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Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection

Ambiguity-Resistant Semi-Supervised Learning for Dense Objec...

引用

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

作者： Liu, Chang Zhang, Weiming Lin, Xiangru Zhang, Wei Tan, Xiao Han, Junyu Li, Xiaomao Ding, Errui Wang, Jingdong Shanghai Univ Shanghai Peoples R China Baidu Inc Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that the root lies in two kinds of ambiguities: (1) Selection ambiguity that selected pseudo labels are less accurate, since classification scores cannot properly represent the localization quality. (2) Assignment ambiguity that samples are matched with improper labels in pseudo-label assignment, as the strategy is misguided by missed objects and inaccurate pseudo boxes. To tackle these problems, we propose a Ambiguity-Resistant Semi-supervised Learning (ARSL) for one-stage detectors. Specifically, to alleviate the selection ambiguity, Joint-Confidence Estimation (JCE) is proposed to jointly quantifies the classification and localization quality of pseudo labels. As for the assignment ambiguity, Task-Separation Assignment (TSA) is introduced to assign labels based on pixel-level predictions rather than unreliable pseudo boxes. It employs a 'divide-and-conquer' strategy and separately exploits positives for the classification and localization task, which is more robust to the assignment ambiguity. Comprehensive experiments demonstrate that ARSL effectively mitigates the ambiguities and achieves state-of-the-art SSOD performance on MS COCO and PASCAL VOC. Codes can be found at https://***/PaddlePaddle/PaddleDetection.

关键词： detection recognition: Categorization retrieval

来源：评论

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Activating More Pixels in Image Super-Resolution Transformer

Activating More Pixels in Image Super-Resolution Transformer

引用

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

作者： Chen, Xiangyu Wang, Xintao Zhou, Jiantao Qiao, Yu Dong, Chao Univ Macau State Key Lab Internet Things Smart City Zhuhai Peoples R China Chinese Acad Sci Shenzhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Tencent PCG ARC Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better reconstruction, we propose a novel Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement. Extensive experiments show the effectiveness of the proposed modules, and we further scale up the model to demonstrate that the performance of this task can be greatly improved. Our overall method significantly outperforms the state-of-the-art methods by more than 1dB.

关键词： Low-level vision

来源：评论

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Practical Network Acceleration with Tiny Sets

Practical Network Acceleration with Tiny Sets

引用

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

作者： Wang, Guo-Hua Wu, Jianxin Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9798350301298

Due to data privacy issues, accelerating networks with tiny training sets has become a critical need in practice. Previous methods mainly adopt filter-level pruning to accelerate networks with scarce training samples. In this paper, we reveal that dropping blocks is a fundamentally superior approach in this scenario. It enjoys a higher acceleration ratio and results in a better latency-accuracy performance under the few-shot setting. To choose which blocks to drop, we propose a new concept namely recoverability to measure the difficulty of recovering the compressed network. Our recoverability is efficient and effective for choosing which blocks to drop. Finally, we propose an algorithm named PRACTISE to accelerate networks using only tiny sets of training images. PRACTISE outperforms previous methods by a significant margin. For 22% latency reduction, PRACTISE surpasses previous methods by on average 7% on ImageNet-1k. It also enjoys high generalization ability, working well under data-free or out-of-domain data settings, too. Our code is at https://***/DoctorKey/Practise.

关键词： Efficient and scalable vision

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Phase-Shifting Coder: Predicting Accurate Orientation in Oriented Object Detection

Phase-Shifting Coder: Predicting Accurate Orientation in Ori...

引用

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

作者： Yu, Yi Da, Feipeng Southeast Univ Sch Automat Nanjing Peoples R China Southeast Univ Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing Peoples R China

ISBN: (纸本)9798350301298

With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By mapping the rotational periodicity of different cycles into the phase of different frequencies, we provide a unified framework for various periodic fuzzy problems caused by rotational symmetry in oriented object detection. Upon such a framework, common problems in oriented object detection such as boundary discontinuity and square-like problems are elegantly solved in a unified form. Visual analysis and experiments on three datasets prove the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance. The codes are publicly available at https://***/open-mmlab/mmrotate.

