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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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Vector Quantization with Self-Attention for Quality-Independent Representation Learning

Vector Quantization with Self-Attention for Quality-Independ...

引用

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

作者： Yang, Zhou Dong, Weisheng Li, Xin Huang, Mengluan Sun, Yulin Shi, Guangming Xidian Univ Xian Peoples R China West Virginia Univ Morgantown WV USA

ISBN: (纸本)9798350301298

Recently, the robustness of deep neural networks has drawn extensive attention due to the potential distribution shift between training and testing data (e.g., deep models trained on high-quality images are sensitive to corruption during testing). Many researchers attempt to make the model learn invariant representations from multiple corrupted data through data augmentation or image-pair-based feature distillation to improve the robustness. Inspired by sparse representation in image restoration, we opt to address this issue by learning image-quality-independent feature representation in a simple plug-and-play manner, that is, to introduce discrete vector quantization (VQ) to remove redundancy in recognition models. Specifically, we first add a codebook module to the network to quantize deep features. Then we concatenate them and design a self-attention module to enhance the representation. During training, we enforce the quantization of features from clean and corrupted images in the same discrete embedding space so that an invariant quality-independent feature representation can be learned to improve the recognition robustness of low-quality images. Qualitative and quantitative experimental results show that our method achieved this goal effectively, leading to a new state-of-the-art result of 43.1 % mCE on ImageNet-C with ResNet50 as the backbone. On other robustness benchmark datasets, such as ImageNet-R, our method also has an accuracy improvement of almost 2%. The source code is available at https://***/faculty/wsdong/Projects/***

关键词： detection recognition: Categorization retrieval

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Learning from Unique Perspectives: User-aware Saliency Modeling

Learning from Unique Perspectives: User-aware Saliency Model...

引用

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

作者： Chen, Shi Valliappa, Nachiappan Shen, Shaolei Ye, Xinyu Kohlhoff, Kai He, Junfeng Univ Minnesota Minneapolis MN USA Google Res Mountain View CA USA Google Mountain View CA USA

ISBN: (纸本)9798350301298

Everyone is unique. Given the same visual stimuli, people's attention is driven by both salient visual cues and their own inherent preferences. Knowledge of visual preferences not only facilitates understanding of fine-grained attention patterns of diverse users, but also has the potential of benefiting the development of customized applications. Nevertheless, existing saliency models typically limit their scope to attention as it applies to the general population and ignore the variability between users' behaviors. In this paper, we identify the critical roles of visual preferences in attention modeling, and for the first time study the problem of user-aware saliency modeling. Our work aims to advance attention research from three distinct perspectives: (1) We present a new model with the flexibility to capture attention patterns of various combinations of users, so that we can adaptively predict personalized attention, user group attention, and general saliency at the same time with one single model;(2) To augment models with knowledge about the composition of attention from different users, we further propose a principled learning method to understand visual attention in a progressive manner;and (3) We carry out extensive analyses on publicly available saliency datasets to shed light on the roles of visual preferences. Experimental results on diverse stimuli, including naturalistic images and web pages, demonstrate the advantages of our method in capturing the distinct visual behaviors of different users and the general saliency of visual stimuli.

关键词： Low-level vision

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EDA: Explicit Text-Decoupling and Dense Alignment for 3D Visual Grounding

EDA: Explicit Text-Decoupling and Dense Alignment for 3D Vis...

引用

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

作者： Wu, Yanmin Cheng, Xinhua Zhang, Renrui Cheng, Zesen Zhang, Jian Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

3D visual grounding aims to find the object within point clouds mentioned by free-form natural language descriptions with rich semantic cues. However, existing methods either extract the sentence-level features coupling all words or focus more on object names, which would lose the word-level information or neglect other attributes. To alleviate these issues, we present EDA that Explicitly Decouples the textual attributes in a sentence and conducts Dense Alignment between such fine-grained language and point cloud objects. Specifically, we first propose a text decoupling module to produce textual features for every semantic component. Then, we design two losses to supervise the dense matching between two modalities: position alignment loss and semantic alignment loss. On top of that, we further introduce a new visual grounding task, locating objects without object names, which can thoroughly evaluate the model's dense alignment capacity. Through experiments, we achieve state-of-the-art performance on two widely-adopted 3D visual grounding datasets, Scan-Refer and SR3D/NR3D, and obtain absolute leadership on our newly-proposed task. The source code is available at https://***/yanmin-wu/EDA.

