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VLPD: Context-Aware Pedestrian Detection via vision-Language Semantic Self-Supervision

VLPD: Context-Aware Pedestrian Detection via Vision-Language...

引用

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

作者： Liu, Mengyin Jiang, Jie Zhu, Chao Yin, Xu-Cheng Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Tencent Data Platform Dept Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Detecting pedestrians accurately in urban scenes is significant for realistic applications like autonomous driving or video surveillance. However, confusing human-like objects often lead to wrong detections, and small scale or heavily occluded pedestrians are easily missed due to their unusual appearances. To address these challenges, only object regions are inadequate, thus how to fully utilize more explicit and semantic contexts becomes a key problem. Meanwhile, previous context-aware pedestrian detectors either only learn latent contexts with visual clues, or need laborious annotations to obtain explicit and semantic contexts. Therefore, we propose in this paper a novel approach via vision-Language semantic self-supervision for context-aware Pedestrian Detection (VLPD) to model explicitly semantic contexts without any extra annotations. Firstly, we propose a self-supervised vision-Language Semantic (VLS) segmentation method, which learns both fully-supervised pedestrian detection and contextual segmentation via self-generated explicit labels of semantic classes by vision-language models. Furthermore, a self-supervised Prototypical Semantic Contrastive (PSC) learning method is proposed to better discriminate pedestrians and other classes, based on more explicit and semantic contexts obtained from VLS. Extensive experiments on popular benchmarks show that our proposed VLPD achieves superior performances over the previous state-of-the-arts, particularly under challenging circumstances like small scale and heavy occlusion. Code is available at https://***/lmy98129/VLPD.

关键词： detection recognition: Categorization retrieval

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PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection

PointDistiller: Structured Knowledge Distillation Towards Ef...

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

作者： Zhang, Linfeng Dong, Runpei Tai, Hung-Shuo Ma, Kaisheng Tsinghua Univ Beijing Peoples R China Xi An Jiao Tong Univ Xian Peoples R China DIDI Beijing Peoples R China

ISBN: (纸本)9798350301298

The remarkable breakthroughs in point cloud representation learning have boosted their usage in real-world applications such as self-driving cars and virtual reality. However, these applications usually have a strict requirement for not only accurate but also efficient 3D object detection. Recently, knowledge distillation has been proposed as an effective model compression technique, which transfers the knowledge from an over-parameterized teacher to a lightweight student and achieves consistent effectiveness in 2D vision. However, due to point clouds' sparsity and irregularity, directly applying previous image-based knowledge distillation methods to point cloud detectors usually leads to unsatisfactory performance. To fill the gap, this paper proposes PointDistiller, a structured knowledge distillation framework for point clouds-based 3D detection. Concretely, PointDistiller includes local distillation which extracts and distills the local geometric structure of point clouds with dynamic graph convolution and reweighted learning strategy, which highlights student learning on the crucial points or voxels to improve knowledge distillation efficiency. Extensive experiments on both voxels-based and raw points-based detectors have demonstrated the effectiveness of our method over seven previous knowledge distillation methods. For instance, our 4x compressed PointPillars student achieves 2.8 and 3.4 mAP improvements on BEV and 3D object detection, outperforming its teacher by 0.9 and 1.8 mAP, respectively. Codes are available in https://***/RunpeiDong/PointDistiller.

关键词： Efficient and scalable vision

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PEFAT: Boosting Semi-supervised Medical Image Classification via Pseudo-loss Estimation and Feature Adversarial Training

PEFAT: Boosting Semi-supervised Medical Image Classification...

引用

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

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Xia, Yong Northwestern Polytech Univ Sch Comp Sci & Engn Xian Peoples R China Univ Adelaide Adelaide SA 5005 Australia

ISBN: (纸本)9798350301298

Pseudo-labeling approaches have been proven beneficial for semi-supervised learning (SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding samples with high-confidence pseudo-labels from the perspective of model predicted probability. Whereas this way may lead to the inclusion of incorrectly pseudo-labeled data if the threshold is not carefully adjusted. In addition, low-confidence probability samples are frequently disregarded and not employed to their full potential. In this paper, we propose a novel Pseudo-loss Estimation and Feature Adversarial Training semi-supervised framework, termed as PEFAT, to boost the performance of multiclass and multi-label medical image classification from the point of loss distribution modeling and adversarial training. Specifically, we develop a trustworthy data selection scheme to split a high-quality pseudo-labeled set, inspired by the dividable pseudo-loss assumption that clean data tend to show lower loss while noise data is the opposite. Instead of directly discarding these samples with low-quality pseudo-labels, we present a novel regularization approach to learn discriminate information from them via injecting adversarial noises at the feature-level to smooth the decision boundary. Experimental results on three medical and two natural image benchmarks validate that our PEFAT can achieve a promising performance and surpass other state-of-the-art methods. The code is available at https://***/maxwell0027/PEFAT.