关键词： detection recognition: Categorization retrieval

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VMRNN: Integrating vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting

VMRNN: Integrating Vision Mamba and LSTM for Efficient and A...

引用

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

作者： Tang, Yujin Dong, Peijie Tang, Zhenheng Chu, Xiaowen Liang, Junwei Hong Kong Univ Sci & Technol Guangzhou AI Thrust Guangzhou Peoples R China Hong Kong Univ Sci & Technol Guangzhou DSA Thrust Guangzhou Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9798350365474

Combining Convolutional Neural Networks (CNNs) or vision Transformers(ViTs) with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded unparalleled results in predicting temporal and spatial dynamics. However, modeling extensive global information remains a formidable challenge;CNNs are limited by their narrow receptive fields, and ViTs struggle with the intensive computational demands of their attention mechanisms. The emergence of recent Mamba-based architectures has been met with enthusiasm for their exceptional long-sequence modeling capabilities, surpassing established vision models in efficiency and accuracy, which motivates us to develop an innovative architecture tailored for spatiotemporal forecasting. In this paper, we propose the VMRNN cell, a new recurrent unit that integrates the strengths of vision Mamba blocks with LSTM. We construct a network centered on VMRNN cells to tackle spatiotemporal prediction tasks effectively. Our extensive evaluations show that our proposed approach secures competitive results on a variety of tasks while maintaining a smaller model size. Our code is available at https://***/yyyujintang/VMRNN-PyTorch.

关键词： Spatiotemporal Forecasting State Space Model Video Prediction

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Data-efficient Large Scale Place recognition with Graded Similarity Supervision

Data-efficient Large Scale Place Recognition with Graded Sim...

引用

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

作者： Leyva-Vallina, Maria Strisciuglio, Nicola Petkov, Nicolai Univ Groningen Groningen Netherlands Univ Twente Enschede Netherlands

ISBN: (纸本)9798350301298

Visual place recognition (VPR) is a fundamental task of computer vision for visual localization. Existing methods are trained using image pairs that either depict the same place or not. Such a binary indication does not consider continuous relations of similarity between images of the same place taken from different positions, determined by the continuous nature of camera pose. The binary similarity induces a noisy supervision signal into the training of VPR methods, which stall in local minima and require expensive hard mining algorithms to guarantee convergence. Motivated by the fact that two images of the same place only partially share visual cues due to camera pose differences, we deploy an automatic re-annotation strategy to re-label VPR datasets. We compute graded similarity labels for image pairs based on available localization metadata. Furthermore, we propose a new Generalized Contrastive Loss (GCL) that uses graded similarity labels for training contrastive networks. We demonstrate that the use of the new labels and GCL allow to dispense from hard-pair mining, and to train image descriptors that perform better in VPR by nearest neighbor search, obtaining superior or comparable results than methods that require expensive hard-pair mining and re-ranking techniques.

关键词： detection recognition: Categorization retrieval

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Segment Anything Model for Road Network Graph Extraction

Segment Anything Model for Road Network Graph Extraction

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

作者： Hetang, Congrui Xue, Haoru Le, Cindy Yue, Tianwei Wang, Wenping He, Yihui Carnegie Mellon Univ Pittsburgh PA 15213 USA Columbia Univ New York NY USA

ISBN: (纸本)9798350365474

We propose SAM-Road, an adaptation of the Segment Anything Model (SAM) [27] for extracting large-scale, vectorized road network graphs from satellite imagery. To predict graph geometry, we formulate it as a dense semantic segmentation task, leveraging the inherent strengths of SAM. The image encoder of SAM is fine-tuned to produce probability masks for roads and intersections, from which the graph vertices are extracted via simple non-maximum suppression. To predict graph topology, we designed a lightweight transformer-based graph neural network, which leverages the SAM image embeddings to estimate the edge existence probabilities between vertices. Our approach directly predicts the graph vertices and edges for large regions without expensive and complex post-processing heuristics and is capable of building complete road network graphs spanning multiple square kilometers in a matter of seconds. With its simple, straightforward, and minimalist design, SAM-Road achieves comparable accuracy with the state-of-the-art method RNGDet++[57], while being 40 times faster on the City-scale dataset. We thus demonstrate the power of a foundational vision model when applied to a graph learning task. The code is available at https://***/htcr/sam_road.