关键词： language reasoning vision

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Fair Federated Medical Image Segmentation via Client Contribution Estimation

Fair Federated Medical Image Segmentation via Client Contrib...

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

作者： Jiang, Meirui Roth, Holger R. Li, Wenqi Yang, Dong Zhao, Can Nath, Vishwesh Xu, Daguang Dou, Qi Xu, Ziyue Chinese Univ Hong Kong Hong Kong Peoples R China NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9798350301298

How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness). Despite achieving progress on either one, we argue that it is critical to consider them together, in order to engage and motivate more diverse clients joining FL to derive a high-quality global model. In this work, we propose a novel method to optimize both types of fairness simultaneously. Specifically, we propose to estimate client contribution in gradient and data space. In gradient space, we monitor the gradient direction differences of each client with respect to others. And in data space, we measure the prediction error on client data using an auxiliary model. Based on this contribution estimation, we propose a FL method, federated training via contribution estimation (FedCE), i.e., using estimation as global model aggregation weights. We have theoretically analyzed our method and empirically evaluated it on two real-world medical datasets. The effectiveness of our approach has been validated with significant performance improvements, better collaboration fairness, better performance fairness, and comprehensive analytical studies. Code is available at https://***. io/NVFlare/research/fed-ce

关键词： cell microscopy Medical and biological vision

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On-the-fly Category Discovery

On-the-fly Category Discovery

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

作者： Du, Ruoyi Chang, Dongliang Liang, Kongming Hospedales, Timothy Song, Yi-Zhe Ma, Zhanyu Beijing Univ Posts & Telecommun Beijing Peoples R China Univ Edinburgh Edinburgh Scotland Univ Surrey Guildford England

ISBN: (纸本)9798350301298

Although machines have surpassed humans on visual recognition problems, they are still limited to providing closed-set answers. Unlike machines, humans can cognize novel categories at the first observation. Novel category discovery (NCD) techniques, transferring knowledge from seen categories to distinguish unseen categories, aim to bridge the gap. However, current NCD methods assume a transductive learning and offline inference paradigm, which restricts them to a pre-defined query set and renders them unable to deliver instant feedback. In this paper, we study on-the-fly category discovery (OCD) aimed at making the model instantaneously aware of novel category samples (i.e., enabling inductive learning and streaming inference). We first design a hash coding-based expandable recognition model as a practical baseline. Afterwards, noticing the sensitivity of hash codes to intra-category variance, we further propose a novel Sign-Magnitude dIsentangLEment (SMILE) architecture to alleviate the disturbance it brings. Our experimental results demonstrate the superiority of SMILE against our baseline model and prior art. Our code is available at https://***/PRISCV/On-the-fly-Category-Discovery.

关键词： detection recognition: Categorization retrieval

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Learning Federated Visual Prompt in Null Space for MRI Reconstruction

Learning Federated Visual Prompt in Null Space for MRI Recon...

