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Triple-cooperative Video Shadow Detection

Triple-cooperative Video Shadow Detection

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

作者： Chen, Zhihao Wan, Liang Zhu, Lei Shen, Jia Fu, Huazhu Liu, Wennan Qin, Jing Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Univ Cambridge Dept Appl Math & Theoret Phys Cambridge England Inception Inst Artificial Intelligence Abu Dhabi U Arab Emirates Tianjin Univ Acad Med Engn & Translat Med Tianjin Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Shadow detection in a single image has received significant research interests in recent years. However, much fewer works have been explored in shadow detection over dynamic scenes. The bottleneck is the lack of a well-established dataset with high-quality annotations for video shadow detection. In this work, we collect a new video shadow detection dataset (ViSha), which contains 120 videos with 11;685 frames, covering 60 object categories, varying lengths, and different motion/lighting conditions. All the frames are annotated with a high-quality pixel-level shadow mask. To the best of our knowledge, this is the first learning-oriented dataset for video shadow detection. Furthermore, we develop a new baseline model, named triple-cooperative video shadow detection network (TVSD-Net). It utilizes triple parallel networks in a cooperative manner to learn discriminative representations at intra-video and inter-video levels. Within the network, a dual gated co-attention module is proposed to constrain features from neighboring frames in the same video, while an auxiliary similarity loss is introduced to mine semantic information between different videos. Finally, we conduct a comprehensive study on ViSha, evaluating 12 state-of-the-art models (including single image shadow detectors, video object segmentation, and saliency detection methods). Experiments demonstrate that our model outperforms SOTA competitors.

关键词： computer vision Annotations Semantics Dynamics Object segmentation Detectors Logic gates

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Learning Fine-Grained Segmentation of 3D Shapes without Part Labels

Learning Fine-Grained Segmentation of 3D Shapes without Part...

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

作者： Wang, Xiaogang Sun, Xun Cao, Xinyu Xu, Kai Zhou, Bin Southwest Univ Chongqing Peoples R China Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9781665445092

Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags. This assumption, however, is impractical for learning fine-grained segmentation. Although most off-the-shelf CAD models are, by construction, composed of fine-grained parts, they usually miss semantic tags and labeling those fine-grained parts is extremely tedious. We approach the problem with deep clustering, where the key idea is to learn part priors from a shape dataset with fine-grained segmentation but no part labels. Given point sampled 3D shapes, we model the clustering priors of points with a similarity matrix and achieve part segmentation through minimizing a novel low rank loss. To handle highly densely sampled point sets, we adopt a divide-and-conquer strategy. We partition the large point set into a number of blocks. Each block is segmented using a deep-clustering-based part prior network trained in a category-agnostic manner. We then train a graph convolution network to merge the segments of all blocks to form the final segmentation result. Our method is evaluated with a challenging benchmark of fine-grained segmentation, showing state-of-the-art performance.

关键词： Training Solid modeling Three-dimensional displays Shape Semantics Graph neural networks pattern recognition

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Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks

Neural Parts: Learning Expressive 3D Shape Abstractions with...

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

作者： Paschalidou, Despoina Katharopoulos, Angelos Geiger, Andreas Fidler, Sanja Max Planck Inst Intelligent Syst Tubingen Tubingen Germany Univ Tubingen Tubingen Germany Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland Max Planck ETH Ctr Learning Syst Zurich Switzerland NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simplicity of existing primitive representations, these methods fail to accurately reconstruct 3D shapes using a small number of primitives/parts. We address the trade-off between reconstruction quality and number of parts with Neural Parts, a novel 3D primitive representation that defines primitives using an Invertible Neural Network (INN) which implements homeomorphic mappings between a sphere and the target object. The INN allows us to compute the inverse mapping of the homeomorphism, which in turn, enables the efficient computation of both the implicit surface function of a primitive and its mesh, without any additional post-processing. Our model learns to parse 3D objects into semantically consistent part arrangements without any part-level supervision. Evaluations on ShapeNet, D-FAUST and FreiHAND demonstrate that our primitives can capture complex geometries and thus simultaneously achieve geometrically accurate as well as interpretable reconstructions using an order of magnitude fewer primitives than state-of-the-art shape abstraction methods.

关键词： Geometry Solid modeling computer vision Three-dimensional displays Shape Computational modeling Neural networks

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STMTrack: Template-free Visual Tracking with Space-time Memory Networks

STMTrack: Template-free Visual Tracking with Space-time Memo...