关键词： cell microscopy Medical and biological vision

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CrowdCLIP: Unsupervised Crowd Counting via vision-Language Model

CrowdCLIP: Unsupervised Crowd Counting via Vision-Language M...

引用

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

作者： Liang, Dingkang Xie, Jiahao Zou, Zhikang Ye, Xiaoqing Xu, Wei Bai, Xiang Huazhong Univ Sci & Technol Wuhan Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Baidu Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Supervised crowd counting relies heavily on costly manual labeling, which is difficult and expensive, especially in dense scenes. To alleviate the problem, we propose a novel unsupervised framework for crowd counting, named CrowdCLIP. The core idea is built on two observations: 1) the recent contrastive pre-trained vision-language model (CLIP) has presented impressive performance on various downstream tasks;2) there is a natural mapping between crowd patches and count text. To the best of our knowledge, CrowdCLIP is the first to investigate the vision-language knowledge to solve the counting problem. Specifically, in the training stage, we exploit the multi-modal ranking loss by constructing ranking text prompts to match the size-sorted crowd patches to guide the image encoder learning. In the testing stage, to deal with the diversity of image patches, we propose a simple yet effective progressive filtering strategy to first select the highly potential crowd patches and then map them into the language space with various counting intervals. Extensive experiments on five challenging datasets demonstrate that the proposed CrowdCLIP achieves superior performance compared to previous unsupervised state-of-the-art counting methods. Notably, CrowdCLIP even surpasses some popular fully-supervised methods under the cross-dataset setting. The source code will be available at https:// ***/dk-liang/CrowdCLIP.

关键词： body gesture Humans: Face movement pose

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Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR

Lite DETR : An Interleaved Multi-Scale Encoder for Efficient...

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

作者： Li, Feng Zeng, Ailing Liu, Shilong Zhang, Hao Li, Hongyang Zhang, Lei Ni, Lionel M. Hong Kong Univ Sci & Technol Hong Kong Peoples R China IDEA Shenzhen Peoples R China Tsinghua Univ Inst AI Dept CST BNRist Ctr Beijing Peoples R China South China Univ Sci & Technol Guangzhou Guangdong Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798350301298

Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance. Its success cannot be achieved without the re-introduction of multi-scale feature fusion in the encoder. However, the excessively increased tokens in multi-scale features, especially for about 75% of low-level features, are quite computationally inefficient, which hinders real applications of DETR models. In this paper, we present Lite DETR, a simple yet efficient end-to-end object detection framework that can effectively reduce the GFLOPs of the detection head by 60% while keeping 99% of the original performance. Specifically, we design an efficient encoder block to update high-level features (corresponding to small-resolution feature maps) and low-level features (corresponding to large-resolution feature maps) in an interleaved way. In addition, to better fuse cross-scale features, we develop a key-aware deformable attention to predict more reliable attention weights. Comprehensive experiments validate the effectiveness and efficiency of the proposed Lite DETR, and the efficient encoder strategy can generalize well across existing DETR-based models. The code will be available in https://***/IDEA-Research/LiteDETR.

关键词： vision applications and systems

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OCELOT: Overlapped Cell on Tissue Dataset for Histopathology

OCELOT: Overlapped Cell on Tissue Dataset for Histopathology

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

作者： Ryu, Jeongun Puche, Aaron Valero Shin, JaeWoong Park, Seonwook Brattoli, Biagio Lee, Jinhee Jung, Wonkyung Cho, Soo Ick Paeng, Kyunghyun Ock, Chan-Young Yoo, Donggeun Pereira, Sergio Lunit Inc Seoul South Korea

ISBN: (纸本)9798350301298

Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection, pathologists often zoom out to understand the tissue-level structures and zoom in to classify cells based on their morphology and the surrounding context. However, there is a lack of efforts to reflect such behaviors by pathologists in the cell detection models, mainly due to the lack of datasets containing both cell and tissue annotations with overlapping regions. To overcome this limitation, we propose and publicly release OCELOT, a dataset purposely dedicated to the study of cell-tissue relationships for cell detection in histopathology. OCELOT provides overlapping cell and tissue annotations on images acquired from multiple organs. Within this setting, we also propose multi-task learning approaches that benefit from learning both cell and tissue tasks simultaneously. When compared against a model trained only for the cell detection task, our proposed approaches improve cell detection performance on 3 datasets: proposed OCELOT, public TIGER, and internal CARP datasets. On the OCELOT test set in particular, we show up to 6.79 improvement in F1-score. We believe the contributions of this paper, including the release of the OCELOT dataset at https://***/research/publications/ocelot are a crucial starting point toward the important research direction of incorporating celltissue relationships in computation pathology.

关键词： cell microscopy Medical and biological vision

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Glocal Energy-based Learning for Few-Shot Open-Set recognition

Glocal Energy-based Learning for Few-Shot Open-Set Recogniti...

