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Few-Shot Object Detection via Classification Refinement and Distractor Retreatment

Few-Shot Object Detection via Classification Refinement and ...

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

作者： Li, Yiting Zhu, Haiyue Cheng, Yu Wang, Wenxin Teo, Chek Sing Xiang, Cheng Vadakkepat, Prahlad Lee, Tong Heng Natl Univ Singapore Singapore Singapore Agcy Sci Technol & Res SIMTech Singapore Singapore

ISBN: (纸本)9781665445092

We aim to tackle the challenging Few-Shot Object Detection (FSOD), where data-scarce categories are presented during the model learning. The failure modes of Faster-RCNN in FSOD are investigated, and we find that the performance degradation is mainly due to the classification incapability (false positives) caused by category confusion, which motivates us to address FSOD from a novel aspect of classification refinement. Specifically, we address the intrinsic limitation from the aspects of both architectural enhancement and hard-example mining. We introduce a novel few-shot classification refinement mechanism where a de coupled Few-Shot Classification Network (FSCN) is employed to improve the final classification of a base detector. Moreover, we especially probe a commonly-overlooked but destructive issue of FSOD, i.e., the presence of distractor samples due to the incomplete annotations where images from the base set may contain novel-class objects but remain unlabelled. Retreatment solutions are developed to eliminate the incurred false positives. For FSCN training, the distractor is formulated as a semi-supervised problem, where a distractor utilization loss is proposed to make proper use of it for boosting the data-scarce classes, while a confidence-guided dataset pruning (CGDP) technique is developed to facilitate the few-shot adaptation of base detector. Experiments demonstrate that our proposed framework achieves state-of-the-art FSOD performance on public datasets, e.g., Pascal VOC and MS-COCO.

关键词： Training Degradation computer vision Filtering Detectors Object detection Boosting

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Dynamic Domain Adaptation for Efficient Inference

Dynamic Domain Adaptation for Efficient Inference

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

作者： Li, Shuang Zhang, JinMing Ma, Wenxuan Liu, Chi Harold Li, Wei Beijing Inst Technol Beijing Peoples R China Inceptio Tech Fremont CA USA

ISBN: (纸本)9781665445092

Domain adaptation (DA) enables knowledge transfer from a labeled source domain to an unlabeled target domain by reducing the cross-domain distribution discrepancy. Most prior DA approaches leverage complicated and powerful deep neural networks to improve the adaptation capacity and have shown remarkable success. However, they may have a lack of applicability to real-world situations such as real-time interaction, where low target inference latency is an essential requirement under limited computational budget. In this paper, we tackle the problem by proposing a dynamic domain adaptation (DDA) framework, which can simultaneously achieve efficient target inference in low-resource scenarios and inherit the favorable cross-domain generalization brought by DA. In contrast to static models, as a simple yet generic method, DDA can integrate various domain confusion constraints into any typical adaptive network, where multiple intermediate classifiers can be equipped to infer "easier" and "harder" target data dynamically. Moreover, we present two novel strategies to further boost the adaptation performance of multiple prediction exits: 1) a confidence score learning strategy to derive accurate target pseudo labels by fully exploring the prediction consistency of different classifiers;2) a class-balanced self-training strategy to explicitly adapt multi-stage classifiers from source to target without losing prediction diversity. Extensive experiments on multiple benchmarks are conducted to verify that DDA can consistently improve the adaptation performance and accelerate target inference under domain shift and limited resources scenarios.

关键词： Training Deep learning computer vision Computational modeling Benchmark testing Predictive models Real-time systems

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DAT: Training Deep Networks Robust to Label-Noise by Matching the Feature Distributions

DAT: Training Deep Networks Robust to Label-Noise by Matchin...

