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Glaucoma Precognition: Recognizing Preclinical Visual Functional Signs of Glaucoma

Glaucoma Precognition: Recognizing Preclinical Visual Functi...

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

作者： Gupta, Krati Thakur, Anshul Goldbaum, Michael Yousefi, Siamak Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi Himachal Prades India Univ Calif San Diego Dept Ophthalmol San Diego CA 92103 USA Univ Tennessee Hlth Sci Ctr Dept Ophthalmol Memphis TN 38103 USA Univ Tennessee Hlth Sci Ctr Dept Genet Genom & Informat Memphis TN USA

ISBN: (纸本)9781728193601

Deep archetypal analysis (DAA) has recently been proposed as an unsupervised approach for discovering latent structures in data. However, while a few approaches have used classical archetypal analysis (AA), DAA has not been incorporated in medical image analysis as yet. The purpose of this study is to develop a precognition framework to identify preclinical signs of glaucomatous vision loss using convex representations derived from DAA. We first develop an AA structure and a novel DAA framework to recognize hidden patterns of visual functional loss, and then project visual field data over the identified patterns to obtain a representation for glaucoma precognition several years prior to disease onset. We then develop a glaucoma classification framework using class-balanced bagging with neural networks to address the class imbalance problem. In contrast to other classification approaches, DAA, applied to a unique prospective longitudinal dataset with approximately eight years of visual field tests from normal eyes that developed glaucoma, has allowed visualization of the early signs of glaucoma and development of a construct for glaucoma precognition. Our findings suggest that our proposed glaucoma precognition approach could significantly advance state-of-the-art glaucoma prediction.

关键词： vision

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Sparsely Annotated Semantic Segmentation with Adaptive Gaussian Mixtures

Sparsely Annotated Semantic Segmentation with Adaptive Gauss...

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conference on computer vision and pattern recognition (CVPR)

作者： Linshan Wu Zhun Zhong Leyuan Fang Xingxin He Qiang Liu Jiayi Ma Hao Chen College of Electrical and Information Engineering Hunan University Department of Information Engineering and Computer Science University of Trento School of Electronic Information Wuhan University Department of Computer Science and Engineering Hong Kong University of Science and Technology

Sparsely annotated semantic segmentation (SASS) aims to learn a segmentation model by images with sparse labels (i.e., points or scribbles). Existing methods mainly focus on introducing low-level affinity or generating pseudo labels to strengthen supervision, while largely ignoring the inherent relation between labeled and unlabeled pixels. In this paper, we observe that pixels that are close to each other in the feature space are more likely to share the same class. Inspired by this, we propose a novel SASS framework, which is equipped with an Adaptive Gaussian Mixture Model (AGMM). Our AGMM can effectively endow reliable supervision for unlabeled pixels based on the distributions of labeled and unlabeled pixels. Specifically, we first build Gaussian mixtures using labeled pixels and their relatively similar unlabeled pixels, where the labeled pixels act as centroids, for modeling the feature distribution of each class. Then, we leverage the reliable information from labeled pixels and adaptively generated GMM predictions to supervise the training of unlabeled pixels, achieving online, dynamic, and robust selfsupervision. In addition, by capturing category-wise Gaussian mixtures, AGMM encourages the model to learn discriminative class decision boundaries in an end-to-end contrastive learning manner. Experimental results conducted on the PASCAL VOC 2012 and Cityscapes datasets demonstrate that our AGMM can establish new state-of-the-art SASS performance. Code is available at https://***/Luffy03/AGMM-SASS

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Blemish-aware and Progressive Face Retouching with Limited Paired Data

Blemish-aware and Progressive Face Retouching with Limited P...