关键词： autonomous driving computer vision foundation model graph graph neural network mapping navigation remote sensing segment anything semantic segmentation transformer

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AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection

AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domai...

引用

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

作者： Gao, Yipeng Lin, Kun-Yu Yan, Junkai Wang, Yaowei Zheng, Wei-Shi Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Pengcheng Lab Shenzhen Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China

ISBN: (纸本)9798350301298

In this work, we study few-shot domain adaptive object detection (FSDAOD), where only a few target labeled images are available for training in addition to sufficient source labeled images. Critically, in FSDAOD, the data scarcity in the target domain leads to an extreme data imbalance between the source and target domains, which potentially causes over-adaptation in traditional feature alignment. To address the data imbalance problem, we propose an asymmetric adaptation paradigm, namely AsyFOD, which leverages the source and target instances from different perspectives. Specifically, by using target distribution estimation, the AsyFOD first identifies the target-similar source instances, which serves to augment the limited target instances. Then, we conduct asynchronous alignment between target-dissimilar source instances and augmented target instances, which is simple yet effective for alleviating the over-adaptation. Extensive experiments demonstrate that the proposed AsyFOD outperforms all state-of-the-art methods on four FSDAOD benchmarks with various environmental variances, e.g., 3.1% mAP improvement on Cityscapes-to-FoggyCityscapes and 2.9% mAP increase on Sim10k-to-Cityscapes. The code is available at https://***/Hlings/AsyFOD.

关键词： detection recognition: Categorization retrieval

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vision Transformers Are Good Mask Auto-Labelers

Vision Transformers Are Good Mask Auto-Labelers

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

作者： Lan, Shiyi Yang, Xitong Yu, Zhiding Wu, Zuxuan Alvarez, Jose M. Anandkumar, Anima NVIDIA Santa Clara CA 95051 USA Meta AI FAIR London England Fudan Univ Shanghai Peoples R China CALTECH Pasadena CA USA

ISBN: (纸本)9798350301298

We propose Mask Auto-Labeler (MAL), a high-quality Transformer-based mask auto-labeling framework for instance segmentation using only box annotations. MAL takes box-cropped images as inputs and conditionally generates their mask pseudo-labels. We show that vision Transformers are good mask auto-labelers. Our method significantly reduces the gap between auto-labeling and human annotation regarding mask quality. Instance segmentation models trained using the MAL-generated masks can nearly match the performance of their fully-supervised counterparts, retaining up to 97.4% performance of fully supervised models. The best model achieves 44.1% mAP on COCO instance segmentation (test-dev 2017), outperforming state-of-the-art box-supervised methods by significant margins. Qualitative results indicate that masks produced by MAL are, in some cases, even better than human annotations.

关键词： grouping and shape analysis Segmentation

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Spectral Bayesian Uncertainty for Image Super-resolution

Spectral Bayesian Uncertainty for Image Super-resolution

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

作者： Liu, Tao Cheng, Jun Tan, Shan Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350301298

Recently deep learning techniques have significantly advanced image super-resolution (SR). Due to the black-box nature, quantifying reconstruction uncertainty is crucial when employing these deep SR networks. Previous approaches for SR uncertainty estimation mostly focus on capturing pixel-wise uncertainty in the spatial domain. SR uncertainty in the frequency domain which is highly related to image SR is seldom explored. In this paper, we propose to quantify spectral Bayesian uncertainty in image SR. To achieve this, a Dual-Domain Learning (DDL) framework is first proposed. Combined with Bayesian approaches, the DDL model is able to estimate spectral uncertainty accurately, enabling a reliability assessment for high frequencies reasoning from the frequency domain perspective. Extensive experiments under non-ideal premises are conducted and demonstrate the effectiveness of the proposed spectral uncertainty. Furthermore, we propose a novel Spectral Uncertainty based Decoupled Frequency (SUDF) training scheme for perceptual SR. Experimental results show the proposed SUDF can evidently boost perceptual quality of SR results without sacrificing much pixel accuracy.

关键词： Low-level vision

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