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

作者： Qu, Yuntao Mo, Shasha Niu, Jianwei Beihang Univ Sch Cyber Sci & Technol Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China Zhengzhou Univ Res Inst Ind Technol Zhengzhou Henan Peoples R China

ISBN: (纸本)9781665445092

In real application scenarios, the performance of deep networks may be degraded when the dataset contains noisy labels. Existing methods for learning with noisy labels are limited by two aspects. Firstly, methods based on the noise probability modeling can only be applied to class-level noisy labels. Secondly, others based on the memorization effect outperform in synthetic noise but get weak promotion in real-world noisy datasets. To solve these problems, this paper proposes a novel label-noise robust method named Discrepant Adversarial Training (DAT). The DAT method has ability of enforcing prominent feature extraction by matching feature distribution between clean and noisy data. Therefore, under the noise-free feature representation, the deep network can simply output the correct result. To better capture the divergence between the noisy and clean distribution, a new metric is designed to change the distribution divergence into computable. By minimizing the proposed metric with a min-max training of discrepancy on classifiers and generators, DAT can match noisy data to clean data in the feature space. To the best of our knowledge, DAT is the first to address the noisy label problem from the perspective of the feature distribution. Experiments on synthetic and real-world noisy datasets demonstrate that DAT can consistently outperform other state-of-the-art methods. Codes are available at https://***/Tygnn0323/DAT.

关键词： Training computer vision Codes Feature extraction Extraterrestrial measurements Generators pattern recognition

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Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes

Discovering Interpretable Latent Space Directions of GANs Be...

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

作者： Yang, Huiting Chai, Liangyu Wen, Qiang Zhao, Shuang Sun, Zixun He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Tencent Inc Interact Entertainment Grp Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Generative adversarial networks (GANs) learn to map noise latent vectors to high-fidelity image outputs. It is found that the input latent space shows semantic correlations with the output image space. Recent works aim to interpret the latent space and discover meaningful directions that correspond to human interpretable image transformations. However, these methods either rely on explicit scores of attributes (e.g., memorability) or are restricted to binary ones (e.g., gender), which largely limits the applicability of editing tasks, especially for free-form artistic tasks like style/anime editing. In this paper, we propose an adversarial method, AdvStyle, for discovering interpretable directions in the absence of well-labeled scores or binary attributes. In particular, the proposed adversarial method simultaneously optimizes the discovered directions and the attribute assessor using the target attribute data as positive samples, while the generated ones being negative. In this way, arbitrary attributes can be edited by collecting positive data only, and the proposed method learns a controllable representation enabling manipulation of non-binary attributes like anime styles and facial characteristics. Moreover, the proposed learning strategy attenuates the entanglement between attributes, such that multiattribute manipulation can be easily achieved without any additional constraint. Furthermore, we reveal several interesting semantics with the involuntarily learned negative directions. Extensive experiments on 9 anime attributes and 7 human attributes demonstrate the effectiveness of our adversarial approach qualitatively and quantitatively. Code is available at https://***/BERYLSHEEP/AdvStyle.

关键词： computer vision Correlation Codes Semantics Generative adversarial networks pattern recognition Task analysis

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Active Learning for Open-set Annotation

Active Learning for Open-set Annotation

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

作者： Ning, Kun-Peng Zhao, Xun Li, Yu Huang, Sheng-Jun Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Tencent PCG Appl Res Ctr Shenzhen Peoples R China Int Digital Econ Acad IDEA Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing active learning studies typically work in the closed-set setting by assuming that all data examples to be labeled are drawn from known classes. However, in real annotation tasks, the unlabeled data usually contains a large amount of examples from unknown classes, resulting in the failure of most active learning methods. To tackle this openset annotation (OSA) problem, we propose a new active learning framework called LfOSA, which boosts the classification performance with an effective sampling strategy to precisely detect examples from known classes for annotation. The LfOSA framework introduces an auxiliary network to model the per-example max activation value (MAV) distribution with a Gaussian Mixture Model, which can dynamically select the examples with highest probability from known classes in the unlabeled set. Moreover, by reducing the temperature T of the loss function, the detection model will be further optimized by exploiting both known and unknown supervision. The experimental results show that the proposed method can significantly improve the selection quality of known classes, and achieve higher classification accuracy with lower annotation cost than state-of-the-art active learning methods. To the best of our knowledge, this is the first work of active learning for open-set annotation.

