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DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

DANNet: A One-Stage Domain Adaptation Network for Unsupervis...

引用

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

作者： Wu, Xinyi Wu, Zhenyao Guo, Hao Ju, Lili Wang, Song Univ South Carolina Columbia SC 29208 USA Farsee2 Technol Ltd Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665445092

Semantic segmentation of nighttime images plays an equally important role as that of daytime images in autonomous driving, but the former is much more challenging due to poor illuminations and arduous human annotations. In this paper, we propose a novel domain adaptation network (DANNet) for nighttime semantic segmentation without using labeled nighttime image data. It employs an adversarial training with a labeled daytime dataset and an unlabeled dataset that contains coarsely aligned day-night image pairs. Specifically, for the unlabeled day-night image pairs, we use the pixel-level predictions of static object categories on a daytime image as a pseudo supervision to segment its counterpart nighttime image. We further design a re-weighting strategy to handle the inaccuracy caused by misalignment between day-night image pairs and wrong predictions of daytime images, as well as boost the prediction accuracy of small objects. The proposed DANNet is the first one-stage adaptation framework for nighttime semantic segmentation, which does not train additional day-night image transfer models as a separate pre-processing stage. Extensive experiments on Dark Zurich and Nighttime Driving datasets show that our method achieves state-of-the-art performance for nighttime semantic segmentation.

关键词： Training Bridges Image segmentation computer vision Annotations Semantics Neural networks

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Affordance Transfer Learning for Human-Object Interaction Detection

Affordance Transfer Learning for Human-Object Interaction De...

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

作者： Hou, Zhi Yu, Baosheng Qiao, Yu Peng, Xiaojiang Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Reasoning the human-object interactions (HOI) is essential for deeper scene understanding, while object affordances (or functionalities) are of great importance for human to discover unseen HOIs with novel objects. Inspired by this, we introduce an affordance transfer learning approach to jointly detect HOIs with novel object and recognize affordances. Specifically, HOI representations can be decoupled into a combination of affordance and object representations, making it possible to compose novel interactions by combining affordance representations and novel object representations from additional images, i.e. transferring the affordance to novel objects. With the proposed affordance transfer learning, the model is also capable of inferring the affordances of novel objects from known affordance representations. The proposed method can thus be used to 1) improve the performance of HOI detection, especially for the HOIs with unseen objects;and 2) infer the affordances of novel objects. Experimental results on two datasets, HICO-DET and HOI-COCO (from V-COCO), demonstrate significant improvements over recent state-of-the-art methods for HOI detection and object affordance detection.

关键词： computer vision Codes Affordances Computational modeling Transfer learning Cognition pattern recognition

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Group Collaborative Learning for Co-Salient Object Detection

Group Collaborative Learning for Co-Salient Object Detection

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

作者： Fan, Qi Fan, Deng-Ping Fu, Huazhu Tang, Chi-Keung Shao, Ling Tai, Yu-Wing HKUST Hong Kong Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module;2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module conditioning the inconsistent consensus. To learn a better embedding space without extra computational overhead, we explicitly employ auxiliary classification supervision. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and Cosal2015, demonstrate that our simple GCoNet outperforms 10 cutting-edge models and achieves the new state-of-the-art. We demonstrate this paper's new technical contributions on a number of important downstream computer vision applications including content aware co-segmentation, co-localization based automatic thumbnails, etc.

关键词： computer vision Codes Computational modeling Semantics Object detection Benchmark testing Collaborative work

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One Thing One Click: A Self-Training Approach for Weakly Supervised 3D Semantic Segmentation

One Thing One Click: A Self-Training Approach for Weakly Sup...

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

作者： Liu, Zhengzhe Qi, Xiaojuan Fu, Chi-Wing Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Point cloud semantic segmentation often requires large-scale annotated training data, but clearly, point-wise labels are too tedious to prepare. While some recent methods propose to train a 3D network with small percentages of point labels, we take the approach to an extreme and propose "One Thing One Click," meaning that the annotator only needs to label one point per object. To leverage these extremely sparse labels in network training, we design a novel self-training approach, in which we iteratively conduct the training and label propagation, facilitated by a graph propagation module. Also, we adopt a relation network to generate the per-category prototype and explicitly model the similarity among graph nodes to generate pseudo labels to guide the iterative training. Experimental results on both ScanNet-v2 and S3DIS show that our self-training approach, with extremely-sparse annotations, outperforms all existing weakly supervised methods for 3D semantic segmentation by a large margin, and our results are also comparable to those of the fully supervised counterparts.