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

作者： Wang, Haoyu Pang, Guansong Wang, Peng Zhang, Lei Wei, Wei Zhang, Yanning Northwestern Polytech Univ Xian Peoples R China Singapore Management Univ Singapore Singapore Univ Wollonong Wollongong NSW Australia

ISBN: (纸本)9798350301298

Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closed-set classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of open-set samples, our model leverages both class-wise and pixel-wise features to learn a glocal energy-based score, in which a global energy score is learned using the class-wise features, while a local energy score is learned using the pixel-wise features. The model is enforced to assign large energy scores to samples that are deviated from the few-shot examples in either the class-wise features or the pixel-wise features, and to assign small energy scores otherwise. Experiments on three standard FSOR datasets show the superior performance of our model.(1)

关键词： detection recognition: Categorization retrieval

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Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval

Boundary-aware Backward-Compatible Representation via Advers...

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

作者： Pan, Tan Xu, Furong Yang, Xudong He, Sifeng Jiang, Chen Guo, Qingpei Qian, Feng Zhang, Xiaobo Cheng, Yuan Yang, Lei Chu, Wei Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Image retrieval plays an important role in the Internet world. Usually, the core parts of mainstream visual retrieval systems include an online service of the embedding model and a large-scale vector database. For traditional model upgrades, the old model will not be replaced by the new one until the embeddings of all the images in the database are re-computed by the new model, which takes days or weeks for a large amount of data. Recently, backward-compatible training (BCT) enables the new model to be immediately deployed online by making the new embeddings directly comparable to the old ones. For BCT, improving the compatibility of two models with less negative impact on retrieval performance is the key challenge. In this paper, we introduce AdvBCT, an Adversarial Backward-Compatible Training method with an elastic boundary constraint that takes both compatibility and discrimination into consideration. We first employ adversarial learning to minimize the distribution disparity between embeddings of the new model and the old model. Meanwhile, we add an elastic boundary constraint during training to improve compatibility and discrimination efficiently. Extensive experiments on GLDv2, Revisited Oxford (ROxford), and Revisited Paris (RParis) demonstrate that our method outperforms other BCT methods on both compatibility and discrimination. The implementation of AdvBCT will be publicly available at https://***/Ashespt/AdvBCT.

关键词： detection recognition: Categorization retrieval

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Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graph

Multi-Granularity Archaeological Dating of Chinese Bronze Di...

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

作者： Zhou, Rixin Wei, Jiafu Zhang, Qian Qi, Ruihua Yang, Xi Li, Chuntao Jilin Univ Sch Artificial Intelligence Jilin Peoples R China MoE Engn Res Ctr Knowledge Driven Human Machine Intel Beijing Peoples R China Jilin Univ Sch Archaeol Jilin Peoples R China

ISBN: (纸本)9798350301298

The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history. Current archaeology depends on trained experts to carry out bronze dating, which is time-consuming and labor-intensive. For such dating, in this study, we propose a learning-based approach to integrate advanced deep learning techniques and archaeological knowledge. To achieve this, we first collect a large-scale image dataset of bronze dings, which contains richer attribute information than other existing fine-grained datasets. Second, we introduce a multihead classifier and a knowledge-guided relation graph to mine the relationship between attributes and the ding era. Third, we conduct comparison experiments with various existing methods, the results of which show that our dating method achieves a state-of-the-art performance. We hope that our data and applied networks will enrich fine-grained classification research relevant to other interdisciplinary areas of expertise. The dataset and source code used are included in our supplementary materials, and will be open after submission owing to the anonymity policy. Source codes and data are available at: https://***/zhourixin/bronze-Ding

关键词： detection recognition: Categorization retrieval

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OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer

OSRT: Omnidirectional Image Super-Resolution with Distortion...

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

作者： Yu, Fanghua Wang, Xintao Cao, Mingdeng Li, Gen Shan, Ying Dong, Chao Chinese Acad Sci ShenZhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Shenzhen Peoples R China Tencent PCG ARC Shenzhen Peoples R China Univ Tokyo Tokyo Japan Tencent Online Video Platform Technol Shenzhen Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Omnidirectional images (ODIs) have obtained lots of research interest for immersive experiences. Although ODIs require extremely high resolution to capture details of the entire scene, the resolutions of most ODIs are insufficient. Previous methods attempt to solve this issue by image super-resolution (SR) on equirectangular projection (ERP) images. However, they omit geometric properties of ERP in the degradation process, and their models can hardly generalize to real ERP images. In this paper, we propose Fisheye downsampling, which mimics the real-world imaging process and synthesizes more realistic low-resolution samples. Then we design a distortion-aware Transformer (OSRT) to modulate ERP distortions continuously and self-adaptively. Without a cumbersome process, OSRT outperforms previous methods by about 0.2dB on PSNR. Moreover, we propose a convenient data augmentation strategy, which synthesizes pseudo ERP images from plain images. This simple strategy can alleviate the over-fitting problem of large networks and significantly boost the performance of ODISR. Extensive experiments have demonstrated the state-of-the-art performance of our OSRT.

关键词： Low-level vision

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