关键词： Learning systems Training computer vision Costs Annotations Object detection Detectors

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Learning Cross-Modal Retrieval with Noisy Labels

Learning Cross-Modal Retrieval with Noisy Labels

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

作者： Hu, Peng Peng, Xi Zhu, Hongyuan Zhen, Liangli Lin, Jie Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore Singapore Agcy Sci Technol & Res Inst High Performance Comp Singapore Singapore

ISBN: (纸本)9781665445092

Recently;cross-modal retrieval is emerging with the help of deep multimodal learning. However, even for unimodal data, collecting large-scale well-annotated data is expensive and time-consuming, and not to mention the additional challenges from multiple modalities. Although crowd-sourcing annotation, e.g., Amazon's Mechanical Turk, can be utilized to mitigate the labeling cost, but leading to the unavoidable noise in labels for the non-expert annotating. To tackle the challenge, this paper presents a general Multimodal Robust Learning framework (MRL) for learning with multimodal noisy labels to mitigate noisy samples and correlate distinct modalities simultaneously. To be specific, we propose a Robust Clustering loss (RC) to make the deep networks focus on clean samples instead of noisy ones. Besides, a simple yet effective multimodal loss function, called Multimodal Contrastive loss (MC), is proposed to maximize the mutual information between different modalities, thus alleviating the interference of noisy samples and cross-modal discrepancy. Extensive experiments are conducted on four widely-used multimodal datasets to demonstrate the effectiveness of the proposed approach by comparing to 14 state-of-the-art methods.

关键词： computer vision Costs Annotations Interference pattern recognition Noise measurement Labeling

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A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift

A Differentiable Two-stage Alignment Scheme for Burst Image ...

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

作者： Guo, Shi Yang, Xi Ma, Jianqi Ren, Gaofeng Zhang, Lei Hong Kong Polytech Univ Hong Kong Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Denoising and demosaicking are two essential steps to reconstruct a clean full-color image from the raw data. Recently, joint denoising and demosaicking (JDD) for burst images, namely JDD-B, has attracted much attention by using multiple raw images captured in a short time to reconstruct a single high-quality image. One key challenge of JDD-B lies in the robust alignment of image frames. State-of-the-art alignment methods in feature domain cannot effectively utilize the temporal information of burst images, where large shifts commonly exist due to camera and object motion. In addition, the higher resolution (e.g., 4K) of modern imaging devices results in larger displacement between frames. To address these challenges, we design a differentiable two-stage alignment scheme sequentially in patch and pixel level for effective JDD-B. The input burst images are firstly aligned in the patch level by using a differentiable progressive block matching method, which can estimate the offset between distant frames with small computational cost. Then we perform implicit pixel-wise alignment in full-resolution feature domain to refine the alignment results. The two stages are jointly trained in an end-to-end manner. Extensive experiments demonstrate the significant improvement of our method over existing JDD-B methods. Codes are available https://***/GuoShi28/2StageAlign.

关键词： Visualization computer vision Image resolution Codes Noise reduction Computational efficiency pattern recognition

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Exploring Geometric Consistency for Monocular 3D Object Detection

Exploring Geometric Consistency for Monocular 3D Object Dete...