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

作者： Fu, Zhihong Liu, Qingjie Fu, Zehua Wang, Yunhong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9781665445092

Boosting performance of the offline trained siamese trackers is getting harder nowadays since the fixed information of the template cropped from the first frame has been almost thoroughly mined, but they are poorly capable of resisting target appearance changes. Existing trackers with template updating mechanisms rely on time-consuming numerical optimization and complex hand-designed strategies to achieve competitive performance, hindering them from real-time tracking and practical applications. In this paper, we propose a novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to appearance variations during tracking. Specifically, a novel memory mechanism is introduced, which stores the historical information of the target to guide the tracker to focus on the most informative regions in the current frame. Furthermore, the pixel-level similarity computation of the memory network enables our tracker to generate much more accurate bounding boxes of the target. Extensive experiments and comparisons with many competitive trackers on challenging large-scale benchmarks, OTB-2015, TrackingNet, GOT-10k, LaSOT, UAV123, and VOT2018, show that, without bells and whistles, our tracker outperforms all previous state-of-the-art real-time methods while running at 37 FPS.

关键词： Visualization computer vision Target tracking Codes Adaptive systems Benchmark testing Boosting

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DeepACG: Co-Saliency Detection via Semantic-aware Contrast Gromov-Wasserstein Distance

DeepACG: Co-Saliency Detection via Semantic-aware Contrast G...

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

作者： Zhang, Kaihua Dong, Mingliang Liu, Bo Yuan, Xiao-Tong Liu, Qingshan Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat Nanjing Peoples R China JD Digits Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

The objective of co-saliency detection is to segment the co-occurring salient objects in a group of images. To address this task, we introduce a new deep network architecture via semantic-aware contrast Gromov-Wasserstein distance (DeepACG). We first adopt the Gromov-Wasserstein (GW) distance to build dense 4D correlation volumes for all pairs of image pixels within the image group. These dense correlation volumes enable the network to accurately discover the structured pair-wise pixel similarities among the common salient objects. Second, we develop a semantic-aware co-attention module (SCAM) to enhance the foreground co-saliency through predicted categorical information. Specifically, SCAM recognizes the semantic class of the foreground co-objects, and this information is then modulated to the deep representations to localize the related pixels. Third, we design a contrast edge-enhanced module (EEM) to capture richer contexts and preserve fine-grained spatial information. We validate the effectiveness of our model using three largest and most challenging benchmark datasets (Cosal2015, CoCA, and CoSOD3k). Extensive experiments have demonstrated the substantial practical merit of each module. Compared with the existing works, DeepACG shows significant improvements and achieves state-of-the-art performance.

关键词： Image segmentation computer vision Correlation Image edge detection Semantics Network architecture Benchmark testing

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Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation

Continual Domain Adaptation through Pruning-aided Domain-spe...

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

作者： Prasanna B Sunandini Sanyal R. Venkatesh Babu Vision and AI Lab Indian Institute of Science Bengaluru

In this paper, we propose to develop a method to address unsupervised domain adaptation (UDA) in a practical setting of continual learning (CL). The goal is to update the model on continually changing domains while preserving domain-specific knowledge to prevent catastrophic forgetting of past-seen domains. To this end, we build a framework for preserving domain-specific features utilizing the inherent model capacity via pruning. We also perform effective inference using a novel batch-norm based metric to predict the final model parameters to be used accurately. Our approach achieves not only state-of-the-art performance but also prevents catastrophic forgetting of past domains significantly. Our code is made publicly available. 1 .

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PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths

PMP-Net: Point Cloud Completion by Learning Multi-step Point...

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

作者： Wen, Xin Xiang, Peng Han, Zhizhong Cao, Yan-Pei Wan, Pengfei Zheng, Wen Liu, Yu-Shen Tsinghua Univ Sch Software BNRist Beijing Peoples R China Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Kuaishou Technol Y Tech Beijing Peoples R China

ISBN: (纸本)9781665445092

The task of point cloud completion aims to predict the missing part for an incomplete 3D shape. A widely used strategy is to generate a complete point cloud from the incomplete one. However, the unordered nature of point clouds will degrade the generation of high-quality 3D shapes, as the detailed topology and structure of discrete points are hard to be captured by the generative process only using a latent code. In this paper, we address the above problem by reconsidering the completion task from a new perspective, where we formulate the prediction as a point cloud deformation process. Specifically, we design a novel neural network, named PMP-Net, to mimic the behavior of an earth mover. It moves move each point of the incomplete input to complete the point cloud, where the total distance of point moving paths (PMP) should be shortest. Therefore, PMP-Net predicts a unique point moving path for each point according to the constraint of total point moving distances. As a result, the network learns a strict and unique correspondence on point-level, and thus improves the quality of the predicted complete shape. We conduct comprehensive experiments on Completion3D and PCN datasets, which demonstrate our advantages over the state-of-the-art point cloud completion methods. Code will be available at https://***/diviswen/PMP-Net.

