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Self-Distillation from the Last Mini-Batch for Consistency Regularization

Self-Distillation from the Last Mini-Batch for Consistency R...

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

作者： Shen, Yiqing Xu, Liwu Yang, Yuzhe Li, Yaqian Guo, Yandong Shanghai Jiao Tong Univ Shanghai Peoples R China OPPO Res Inst Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Knowledge distillation (KD) shows a bright promise as a powerful regularization strategy to boost generalization ability by leveraging learned sample-level soft targets. Yet, employing a complex pre-trained teacher network or an ensemble of peer students in existing KD is both time-consuming and computationally costly. Various self KD methods have been proposed to achieve higher distillation efficiency. However, they either require extra network architecture modification or are difficult to parallelize. To cope with these challenges, we propose an efficient and reliable self-distillation framework, named Self-Distillation from Last Mini-Batch (DLB). Specifically, we rearrange the sequential sampling by constraining half of each mini-batch coinciding with the previous iteration. Meanwhile, the rest half will coincide with the upcoming iteration. Afterwards, the former half mini-batch distills on-the-fly soft targets generated in the previous iteration. Our proposed mechanism guides the training stability and consistency, resulting in robustness to label noise. Moreover, our method is easy to implement, without taking up extra run-time memory or requiring model structure modification. Experimental results on three classification benchmarks illustrate that our approach can consistently outperform state-of-the-art self-distillation approaches with different network architectures. Additionally, our method shows strong compatibility with augmentation strategies by gaining additional performance improvement. The code is available at https://***/Meta-knowledge-Lab/DLB.

关键词： Training computer vision Codes computer network reliability Memory management Network architecture Benchmark testing

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Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning 32

Heavy Rain Image Restoration: Integrating Physics Model and ...

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

作者： Li, Ruoteng Cheong, Loong-Fah Tan, Robby T. Natl Univ Singapore Singapore Singapore Yale NUS Coll Singapore Singapore

ISBN: (纸本)9781728132938

Most deraining works focus on rain streaks removal but they cannot deal adequately with heavy rain images. In heavy rain, streaks are strongly visible, dense rain accumulation or rain veiling effect significantly washes out the image, further scenes are relatively more blurry, etc. In this paper, we propose a novel method to address these problems. We put forth a 2-stage network: a physics-based backbone followed by a depth-guided GAN refinement. The first stage estimates the rain streaks, the transmission, and the atmospheric light governed by the underlying physics. To tease out these components more reliably, a guided filtering framework is used to decompose the image into its low-and high frequency components. This filtering is guided by a rain free residue image - its content is used to set the passbands for the two channels in a spatially-variant manner so that the background details do not get mixed up with the rain-streaks. For the second stage, the refinement stage, we put forth a depth-guided GAN to recover the background details failed to be retrieved by the first stage, as well as correcting artefacts introduced by that stage. We have evaluated our method against the state of the art methods. Extensive experiments show that our method outperforms them on real rain image data, recovering visually clean images with good details.

关键词： Image and Video Synthesis Low-level vision

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Deep Modular Co-Attention Networks for Visual Question Answering 32

Deep Modular Co-Attention Networks for Visual Question Answe...

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

作者： Yu, Zhou Yu, Jun Cui, Yuhao Tao, Dacheng Tian, Qi Hangzhou Dianzi Univ Sch Comp Sci & Technol Key Lab Complex Syst Modeling & Simulat Hangzhou Peoples R China Univ Sydney FEIT Sch Comp Sci UBTECH Sydney AI Ctr Sydney NSW Australia Huawei Noahs Ark Lab Shenzhen Peoples R China

ISBN: (纸本)9781728132938

Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective 'co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper;we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the question-guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-of-the-art. Our best single model delivers 70.63% overall accuracy on the test-dev set.

关键词： vision + Language Visual Reasoning

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Robust Instance Segmentation through Reasoning about Multi-Object Occlusion

Robust Instance Segmentation through Reasoning about Multi-O...

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

作者： Yuan, Xiaoding Kortylewski, Adam Sun, Yihong Yuille, Alan Tongji Univ Shanghai Peoples R China Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781665445092

Analyzing complex scenes with Deep Neural Networks is a challenging task, particularly when images contain multiple objects that partially occlude each other. Existing approaches to image analysis mostly process objects independently and do not take into account the relative occlusion of nearby objects. In this paper, we propose a deep network for multi-object instance segmentation that is robust to occlusion and can be trained from bounding box supervision only. Our work builds on Compositional Networks, which learn a generative model of neural feature activations to locate occluders and to classify objects based on their non-occluded parts. We extend their generative model to include multiple objects and introduce a framework for efficient inference in challenging occlusion scenarios. In particular, we obtain feed-forward predictions of the object classes and their instance and occluder segmentations. We introduce an Occlusion Reasoning Module (ORM) that locates erroneous segmentations and estimates the occlusion order to correct them. The improved segmentation masks are, in turn, integrated into the network in a top-down manner to improve the image classification. Our experiments on the KITTI INStance dataset (KINS) and a synthetic occlusion dataset demonstrate the effectiveness and robustness of our model at multi-object instance segmentation under occlusion. Code is publically available at https://***/XD7479/Multi-Object-Occlusion.

