu, Jie Du, Zongcai Guo, Jiaming Zhou, Xueyi Jia, Hao Yan, Youliang Zhang, Zexin Chen, Yixin Peng, Yunbo Lin, Yue Zhang, Xindong Zeng, Hui Zeng, Kun Li, Peirong Liu, Zhihuang Xue, Shiqi Wang, Shengpeng Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Synapt Europe Lausanne Switzerland Synapt HQ San Jose CA USA AI Witchlabs Zurich Switzerland Aselsan Corp Ankara Turkey Nanjing Univ Nanjing Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Netease Games AI Lab Beijing Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Minjiang Univ Fuzhou Peoples R China

ISBN: (纸本)9781665448994

Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices. While many solutions have been proposed for this task, they are usually not optimized even for common smartphone AI hardware, not to mention more constrained smart TV platforms that are often supporting INT8 inference only. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image super-resolution solutions that can demonstrate a real-time performance on mobile or edge NPUs. For this, the participants were provided with the DIV2K dataset and trained quantized models to do an efficient 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated NPU capable of accelerating quantized neural networks. The proposed solutions are fully compatible with all major mobile AI accelerators and are capable of reconstructing Full HD images under 40-60 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.

关键词： computer vision Runtime Smart TV Superresolution Neural networks Smart homes Real-time systems

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Learning a Self-Expressive Network for Subspace Clustering

Learning a Self-Expressive Network for Subspace Clustering

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

作者： Zhang, Shangzhi You, Chong Vidal, Rene Li, Chun-Guang Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA Johns Hopkins Univ Math Inst Data Sci Baltimore MD USA

ISBN: (纸本)9781665445092

State-of-the-art subspace clustering methods are based on the self-expressive model, which represents each data point as a linear combination of other data points. However, such methods are designed for a finite sample dataset and lack the ability to generalize to out-of-sample data. Moreover, since the number of self-expressive coefficients grows quadratically with the number of data points, their ability to handle large-scale datasets is often limited. In this paper, we propose a novel framework for subspace clustering, termed Self-Expressive Network (SENet), which employs a properly designed neural network to learn a self-expressive representation of the data. We show that our SENet can not only learn the self-expressive coefficients with desired properties on the training data, but also handle out-of-sample data. Besides, we show that SENet can also be leveraged to perform subspace clustering on large-scale datasets. Extensive experiments conducted on synthetic data and real world benchmark data validate the effectiveness of the proposed method. In particular, SENet yields highly competitive performance on MNIST, Fashion MNIST and Extended MNIST and state-of-the-art performance on CIFAR-10.

关键词： Training computer vision Computational modeling Clustering methods Neural networks Training data Stochastic processes

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The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in computer vision Models

The Lottery Tickets Hypothesis for Supervised and Self-super...

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

作者： Chen, Tianlong Frankle, Jonathan Chang, Shiyu Liu, Sijia Zhang, Yang Carbin, Michael Wang, Zhangyang Univ Texas Austin Austin TX 78712 USA MIT CSAIL Cambridge MA USA MIT IBM Watson AI Lab Cambridge MA USA Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781665445092

The computer vision world has been re-gaining enthusiasm in various pre-trained models, including both classical ImageNet supervised pre-training and recently emerged self-supervised pre-training such as simCLR [10] and MoCo [40]. Pre-trained weights often boost a wide range of downstream tasks including classification, detection, and segmentation. Latest studies suggest that pre-training benefits from gigantic model capacity [11]. We are hereby curious and ask: after pre-training, does a pre-trained model indeed have to stay large for its downstream transferability? In this paper, we examine supervised and self-supervised pre-trained models through the lens of the lottery ticket hypothesis (LTH) [31]. LTH identifies highly sparse matching subnetworks that can be trained in isolation from (nearly) scratch yet still reach the full models' performance. We extend the scope of LTH and question whether matching subnetworks still exist in pre-trained computer vision models, that enjoy the same downstream transfer performance. Our extensive experiments convey an overall positive message: from all pre-trained weights obtained by ImageNet classification, simCLR, and MoCo, we are consistently able to locate such matching subnetworks at 59.04% to 96.48% sparsity that transfer universally to multiple downstream tasks, whose performance see no degradation compared to using full pre-trained weights. Further analyses reveal that subnetworks found from different pre-training tend to yield diverse mask structures and perturbation sensitivities. We conclude that the core LTH observations remain generally relevant in the pre-training paradigm of computer vision, but more delicate discussions are needed in some cases. Codes and pre-trained models will be made available at: https://***/VITA-Group/CV_LTH_Pre-training.

关键词： Degradation computer vision Image segmentation Sensitivity Computational modeling Perturbation methods pattern recognition

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