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Learning to Exploit Temporal Structure for Biomedical vision-Language Processing

Learning to Exploit Temporal Structure for Biomedical Vision...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Shruthi Bannur Stephanie Hyland Qianchu Liu Fernando Pérez-García Maximilian Ilse Daniel C. Castro Benedikt Boecking Harshita Sharma Kenza Bouzid Anja Thieme Anton Schwaighofer Maria Wetscherek Matthew P. Lungren Aditya Nori Javier Alvarez-Valle Ozan Oktay Microsoft Health Futures

Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data.

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Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion

Improving Multi-Target Multi-Camera Tracking by Track Refine...

引用

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

作者： Andreas Specker Lucas Florin Mickael Cormier Jü rgen Beyerer Karlsruhe Institute of Technology Fraunhofer IOSB Fraunhofer Center for Machine Learning

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Multi-camera tracking of vehicles on a city-wide level is a core component of modern traffic monitoring systems. For this task, single-camera tracking failures are the most common causes of errors concerning automatic multi-target multi-camera tracking systems. To address these problems, we propose several modules that aim at improving single-camera tracklets, e.g., appearance-based tracklet splitting, single-camera clustering, and track completion. After these track refinement steps, hierarchical clustering is used to associate the enhanced single-camera tracklets. During this stage, we leverage vehicle re-identification features as well as prior knowledge about the scene's topology. Last, the proposed track completion strategy is adopted for the cross-camera association task to obtain the final multi-camera tracks. Our method proves itself competitive: With it, we achieved 4th place in track 1 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Topology pattern recognition Task analysis Artificial intelligence

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On the Pitfall of Mixup for Uncertainty Calibration

On the Pitfall of Mixup for Uncertainty Calibration

引用

conference on computer vision and pattern recognition (CVPR)

作者： Deng-Bao Wang Lanqing Li Peilin Zhao Pheng-Ann Heng Min-Ling Zhang School of Computer Science and Engineering Southeast University Naniing China Tencent AI Lab Zhejiang Lab The Chinese University of Hong Kong

By simply taking convex combinations between pairs of samples and their labels, mixup training has been shown to easily improve predictive accuracy. It has been recently found that models trained with mixup also perform well on uncertainty calibration. However, in this study, we found that mixup training usually makes models less calibratable than vanilla empirical risk minimization, which means that it would harm uncertainty estimation when post-hoc calibration is considered. By decomposing the mixup process into data transformation and random perturbation, we suggest that the confidence penalty nature of the data transformation is the reason of calibration degradation. To mitigate this problem, we first investigate the mixup inference strategy and found that despite it improves calibration on mixup, this ensemble-like strategy does not necessarily out-perform simple ensemble. Then, we propose a general strategy named mixup inference in training, which adopts a simple decoupling principle for recovering the outputs of raw samples at the end of forward network pass. By embedding the mixup inference, models can be learned from the original one-hot labels and hence avoid the negative impact of confidence penalty. Our experiments show this strategy properly solves mixup's calibration issue without sacrificing the predictive performance, while even improves accuracy than vanilla mixup.

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VILA: Learning Image Aesthetics from User Comments with vision-Language Pretraining

VILA: Learning Image Aesthetics from User Comments with Visi...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Junjie Ke Keren Ye Jiahui Yu Yonghui Wu Peyman Milanfar Feng Yang Google Research

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic assessment (IAA) methods primarily rely on human-labeled rating scores, which oversimplify the visual aesthetic information that humans perceive. Conversely, user comments offer more comprehensive information and are a more natural way to express human opinions and preferences regarding image aesthetics. In light of this, we propose learning image aesthetics from user comments, and exploring vision-language pretraining methods to learn multimodal aesthetic representations. Specifically, we pretrain an image-text encoder-decoder model with image-comment pairs, using contrastive and generative objectives to learn rich and generic aesthetic semantics without human labels. To efficiently adapt the pretrained model for downstream IAA tasks, we further propose a lightweight rank-based adapter that employs text as an anchor to learn the aesthetic ranking concept. Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines. With only minimal finetuning parameters using the proposed adapter module, our model achieves state-of-the-art IAA performance over the AVA dataset. 1 1 Our model is available at https://***/google-research/google-research/tree/master/VILA

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Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing

Domain Agnostic Feature Learning for Image and Video Based F...