关键词： Deep learning Image segmentation computer vision Codes computer architecture Cognition Robustness

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TIM: A Time Interval Machine for Audio-Visual Action recognition

TIM: A Time Interval Machine for Audio-Visual Action Recogni...

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

作者： Chalk, Jacob Huh, Jaesung Kazakos, Evangelos Zisserman, Andrew Damen, Dima Univ Bristol Bristol Avon England Univ Oxford VGG Oxford England Czech Tech Univ Prague Czech Republic

ISBN: (纸本)9798350353006

Diverse actions give rise to rich audio-visual signals in long videos. Recent works showcase that the two modalities of audio and video exhibit different temporal extents of events and distinct labels. We address the interplay between the two modalities in long videos by explicitly modelling the temporal extents of audio and visual events. We propose the Time Interval Machine (TIM) where a modality-specific time interval poses as a query to a transformer encoder that ingests a long video input. The encoder then attends to the specified interval, as well as the surrounding context in both modalities, in order to recognise the ongoing action. We test TIM on three long audio-visual video datasets: EPIC-KITCHENS, Perception Test, and AVE, reporting state-of-the-art (SOTA) for recognition. On EPIC-KITCHENS, we beat previous SOTA that utilises LLMs and significantly larger pre- training by 2.9% top-1 action recognition accuracy. Additionally, we show that TIM can be adapted for action detection, using dense multi-scale interval queries, outperforming SOTA on EPIC-KITCHENS-100 for most metrics, and showing strong performance on the Perception Test. Our ablations show the critical role of integrating the two modalities and modelling their time intervals in achieving this performance. Code and models at: https://***/JacobChalk/TIM.

关键词： action detection action recognition audio-visual learning egocentric videos video understanding

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360MonoDepth: High-Resolution 360° Monocular Depth Estimation

360MonoDepth: High-Resolution 360° Monocular Depth Estimati...

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

作者： Rey-Area, Manuel Yuan, Mingze Richardt, Christian Univ Bath Bath Avon England

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

360 degrees cameras can capture complete environments in a single shot, which makes 360 degrees imagery alluring in many computer vision tasks. However, monocular depth estimation remains a challenge for 360 degrees data, particularly for high resolutions like 2K (2048x 1024) and beyond that are important for novel-view synthesis and virtual reality applications. Current CNN-based methods do not support such high resolutions due to limited GPU memory. In this work, we propose a flexible framework for monocular depth estimation from high-resolution 360 degrees images using tangent images. We project the 360 degrees input image onto a set of tangent planes that produce perspective views, which are suitable for the latest, most accurate state-of-the-art perspective monocular depth estimators. To achieve globally consistent disparity estimates, we recombine the individual depth estimates using deformable multi-scale alignment followed by gradient-domain blending. The result is a dense, high-resolution 360 degrees depth map with a high level of detail, also for outdoor scenes which are not supported by existing methods. Our source code and data are available at https://***/360monodepth/.

关键词： computer vision Image resolution Three-dimensional displays Codes Estimation Graphics processing units Virtual reality

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MS²DG-Net: Progressive Correspondence Learning via Multiple Sparse Semantics Dynamic Graph

MS<SUP>2</SUP>DG-Net: Progressive Correspondence Learning vi...

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

作者： Dai, Luanyuan Liu, Yizhang Ma, Jiayi Wei, Lifang Lai, Taotao Yang, Changcai Chen, Riqing Fujian Agr & Forestry Univ Fuzhou Peoples R China Tongji Univ Shanghai Peoples R China Wuhan Univ Wuhan Peoples R China Minjiang Univ Fuzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Establishing superior-quality correspondences in an image pair is pivotal to many subsequent computer vision tasks. Using Euclidean distance between correspondences to find neighbors and extract local information is a common strategy in previous works. However, most such works ignore similar sparse semantics information between two given images and cannot capture local topology among correspondences well. Therefore, to deal with the above problems, Multiple Sparse Semantics Dynamic Graph Network (MS(2)DG-Net) is proposed, in this paper, to predict probabilities of correspondences as inliers and recover camera poses. MS(2)DG-Net dynamically builds sparse semantics graphs based on sparse semantics similarity between two given images, to capture local topology among correspondences, while maintaining permutation-equivariant. Extensive experiments prove that MS(2)DG-Net outperforms stateof-the-art methods in outlier removal and camera pose estimation tasks on the public datasets with heavy outliers.