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

作者： Feng, Chun-Mei Li, Bangjun Xu, Xinxing Liu, Yong Fu, Huazhu Zuo, Wangmeng ASTAR Inst High Performance Comp WPC Singapore Singapore Shangdong Univ Sch Informat Sci & Engn Jinan Peoples R China Harbin Inst Technol Harbin Peoples R China

ISBN: (纸本)9798350301298

Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy. However, the data heterogeneity caused by different MRI protocols, insufficient local training data, and limited communication bandwidth inevitably impair global model convergence and updating. In this paper, we propose a new algorithm, FedPR, to learn federated visual prompts in the null space of global prompt for MRI reconstruction. FedPR is a new federated paradigm that adopts a powerful pre-trained model while only learning and communicating the prompts with few learnable parameters, thereby significantly reducing communication costs and achieving competitive performance on limited local data. Moreover, to deal with catastrophic forgetting caused by data heterogeneity, FedPR also updates efficient federated visual prompts that project the local prompts into an approximate null space of the global prompt, thereby suppressing the interference of gradients on the server performance. Extensive experiments on federated MRI show that FedPR significantly outperforms state-of-the-art FL algorithms with < 6% of communication costs when given the limited amount of local training data.

关键词： cell microscopy Medical and biological vision

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Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching between Parts and Words

Parts2Words: Learning Joint Embedding of Point Clouds and Te...

引用

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

作者： Tang, Chuan Yang, Xi Wu, Bojian Han, Zhizhong Chang, Yi Jilin Univ Sch Artificial Intelligence Jilin Peoples R China Zhejiang Univ Hangzhou Peoples R China Wayne State Univ Dept Comp Sci Wayne NJ USA MoE Engn Res Ctr Knowledge Driven Human Machine Intel Beijing Peoples R China

ISBN: (纸本)9798350301298

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here.

关键词： vision + graphics

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itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection

itKD: Interchange Transfer-based Knowledge Distillation for ...

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

作者： Cho, Hyeon Choi, Junyong Baek, Geonwoo Hwang, Wonjun Ajou Univ Suwon South Korea Hyundai Motor Co Seoul South Korea Naver AI Lab Seongnam South Korea

ISBN: (纸本)9798350301298

Point-cloud based 3D object detectors recently have achieved remarkable progress. However, most studies are limited to the development of network architectures for improving only their accuracy without consideration of the computational efficiency. In this paper, we first propose an autoencoder-style framework comprising channel-wise compression and decompression via interchange transfer-based knowledge distillation. To learn the map-view feature of a teacher network, the features from teacher and student networks are independently passed through the shared autoencoder;here, we use a compressed representation loss that binds the channel-wised compression knowledge from both student and teacher networks as a kind of regularization. The decompressed features are transferred in opposite directions to reduce the gap in the interchange reconstructions. Lastly, we present an head attention loss to match the 3D object detection information drawn by the multi-head self-attention mechanism. Through extensive experiments, we verify that our method can train the lightweight model that is well-aligned with the 3D point cloud detection task and we demonstrate its superiority using the well-known public datasets;e.g., Waymo and nuScenes.(1)

关键词： Efficient and scalable vision

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Progressive Transformation Learning for Leveraging Virtual Images in Training

Progressive Transformation Learning for Leveraging Virtual I...

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

作者： Shen, Yi-Ting Lee, Hyungtae Kwon, Heesung Bhattacharyya, Shuvra S. Univ Maryland College Pk MD USA DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350301298

To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data curation, we introduce Progressive Transformation Learning (PTL), which gradually augments a training dataset by adding transformed virtual images with enhanced realism. Generally, a virtual2real transformation generator in the conditional GAN framework suffers from quality degradation when a large domain gap exists between real and virtual images. To deal with the domain gap, PTL takes a novel approach that progressively iterates the following three steps: 1) select a subset from a pool of virtual images according to the domain gap, 2) transform the selected virtual images to enhance realism, and 3) add the transformed virtual images to the training set while removing them from the pool. In PTL, accurately quantifying the domain gap is critical. To do that, we theoretically demonstrate that the feature representation space of a given object detector can be modeled as a multivariate Gaussian distribution from which the Mahalanobis distance between a virtual object and the Gaussian distribution of each object category in the representation space can be readily computed. Experiments show that PTL results in a substantial performance increase over the baseline, especially in the small data and the cross-domain regime.

关键词： detection recognition: Categorization retrieval

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