关键词： Training computer vision Three-dimensional displays Annotations Semantics Training data Prototypes

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Meta-Mining Discriminative Samples for Kinship Verification

Meta-Mining Discriminative Samples for Kinship Verification

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

作者： Li, Wanhua Wang, Shiwei Lu, Jiwen Feng, Jianjiang Zhou, Jie Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Modern Post Beijing Peoples R China

ISBN: (纸本)9781665445092

Kinship verification aims to find out whether there is a kin relation for a given pair of facial images. Kinship verification databases are born with unbalanced data. For a database with N positive kinship pairs, we naturally obtain N(N - 1) negative pairs. How to fully utilize the limited positive pairs and mine discriminative information from sufficient negative samples for kinship verification remains an open issue. To address this problem, we propose a Discriminative Sample Meta-Mining (DSMM) approach in this paper. Unlike existing methods that usually construct a balanced dataset with fixed negative pairs, we propose to utilize all possible pairs and automatically learn discriminative information from data. Specifically, we sample an unbalanced train batch and a balanced meta-train batch for each iteration. Then we learn a meta-miner with the meta-gradient on the balanced meta-train batch. In the end, the samples in the unbalanced train batch are re-weighted by the learned meta-miner to optimize the kinship models. Experimental results on the widely used KinFaceWI, KinFaceW-II, TSKinFace, and Cornell Kinship datasets demonstrate the effectiveness of the proposed approach.

关键词： Training computer vision Databases Computational modeling pattern recognition

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CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning

CoLA: Weakly-Supervised Temporal Action Localization with Sn...

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

作者： Zhang, Can Cao, Meng Yang, Dongming Chen, Jie Zou, Yuexian Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Weakly-supervised temporal action localization (WS-TAL) aims to localize actions in untrimmed videos with only video-level labels. Most existing models follow the "localization by classification" procedure: locate temporal regions contributing most to the video-level classification. Generally, they process each snippet (or frame) individually and thus overlook the fruitful temporal context relation. Here arises the single snippet cheating issue: "hard" snippets are too vague to be classified. In this paper, we argue that learning by comparing helps identify these hard snippets and we propose to utilize snippet Contrastive learning to Localize Actions, CoLA for short. Specifically, we propose a Snippet Contrast (SniCo) Loss to refine the hard snippet representation in feature space, which guides the network to perceive precise temporal boundaries and avoid the temporal interval interruption. Besides, since it is infeasible to access frame-level annotations, we introduce a Hard Snippet Mining algorithm to locate the potential hard snippets. Substantial analyses verify that this mining strategy efficaciously captures the hard snippets and SniCo Loss leads to more informative feature representation. Extensive experiments show that CoLA achieves state-of-the-art results on THUMOS'14 and ActivityNet v1.2 datasets.

关键词： Location awareness computer vision Annotations Benchmark testing pattern recognition Videos

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Learning Spatially-Variant MAP Models for Non-blind Image Deblurring

Learning Spatially-Variant MAP Models for Non-blind Image De...

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

作者： Dong, Jiangxin Roth, Stefan Schiele, Bernt Saarland Informat Campus MPI Informat Saarbrucken Germany Tech Univ Darmstadt Darmstadt Germany Hessian AI Darmstadt Germany

ISBN: (纸本)9781665445092

The classical maximum a-posteriori (MAP) framework for non-blind image deblurring requires defining suitable data and regularization terms, whose interplay yields the desired clear image through optimization. The vast majority of prior work focuses on advancing one of these two crucial ingredients, while keeping the other one standard. Considering the indispensable roles and interplay of both data and regularization terms, we propose a simple and effective approach to jointly learn these two terms, embedding deep neural networks within the constraints of the MAP framework, trained in an end-to-end manner. The neural networks not only yield suitable image-adaptive features for both terms, but actually predict per-pixel spatially-variant features instead of the commonly used spatially-uniform ones. The resulting spatially-variant data and regularization terms particularly improve the restoration of fine-scale structures and detail. Quantitative and qualitative results underline the effectiveness of our approach, substantially outperforming the current state of the art.