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conference on computer vision and pattern recognition (CVPR)

作者： Lianxin Xie Wen Xue Zhen Xu Si Wu Zhiwen Yu 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

Face retouching aims to remove facial blemishes, while at the same time maintaining the textual details of a given input image. The main challenge lies in distinguishing blemishes from the facial characteristics, such as moles. Training an image-to-image translation network with pixel-wise supervision suffers from the problem of expensive paired training data, since professional retouching needs specialized experience and is time-consuming. In this paper, we propose a Blemish-aware and Progressive Face Retouching model, which is referred to as BPFRe. Our framework can be partitioned into two manageable stages to perform progressive blemish removal. Specifically, an encoder-decoder-based module learns to coarsely remove the blemishes at the first stage, and the resulting intermediate features are injected into a generator to enrich local detail at the second stage. We find that explicitly suppressing the blemishes can contribute to an effective collaboration among the components. Toward this end, we incorporate an attention module, which learns to infer a blemish-aware map and further determine the corresponding weights, which are then used to refine the intermediate features transferred from the encoder to the decoder, and from the decoder to the generator. Therefore, BPFRe is able to deliver significant performance gains on a wide range of face retouching tasks. It is worth noting that we reduce the dependence of BPFRe on paired training samples by imposing effective regularization on unpaired ones.

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A method for detecting text of arbitrary shapes in natural scenes that improves text spotting

A method for detecting text of arbitrary shapes in natural s...

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

作者： Wang, Qitong Zheng, Yi Betke, Margrit Boston Univ Boston MA 02215 USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193601

Understanding the meaning of text in images of natural scenes like highway signs or store front emblems is particularly challenging if the text is foreshortened in the image or the letters are artistically distorted. We introduce a pipeline-based text spotting framework that can both detect and recognize text in various fonts, shapes, and orientations in natural scene images with complicated backgrounds. The main contribution of our work is the text detection component, which we call UHT, short for UNet, Heatmap, and Textfill. UHT uses a UNet to compute heatmaps for candidate text regions and a textfill algorithm to produce tight polygonal boundaries around each word in the candidate text. Our method trains the UNet with groundtruth heatmaps that we obtain from text bounding polygons provided by groundtruth annotations. Our text spotting framework, called UHTA, combines UHT with the state-of-the-art text recognition system ASTER. Experiments on four challenging and public scene-text-detection datasets (Total-Text, SCUT-CTW1500, MSRA-TD500, and COCO-Text) show the effectiveness and generalization ability of UHT in detecting not only multilingual (potentially rotated) straight but also curved text in scripts of multiple languages. Our experimental results of UHTA on the Total-Text dataset show that UHTA outperforms four state-of-the-art text spotting frameworks by at least 9.1 percent points in the F-measure, which suggests that UHTA may be used as a complete text detection and recognition system in real applications.

关键词： Heating systems Skeleton Feature extraction Shape Text recognition computer vision Training

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ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos

ANetQA: A Large-scale Benchmark for Fine-grained Composition...

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conference on computer vision and pattern recognition (CVPR)

作者： Zhou Yu Lixiang Zheng Zhou Zhao Fei Wu Jianping Fan Kui Ren Jun Yu School of Computer Science Hangzhou Dianzi University China Colledge of Computer Science and Technology Zhejiang University China AI Lab at Lenovo Research China School of Cyber Science and Technology Zhejiang University China

Building benchmarks to systemically analyze different capabilities of video question answering (VideoQA) models is challenging yet crucial. Existing benchmarks often use non-compositional simple questions and suffer from language biases, making it difficult to diagnose model weaknesses incisively. A recent benchmark AGQA [8] poses a promising paradigm to generate QA pairs automatically from pre-annotated scene graphs, enabling it to measure diverse reasoning abilities with granular control. However, its questions have limitations in reasoning about the fine-grained semantics in videos as such information is absent in its scene graphs. To this end, we present ANetQA, a large-scale benchmark that supports fine-grained compositional reasoning over the challenging untrimmed videos from ActivityNet [4]. Similar to AGQA, the QA pairs in ANetQA are automatically generated from annotated video scene graphs. The fine-grained properties of ANetQA are reflected in the following: (i) untrimmed videos with fine-grained semantics; (ii) spatio-temporal scene graphs with fine-grained taxonomies; and (iii) diverse questions generated from fine-grained templates. ANetQA attains 1.4 billion unbalanced and 13.4 million balanced QA pairs, which is an order of magnitude larger than AGQA with a similar number of videos. Comprehensive experiments are performed for state-of-the-art methods. The best model achieves 44.5% accuracy while human performance tops out at 84.5%, leaving sufficient room for improvement.