关键词： Earth computer vision Three-dimensional displays Codes Shape Neural networks Topology

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Delving into Localization Errors for Monocular 3D Object Detection

Delving into Localization Errors for Monocular 3D Object Det...

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

作者： Ma, Xinzhu Zhang, Yinmin Xu, Dan Zhou, Dongzhan Yi, Shuai Li, Haojie Ouyang, Wanli Univ Sydney Sydney NSW Australia Hong Kong Univ Sci & Technol Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Dalian Univ Technol Dalian Peoples R China

ISBN: (纸本)9781665445092

Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging. In this work, by intensive diagnosis experiments, we quantify the impact introduced by each sub-task and found the 'localization error' is the vital factor in restricting monocular 3D detection. Besides, we also investigate the underlying reasons behind localization errors, analyze the issues they might bring, and propose three strategies. First, we revisit the misalignment between the center of the 2D bounding box and the projected center of the 3D object, which is a vital factor leading to low localization accuracy. Second, we observe that accurately localizing distant objects with existing technologies is almost impossible, while those samples will mislead the learned network. To this end, we propose to remove such samples from the training set for improving the overall performance of the detector. Lastly, we also propose a novel 3D IoU oriented loss for the size estimation of the object, which is not affected by 'localization error'. We conduct extensive experiments on the KITTI dataset, where the proposed method achieves real-time detection and outperforms previous methods by a large margin. The code will be made available at: https://***/xinzhuma/monodle.

关键词： Location awareness Training computer vision Three-dimensional displays Estimation Object detection Detectors

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ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

ClassSR: A General Framework to Accelerate Super-Resolution ...

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

作者： Kong, Xiangtao Zhao, Hengyuan Qiao, Yu Dong, Chao Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synergy Syst Shenzhen Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China

ISBN: (纸本)9781665445092

We aim at accelerating super-resolution (SR) networks on large images (2K-8K). The large images are usually decomposed into small sub-images in practical usages. Based on this processing, we found that different image regions have different restoration difficulties and can be processed by networks with different capacities. Intuitively, smooth areas are easier to super-solve than complex textures. To utilize this property, we can adopt appropriate SR networks to process different sub-images after the decomposition. On this basis, we propose a new solution pipeline - ClassSR that combines classification and SR in a unified framework. In particular, it first uses a Class-Module to classify the sub-images into different classes according to restoration difficulties, then applies an SR-Module to perform SR for different classes. The Class-Module is a conventional classification network, while the SR-Module is a network container that consists of the to-be-accelerated SR network and its simplified versions. We further introduce a new classification method with two losses - Class-Loss and Average-Loss to produce the classification results. After joint training, a majority of sub-images will pass through smaller networks, thus the computational cost can be significantly reduced. Experiments show that our ClassSR can help most existing methods (e.g., FSRCNN, CARN, SRResNet, RCAN) save up to 50% FLOPs on DIV8K datasets. This general framework can also be applied in other low-level vision tasks.

关键词： Training computer vision Superresolution Pipelines Containers Image restoration pattern recognition

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Prototype Completion with Primitive Knowledge for Few-Shot Learning

Prototype Completion with Primitive Knowledge for Few-Shot L...

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

作者： Zhang, Baoquan Li, Xutao Ye, Yunming Huang, Zhichao Zhang, Lisai Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pretraining based meta-learning methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes very marginal improvements. In this paper, 1) we figure out the key reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning the feature extractor is less meaningful;2) instead of fine-tuning the feature extractor, we focus on estimating more representative prototypes during meta-learning. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative attribute features as priors. Then, we design a prototype completion network to learn to complete prototypes with these priors. To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples. Extensive experiments show that our method: (i) can obtain more accurate prototypes;(ii) outperforms state-of-the-art techniques by 2% similar to 9% in terms of classification accuracy. Our code is available online(1).

关键词： Knowledge engineering computer vision Codes Annotations Computational modeling Prototypes Feature extraction

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