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

作者： Lian, Qing Ye, Botao Xu, Ruijia Yao, Weilong Zhang, Tong Hong Kong Univ Sci & Technol Hong Kong Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Autowise AI Hayward CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper investigates the geometric consistency for monocular 3D object detection, which suffers from the ill-posed depth estimation. We first conduct a thorough analysis to reveal how existing methods fail to consistently localize objects when different geometric shifts occur. In particular, we design a series of geometric manipulations to diagnose existing detectors and then illustrate their vulnerability to consistently associate the depth with object apparent sizes and positions. To alleviate this issue, we propose four geometry-aware data augmentation approaches to enhance the geometric consistency of the detectors. We first modify some commonly used data augmentation methods for 2D images so that they can maintain geometric consistency in 3D spaces. We demonstrate such modifications are important. In addition, we propose a 3D-specific image perturbation method that employs the camera movement. During the augmentation process, the camera system with the corresponding image is manipulated, while the geometric visual cues for depth recovery are preserved. We show that by using the geometric consistency constraints, the proposed augmentation techniques lead to improvements on the KITTI and nuScenes monocular 3D detection benchmarks with state-of-the-art results. In addition, we demonstrate that the augmentation methods are well suited for semi-supervised training and cross-dataset generalization.

关键词： Training Visualization computer vision Three-dimensional displays Perturbation methods Estimation Object detection

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A Large-scale Comprehensive Dataset and Copy-overlap Aware Evaluation Protocol for Segment-level Video Copy Detection

A Large-scale Comprehensive Dataset and Copy-overlap Aware E...

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

作者： He, Sifeng Yang, Xudong Jiang, Chen Liang, Gang Zhang, Wei Pan, Tan Wang, Qing Xu, Furong Li, Chunguang Liu, Jingxiong Xu, Hui Huang, Kaiming Cheng, Yuan Qian, Feng Zhang, Xiaobo Yang, Lei Ant Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we introduce VCSL (Video Copy Segment Localization), a new comprehensive segment-level annotated video copy dataset. Compared with existing copy detection datasets restricted by either video-level annotation or small-scale, VCSL not only has two orders of magnitude more segment-level labelled data, with 160k realistic video copy pairs containing more than 280k localized copied segment pairs, but also covers a variety of video categories and a wide range of video duration. All the copied segments inside each collected video pair are manually extracted and accompanied by precisely annotated starting and ending timestamps. Alongside the dataset, we also propose a novel evaluation protocol that better measures the prediction accuracy of copy overlapping segments between a video pair and shows improved adaptability in different scenarios. By benchmarking several baseline and state-of-the-art segment-level video copy detection methods with the proposed dataset and evaluation metric, we provide a comprehensive analysis that uncovers the strengths and weaknesses of current approaches, hoping to open up promising directions for future works. The VCSL dataset, metric and benchmark codes are all publicly available at https://***/alipay/VCSL.

关键词： Measurement Location awareness computer vision Protocols Video on demand Current measurement Benchmark testing

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GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

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

作者： You, Shan Huang, Tao Yang, Mingmin Wang, Fei Qian, Chen Zhang, Changshui SenseTime Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Huazhong Univ Sci & Technol Sch CST Dian Grp Wuhan Peoples R China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

Training a supernet matters for one-shot neural architecture search (NAS) methods since it serves as a basic performance estimator for different architectures (paths). Current methods mainly hold the assumption that a supernet should give a reasonable ranking over all paths. They thus treat all paths equally, and spare much effort to train paths. However, it is harsh for a single supernet to evaluate accurately on such a huge-scale search space (e.g., 7(21)). In this paper, instead of covering all paths, we ease the burden of supernet by encouraging it to focus more on evaluation of those potentially-good ones, which are identified using a surrogate portion of validation data. Concretely, during training, we propose a multi-path sampling strategy with rejection, and greedily filter the weak paths. The training efficiency is thus boosted since the training space has been greedily shrunk from all paths to those potentially-good ones. Moreover;we further adopt an exploration and exploitation policy by introducing an empirical candidate path pool. Our proposed method GreedyNAS is easy-to-follow, and experimental results on ImageNet dataset indicate that it can achieve better Top-1 accuracy under same search space and FLOPs or latency level, but with only similar to 60% of supernet training cost. By searching on a larger space, our GreedyNAS can also obtain new state-of-the-art architectures.

关键词： Training computer architecture Task analysis Graphics processing units Hardware computer vision Estimation

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