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Are We Ready for vision-Centric Driving Streaming Perception? The ASAP Benchmark

Are We Ready for Vision-Centric Driving Streaming Perception...

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conference on computer vision and pattern recognition (CVPR)

作者： Xiaofeng Wang Zheng Zhu Yunpeng Zhang Guan Huang Yun Ye Wenbo Xu Ziwei Chen Xingang Wang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences PhiGent Robotics Southeast University

In recent years, vision-centric perception has flourished in various autonomous driving tasks, including 3D detection, semantic map construction, motion forecasting, and depth estimation. Nevertheless, the latency of vision-centric approaches is too high for practical deployment (e.g., most camera-based 3D detectors have a runtime greater than 300ms). To bridge the gap between ideal researches and real-world applications, it is necessary to quantify the trade-off between performance and efficiency. Traditionally, autonomous-driving perception benchmarks perform the offline evaluation, neglecting the inference time delay. To mitigate the problem, we propose the Autonomous-driving StreAming Perception (ASAP) benchmark, which is the first benchmark to evaluate the online performance of vision-centric perception in autonomous driving. On the basis of the 2Hz annotated nuScenes dataset, we first propose an annotation-extending pipeline to generate high-frame-rate labels for the 12Hz raw images. Referring to the practical deployment, the Streaming Perception Under constRained-computation (SPUR) evaluation protocol is further constructed, where the 12Hz inputs are utilized for streaming evaluation under the constraints of different computational resources. In the ASAP benchmark, comprehensive experiment results reveal that the model rank alters under different constraints, suggesting that the model latency and computation budget should be considered as design choices to optimize the practical deployment. To facilitate further research, we establish baselines for camera-based streaming 3D detection, which consistently enhance the streaming performance across various hardware. ASAP project page: https://***/JeffWang987/ASAP.

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Weakly Supervised Video Representation Learning with Unaligned Text for Sequential Videos

Weakly Supervised Video Representation Learning with Unalign...

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conference on computer vision and pattern recognition (CVPR)

作者： Sixun Dong Huazhang Hu Dongze Lian Weixin Luo Yicheng Qian Shenghua Gao ShanghaiTech University National University of Singapore Meituan Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai Engineering Research Center of Energy Efficient and Custom AI IC

Sequential video understanding, as an emerging video understanding task, has driven lots of researchers' attention because of its goal-oriented nature. This paper studies weakly supervised sequential video understanding where the accurate time-stamp level text-video alignment is not provided. We solve this task by borrowing ideas from CLIP. Specifically, we use a transformer to aggregate frame-level features for video representation and use a pre-trained text encoder to encode the texts corresponding to each action and the whole video, respectively. To model the correspondence between text and video, we propose a multiple granularity loss, where the video-paragraph contrastive loss enforces matching between the whole video and the complete script, and a fine-grained frame-sentence contrastive loss enforces the matching between each action and its description. As the frame-sentence correspondence is not available, we propose to use the fact that video actions happen sequentially in the temporal domain to generate pseudo frame-sentence correspondence and supervise the network training with the pseudo labels. Extensive experiments on video sequence verification and text-to-video matching show that our method outperforms baselines by a large margin, which validates the effectiveness of our proposed approach. Code is available at https://***/svip-lab/WeakSVR.