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

作者： Saha, Suman Xu, Wenhao Kanakis, Menelaos Georgoulis, Stamatios Chen, Yuhua Paudel, Danda Pani Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this direction for biometric user authentication, and in particular face recognition, that tries to prevent spoof attacks. The state-of-the-art anti-spoofing techniques leverage the ability of deep neural networks to learn discriminative features, based on cues from the training set images or video samples, in an effort to detect spoof attacks. However, due to the particular nature of the problem, i.e. large variability due to factors like different backgrounds, lighting conditions, camera resolutions, spoof materials, etc., these techniques typically fail to generalize to new samples. In this paper, we explicitly tackle this problem and propose a class-conditional domain discriminator module, that, coupled with a gradient reversal layer, tries to generate live and spoof features that are discriminative, but at the same time robust against the aforementioned variability factors. Extensive experimental analysis shows the effectiveness of the proposed method over existing image- and video-based anti-spoofing techniques, both in terms of numerical improvement as well as when visualizing the learned features.

关键词： Face Lighting computer vision Authentication Feature extraction Cameras Visualization

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Balanced Product of Calibrated Experts for Long-Tailed recognition

Balanced Product of Calibrated Experts for Long-Tailed Recog...

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conference on computer vision and pattern recognition (CVPR)

作者： Emanuel Sanchez Aimar Arvi Jonnarth Michael Felsberg Marco Kuhlmann Department of Electrical Engineering Linköing University Sweden Husqvarna Group Huskvarna Sweden University of KwaZulu-Natal Durban South Africa Department of Computer and Information Science Linköping University Sweden

Many real-world recognition problems are characterized by long-tailed label distributions. These distributions make representation learning highly challenging due to limited generalization over the tail classes. If the test distribution differs from the training distribution, e.g. uniform versus long-tailed, the problem of the distribution shift needs to be addressed. A recent line of work proposes learning multiple diverse experts to tackle this issue. Ensemble diversity is encouraged by various techniques, e.g. by specializing different experts in the head and the tail classes. In this work, we take an analytical approach and extend the notion of logit adjustment to ensembles to form a Balanced Product of Experts (BalPoE). BalPoE combines a family of experts with different test-time target distributions, generalizing several previous approaches. We show how to properly define these distributions and combine the experts in order to achieve unbiased predictions, by proving that the ensemble is Fisher-consistent for minimizing the balanced error. Our theoretical analysis shows that our balanced ensemble requires calibrated experts, which we achieve in practice using mixup. We conduct extensive experiments and our method obtains new state-of-the-art results on three long-tailed datasets: CIFAR-100-LT, ImageNet-LT, and iNaturalist-2018. Our code is available at https://***/emasa/BalPoE-CalibratedLT.

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Reducing the Label Bias for Timestamp Supervised Temporal Action Segmentation

Reducing the Label Bias for Timestamp Supervised Temporal Ac...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Kaiyuan Liu Yunheng Li Shenglan Liu Chenwei Tan Zihang Shao School of Computer Science Dalian University of Technology China

