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Scale-aware Automatic Augmentation for Object Detection

Scale-aware Automatic Augmentation for Object Detection

引用

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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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...

引用

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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Detecting Human-Object Interaction via Fabricated Compositional Learning

Detecting Human-Object Interaction via Fabricated Compositio...

引用

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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Affordance Transfer Learning for Human-Object Interaction Detection

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

引用

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

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

引用

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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Meta-Mining Discriminative Samples for Kinship Verification

Meta-Mining Discriminative Samples for Kinship Verification

引用

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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Semantic Image Matting

Semantic Image Matting

引用

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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Evaluating and Improving Compositional Text-to-Visual Generation

Evaluating and Improving Compositional Text-to-Visual Genera...

引用

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

作者： Baiqi Li Zhiqiu Lin Deepak Pathak Jiayao Li Yixin Fei Kewen Wu Xide Xia Pengchuan Zhang Graham Neubig Deva Ramanan CMU Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While text-to-visual models now produce photo-realistic images and videos, they struggle with compositional text prompts involving attributes, relationships, and higher-order reasoning such as logic and comparison. In this work, we conduct an extensive human study on GenAI-Bench to evaluate the performance of leading image and video generation models in various aspects of compositional text-to-visual generation. We also compare automated evaluation metrics against our collected human ratings and find that VQAScore – a metric measuring the likelihood that a VQA model views an image as accurately depicting the prompt – significantly outperforms previous metrics such as CLIP-Score. In addition, VQAScore can improve generation in a black-box manner (without finetuning) via simply ranking a few (3 to 9) candidate images. Ranking by VQAScore is 2x to 3x more effective than other scoring methods like PickScore and ImageReward at improving human ratings for DALL-E 3 and Stable Diffusion, especially on compositional prompts that require advanced visio-linguistic reasoning. Lastly, we identify areas for improvement in VQAScore, such as addressing fine-grained visual details. Despite mild limitations, VQAScore serves as the best automated metric as well as reward function for improving prompt alignment. We will release over 80,000 human ratings to facilitate scientific benchmarking of both generative models and automated metrics.

关键词： Measurement Visualization Toxicology Closed box Footwear Cognition pattern recognition

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引用

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

作者： Hu, Zijian Yang, Zhengyu Hu, Xuefeng Nevatia, Ram Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781665445092

A common classification task situation is where one has a large amount of data available for training, but only a small portion is annotated with class labels. The goal of semi-supervised training, in this context, is to improve classification accuracy by leverage information not only from labeled data but also from a large amount of unlabeled data. Recent works [2, 1, 26] have developed significant improvements by exploring the consistency constrain between differently augmented labeled and unlabeled data. Following this path, we propose a novel unsupervised objective that focuses on the less studied relationship between the high confidence unlabeled data that are similar to each other. The new proposed Pair Loss minimizes the statistical distance between high confidence pseudo labels with similarity above a certain threshold. Combining the Pair Loss with the techniques developed by the MixMatch family [2, 1, 26], our proposed SimPLE algorithm shows significant performance gains over previous algorithms on CIFAR-100 and Mini-ImageNet [31], and is on par with the state-of-the-art methods on CIFAR-10 and SVHN. Furthermore, SimPLE also outperforms the state-of-the-art methods in the transfer learning setting, where models are initialized by the weights pre-trained on ImageNet[15] or DomainNet-Real[23].

关键词： Training computer vision Codes Computational modeling Transfer learning Performance gain Semisupervised learning

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