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Semi-Supervised Learning with Scarce Annotations

Semi-Supervised Learning with Scarce Annotations

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

作者： Rebuffi, Sylvestre-Alvise Ehrhardt, Sebastien Han, Kai Vedaldi, Andrea Zisserman, Andrew Univ Oxford Visual Geometry Grp Oxford England

ISBN: (纸本)9781728193601

While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small. In this work, we consider the problem of SSL multi-class classification with very few labelled instances. We introduce two key ideas. The first is a simple but effective one: we leverage the power of transfer learning among different tasks and self-supervision to initialize a good representation of the data without making use of any label. The second idea is a new algorithm for SSL that can exploit well such a pre-trained representation. The algorithm works by alternating two phases, one fitting the labelled points and one fitting the unlabelled ones, with carefully-controlled information flow between them. The benefits are greatly reducing overfitting of the labelled data and avoiding issue with balancing labelled and unlabelled losses during training. We show empirically that this method can successfully train competitive models with as few as 10 labelled data points per class. More in general, we show that the idea of bootstrapping features using self-supervised learning always improves SSL on standard benchmarks. We show that our algorithm works increasingly well compared to other methods when refining from other tasks or datasets.

关键词： computer vision

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Finetune like you pretrain: Improved finetuning of zero-shot vision models

Finetune like you pretrain: Improved finetuning of zero-shot...

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conference on computer vision and pattern recognition (CVPR)

作者： Sachin Goyal Ananya Kumar Sankalp Garg Zico Kolter Aditi Raghunathan Carnegie Mellon University Stanford University Bosch Center for AI

Finetuning image-text models such as CLIP achieves state-of-the-art accuracies on a variety of benchmarks. However, recent works (Kumar et al., 2022; Wortsman et al., 2021) have shown that even subtle differences in the finetuning process can lead to surprisingly large differences in the final performance, both for in-distribution (ID) and out-of-distribution (OOD) data. In this work, we show that a natural and simple approach of mimicking contrastive pretraining consistently outperforms alternative finetuning approaches. Specifically, we cast downstream class labels as text prompts and continue optimizing the contrastive loss between image embeddings and class-descriptive prompt embeddings (contrastive finetuning). Our method consistently outperforms baselines across 7 distribution shift, 6 transfer learning, and 3 few-shot learning benchmarks. On WILDS-iWILDCam, our proposed approach FLYP outperforms the top of the leaderboard by 2.3% ID and 2.7% OOD, giving the highest reported accuracy. Averaged across 7 OOD datasets (2 WILDS and 5 ImageNet associated shifts), FLYP gives gains of 4.2% OOD over standard finetuning and outperforms current state-of-the-art (LP-FT) by more than 1 % both ID and OOD. Similarly, on 3 few-shot learning benchmarks, FLYP gives gains up to 4.6% over standard finetuning and 4.4% over the state-of-the-art. Thus we establish our proposed method of contrastive finetuning as a simple and intuitive state-of-the-art for supervised finetuning of image-text models like CLIP. Code is available at https://***/locuslab/FLYP.

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Hierarchical B-Frame Video Coding Using Two-Layer CANF Without Motion Coding

Hierarchical B-Frame Video Coding Using Two-Layer CANF Witho...

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conference on computer vision and pattern recognition (CVPR)

作者： David Alexandre Hsueh-Ming Hang Wen-Hsiao Peng Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Typical video compression systems consist of two main modules: motion coding and residual coding. This general architecture is adopted by classical coding schemes (such as international standards H.265 and H.266) and deep learning-based coding schemes. We propose a novel B-frame coding architecture based on two-layer Conditional Augmented Normalization Flows (CANF). It has the striking feature of not transmitting any motion information. Our proposed idea of video compression without motion coding offers a new direction for learned video coding. Our base layer is a low-resolution image compressor that replaces the full-resolution motion compressor. The low-resolution coded image is merged with the warped high-resolution images to generate a high-quality image as a conditioning signal for the enhancement-layer image coding in full resolution. One advantage of this architecture is significantly reduced computational complexity due to eliminating the motion information compressor. In addition, we adopt a skip-mode coding technique to reduce the transmitted latent samples. The rate-distortion performance of our scheme is slightly lower than that of the state-of-the-art learned B-frame coding scheme, B-CANF, but outperforms other learned B-frame coding schemes. However, compared to B-CANF, our scheme saves 45% of multiply-accumulate operations (MACs) for encoding and 27% of MACs for decoding. The code is available at https://***.

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