Timestamp supervised temporal action segmentation (TSTAS) is more cost-effective than fully supervised counterparts. However, previous approaches suffer from severe label bias due to over-reliance on sparse timestamp annotations, resulting in unsatisfactory performance. In this paper, we propose the Debiasing-TSTAS (D-TSTAS) framework by exploiting unannotated frames to alleviate this bias from two phases: 1) Initialization. To reduce the dependencies on annotated frames, we propose masked timestamp predictions (MTP) to ensure that initialized model captures more contextual information. 2) Refinement. To overcome the limitation of the expressiveness from sparsely annotated timestamps, we propose a center-oriented timestamp expansion (CTE) approach to progressively expand pseudo-timestamp groups which contain semantic-rich motion representation of action segments. Then, these pseudo-timestamp groups and the model output are used to iteratively generate pseudo-labels for refining the model in a fully supervised setup. We further introduce segmental confidence loss to enable the model to have high confidence predictions within the pseudo-timestamp groups and more accurate action boundaries. Our D-TSTAS outperforms the state-of-the-art TSTAS method as well as achieves competitive results compared with fully supervised approaches on three benchmark datasets.

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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...

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conference on computer vision and pattern recognition (CVPR)

作者： Qingjie Zeng Yutong Xie Zilin Lu Yong Xia School of Computer Science and Engineering Northwestern Polytechnical University China The University of Adelaide Australia

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 multi-class 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.

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FAME-ViL: Multi-Tasking vision-Language Model for Heterogeneous Fashion Tasks

FAME-ViL: Multi-Tasking Vision-Language Model for Heterogene...

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conference on computer vision and pattern recognition (CVPR)

作者： Xiao Han Xiatian Zhu Licheng Yu Li Zhang Yi-Zhe Song Tao Xiang CVSSP University of Surrey iFlyTek-Surrey Joint Research Centre on Artificial Intelligence Surrey Institute for People-Centred Artificial Intelligence Fudan University

In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image captioning. They differ drastically in each individual input/output format and dataset size. It has been common to design a task-specific model and fine-tune it independently from a pre-trained V+l model (e.g., CLIP). This results in parameter inefficiency and inability to exploit inter-task relatedness. To address such issues, we propose a novel FAshion-focused Multi-task Efficient learning method for vision-and-Language tasks (FAME-ViL) in this work. Compared with existing approaches, FAME-ViL applies a single model for multiple heterogeneous fashion tasks, therefore being much more parameter-efficient. It is enabled by two novel components: (1) a task-versatile architecture with cross-attention adapters and task-specific adapters integrated into a unified V+L model, and (2) a stable and effective multi-task training strategy that supports learning from heterogeneous data and prevents negative transfer. Extensive experiments on four fashion tasks show that our FAME-ViL can save 61.5% of parameters over alternatives, while significantly outperforming the conventional independently trained single-task models. Code is available at https://***/BrandonHanx/FAME-ViL.

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Exploring Intra-class Variation Factors with Learnable Cluster Prompts for Semi-supervised Image Synthesis

Exploring Intra-class Variation Factors with Learnable Clust...

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conference on computer vision and pattern recognition (CVPR)

作者： Yunfei Zhang Xiaoyang Huo Tianyi Chen Si Wu Hau San Wong School of Computer Science and Engineering South China University of Technology Peng Cheng Laboratory PAZHOU LAB Department of Computer Science City University of Hong Kong

Semi-supervised class-conditional image synthesis is typically performed by inferring and injecting class labels into a conditional Generative Adversarial Network (GAN). The supervision in the form of class identity may be inadequate to model classes with diverse visual appearances. In this paper, we propose a Learnable Cluster Prompt-based GAN (LCP-GAN) to capture class-wise characteristics and intra-class variation factors with a broader source of supervision. To exploit partially labeled data, we perform soft partitioning on each class, and explore the possibility of associating intra-class clusters with learnable visual concepts in the feature space of a pre-trained language-vision model, e.g., CLIP. For class-conditional image generation, we design a cluster-conditional generator by injecting a combination of intra-class cluster label embeddings, and further incorporate a real-fake classification head on top of CLIP to distinguish real instances from the synthesized ones, conditioned on the learnable cluster prompts. This significantly strengthens the generator with more semantic language supervision. LCP-GAN not only possesses superior generation capability but also matches the performance of the fully supervised version of the base models: BigGAN and StyleGAN2-ADA, on multiple standard benchmarks.

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