关键词： computer vision Network topology Semantics Pose estimation Machine learning Euclidean distance Cameras

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CLIP-Event: Connecting Text and Images with Event Structures

CLIP-Event: Connecting Text and Images with Event Structures

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

作者： Li, Manling Xu, Ruochen Wang, Shuohang Zhou, Luowei Lin, Xudong Zhu, Chenguang Zeng, Michael Ji, Heng Chang, Shih-Fu Univ Illinois Champaign IL 61820 USA Microsoft Res Redmond WA USA Columbia Univ New York NY 10027 USA Microsoft Redmond WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text. While existing vision-language pretraining models primarily focus on understanding objects in images or entities in text, they often ignore the alignment at the level of events and their argument structures. In this work, we propose a contrastive learning framework to enforce vision-language pretraining models to comprehend events and associated argument (participant) roles. To achieve this, we take advantage of text information extraction technologies to obtain event structural knowledge, and utilize multiple prompt functions to contrast difficult negative descriptions by manipulating event structures. We also design an event graph alignment loss based on optimal transport to capture event argument structures. In addition, we collect a large event-rich dataset (106,875 images) for pretraining, which provides a more challenging image retrieval benchmark to assess the understanding of complicated lengthy sentences(1). Experiments show that our zero-shot CLIP-Event outperforms the state-of-the-art supervised model in argument extraction on Multimedia Event Extraction, achieving more than 5% absolute F-score gain in event extraction, as well as significant improvements on a variety of downstream tasks under zero-shot settings.

关键词： computer vision Codes Computational modeling Image retrieval Benchmark testing Information retrieval pattern recognition

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CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching

CFNet: Cascade and Fused Cost Volume for Robust Stereo Match...

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

作者： Shen, Zhelun Dai, Yuchao Rao, Zhibo Northwestern Polytech Univ Xian Peoples R China Peking Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

Recently, the ever-increasing capacity of large-scale annotated datasets has led to profound progress in stereo matching. However, most of these successes are limited to a specific dataset and cannot generalize well to other datasets. The main difficulties lie in the large domain differences and unbalanced disparity distribution across a variety of datasets, which greatly limit the real-world applicability of current deep stereo matching models. In this paper, we propose CFNet, a Cascade and Fused cost volume based network to improve the robustness of the stereo matching network. First, we propose a fused cost volume representation to deal with the large domain difference. By fusing multiple low-resolution dense cost volumes to enlarge the receptive field, we can extract robust structural representations for initial disparity estimation. Second, we propose a cascade cost volume representation to alleviate the unbalanced disparity distribution. Specifically, we employ a variance-based uncertainty estimation to adaptively adjust the next stage disparity search space, in this way driving the network progressively prune out the space of unlikely correspondences. By iteratively narrowing down the disparity search space and improving the cost volume resolution, the disparity estimation is gradually refined in a coarse-to-fine manner. When trained on the same training images and evaluated on KITTI, ETH3D, and Middlebury datasets with the fixed model parameters and hyperparameters, our proposed method achieves the state-of-the-art overall performance and obtains the 1st place on the stereo task of Robust vision Challenge 2020.

关键词： Training computer vision Costs Uncertainty Image resolution Codes Estimation

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SimMatch: Semi-supervised Learning with Similarity Matching

SimMatch: Semi-supervised Learning with Similarity Matching

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

作者： Zheng, Mingkai You, Shan Huang, Lang Wang, Fei Qian, Chen Xu, Chang Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia SenseTime Res Hong Kong Peoples R China Univ Tokyo Tokyo Japan Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community. In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simultaneously considers semantic similarity and instance similarity. In SimMatch, the consistency regularization will be applied on both semantic-level and instance-level. The different augmented views of the same instance are encouraged to have the same class prediction and similar similarity relationship respected to other instances. Next, we instantiated a labeled memory buffer to fully leverage the ground truth labels on instance-level and bridge the gaps between the semantic and instance similarities. Finally, we proposed the unfolding and aggregation operation which allows these two similarities be isomorphically transformed with each other. In this way, the semantic and instance pseudo-labels can be mutually propagated to generate more high-quality and reliable matching targets. Extensive experimental results demonstrate that SimMatch improves the performance of semi-supervised learning tasks across different benchmark datasets and different settings. Notably, with 400 epochs of training, SimMatch achieves 67.2%, and 74.4% Top-1 Accuracy with 1% and 10% labeled examples on ImageNet, which significantly outperforms the baseline methods and is better than previous semi-supervised learning frameworks.

关键词： Training computer vision Annotations Semantics Machine learning Semisupervised learning Benchmark testing

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