关键词： Deep learning Maximum a posteriori estimation Image quality computer vision Neural networks Data models Image restoration

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Semantic Image Matting

Semantic Image Matting

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

作者： Sun, Yanan Tang, Chi-Keung Tai, Yu-Wing HKUST Hong Kong Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Natural image matting separates the foreground from background in fractional occupancy which can be caused by highly transparent objects, complex foreground (e.g., net or tree), and/or objects containing very fine details (e.g., hairs). Although conventional matting formulation can be applied to all of the above cases, no previous work has attempted to reason the underlying causes of matting due to various foreground semantics. We show how to obtain better alpha mattes by incorporating into our framework semantic classification of matting regions. Specifically, we consider and learn 20 classes of matting patterns, and propose to extend the conventional trimap to semantic trimap. The proposed semantic trimap can be obtained automatically through patch structure analysis within trimap regions. Meanwhile, we learn a multi-class discriminator to regularize the alpha prediction at semantic level, and content-sensitive weights to balance different regularization losses. Experiments on multiple benchmarks show that our method outperforms other methods and has achieved the most competitive state-of-theart performance. Finally, we contribute a large-scale Semantic Image Matting Dataset with careful consideration of data balancing across different semantic classes. Code and dataset are available at https://***/nowsyn/ SIM.

关键词： Hair computer vision Codes Semantics Benchmark testing Data mining Optimization

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Detecting Human-Object Interaction via Fabricated Compositional Learning

Detecting Human-Object Interaction via Fabricated Compositio...

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

作者： Hou, Zhi Yu, Baosheng Qiao, Yu Peng, Xiaojiang Tao, Dacheng Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Human-Object Interaction (HOI) detection, inferring the relationships between human and objects from images/videos, is a fundamental task for high-level scene understanding. However, HOI detection usually suffers from the open long-tailed nature of interactions with objects, while human has extremely powerful compositional perception ability to cognize rare or unseen HOI samples. Inspired by this, we devise a novel HOI compositional learning framework, termed as Fabricated Compositional Learning (FCL), to address the problem of open long-tailed HOI detection. Specifically, we introduce an object fabricator to generate effective object representations, and then combine verbs and fabricated objects to compose new HOI samples. With the proposed object fabricator, we are able to generate large-scale HOI samples for rare and unseen categories to alleviate the open long-tailed issues in HOI detection. Extensive experiments on the most popular HOI detection dataset, HICO-DET, demonstrate the effectiveness of the proposed method for imbalanced HOI detection and significantly improve the state-of-the-art performance on rare and unseen HOI categories. Code is available at https://***/zhihou7/HOI-CL.

关键词： computer vision Codes Benchmark testing pattern recognition Task analysis

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Scale-aware Automatic Augmentation for Object Detection

Scale-aware Automatic Augmentation for Object Detection

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

作者： Chen, Yukang Li, Yanwei Kong, Tao Qi, Lu Chu, Ruihang Li, Lei Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China ByteDance AI Lab Beijing Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9781665445092

We propose Scale-aware AutoAug to learn data augmentation policies for object detection. We define a new scaleaware search space, where both image- and box-level augmentations are designed for maintaining scale invariance. Upon this search space, we propose a new search metric, termed Pareto Scale Balance, to facilitate search with high efficiency. In experiments, Scale-aware AutoAug yields significant and consistent improvement on various object detectors (e.g., RetinaNet, Faster R-CNN, Mask R-CNN, and FCOS), even compared with strong multi-scale training baselines. Our searched augmentation policies are transferable to other datasets and box-level tasks beyond object detection (e.g., instance segmentation and keypoint estimation) to improve performance. The search cost is much less than previous automated augmentation approaches for object detection. It is notable that our searched policies have meaningful patterns, which intuitively provide valuable insight for human data augmentation design. Code and models are available at https://***/Jia-ResearchLab/SA-AutoAug.

关键词： Training computer vision Costs Estimation Object detection Detectors Search problems

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