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Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection

Consistent-Teacher: Towards Reducing Inconsistent Pseudo-tar...

引用

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

作者： Wang, Xinjiang Yang, Xingyi Zhang, Shilong Li, Yijiang Feng, Litong Fang, Shijie Lyu, Chengqi Chen, Kai Zhang, Wayne SenseTime Res Hong Kong Peoples R China Shanghai AI Lab Shanghai Peoples R China Natl Univ Singapore Singapore Singapore Peking Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD). Our core observation is that the oscillating pseudo-targets undermine the training of an accurate detector. It injects noise into the student's training, leading to severe overfitting problems. Therefore, we propose a systematic solution, termed Consistent-Teacher, to reduce the inconsistency. First, adaptive anchor assignment (ASA) substitutes the static IoU-based strategy, which enables the student network to be resistant to noisy pseudo-bounding boxes. Then we calibrate the subtask predictions by designing a 3D feature alignment module (FAM-3D). It allows each classification feature to adaptively query the optimal feature vector for the regression task at arbitrary scales and locations. Lastly, a Gaussian Mixture Model (GMM) dynamically revises the score threshold of pseudo-b-boxes, which stabilizes the number of ground truths at an early stage and remedies the unreliable supervision signal during training. Consistent-Teacher provides strong results on a large range of SSOD evaluations. It achieves 40.0 mAP with ResNet-50 backbone given only 10% of annotated MS-COCO data, which surpasses previous baselines using pseudo labels by around 3 mAP. When trained on fully annotated MS-COCO with additional unlabeled data, the performance further increases to 47.7 mAP. Our code is available at https://github. com/Adamdad/ConsistentTeacher.

关键词： vision applications and systems

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SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations

SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sk...

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

作者： Li, Pu Guo, Jianwei Zhang, Xiaopeng Yan, Dong-Ming Chinese Acad Sci Inst Automat MAIS Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9798350301298

Reverse engineering CAD models from raw geometry is a classic but strenuous research problem. Previous learning-based methods rely heavily on labels due to the supervised design patterns or reconstruct CAD shapes that are not easily editable. In this work, we introduce SECADNet, an end-to-end neural network aimed at reconstructing compact and easy-to-edit CAD models in a self-supervised manner. Drawing inspiration from the modeling language that is most commonly used in modern CAD software, we propose to learn 2D sketches and 3D extrusion parameters from raw shapes, from which a set of extrusion cylinders can be generated by extruding each sketch from a 2D plane into a 3D body. By incorporating the Boolean operation (i.e., union), these cylinders can be combined to closely approximate the target geometry. We advocate the use of implicit fields for sketch representation, which allows for creating CAD variations by interpolating latent codes in the sketch latent space. Extensive experiments on both ABC and Fusion 360 datasets demonstrate the effectiveness of our method, and show superiority over state-of-the-art alternatives including the closely related method for supervised CAD reconstruction. We further apply our approach to CAD editing and single-view CAD reconstruction. Code will be released at https://***/BunnySoCrazy/SECAD-Net.

关键词： vision + graphics

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Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Style

Zero-Shot Everything Sketch-Based Image Retrieval, and in Ex...

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

作者： Lin, Fengyin Li, Mingkang Li, Da Hospedales, Timothy Song, Yi-Zhe Qi, Yonggang Beijing Univ Posts & Telecommun Beijing Peoples R China Samsung AI Ctr Cambridge England Univ Edinburgh Edinburgh Midlothian Scotland Univ Surrey SketchX CVSSP Guildford Surrey England

ISBN: (纸本)9798350301298

This paper studies the problem of zero-short sketch-based image retrieval (ZS-SBIR), however with two significant differentiators to prior art (i) we tackle all variants (inter-category, intra-category, and cross datasets) of ZS-SBIR with just one network ("everything"), and (ii) we would really like to understand how this sketch-photo matching operates ("explainable"). Our key innovation lies with the realization that such a cross-modal matching problem could be reduced to comparisons of groups of key local patches - akin to the seasoned "bag-of-words" paradigm. Just with this change, we are able to achieve both of the aforementioned goals, with the added benefit of no longer requiring external semantic knowledge. Technically, ours is a transformer-based cross-modal network, with three novel components (i) a self-attention module with a learnable tokenizer to produce visual tokens that correspond to the most informative local regions, (ii) a cross-attention module to compute local correspondences between the visual tokens across two modalities, and finally (iii) a kernel-based relation network to assemble local putative matches and produce an overall similarity metric for a sketch-photo pair. Experiments show ours indeed delivers superior performances across all ZS-SBIR settings. The all important explainable goal is elegantly achieved by visualizing cross-modal token correspondences, and for the first time, via sketch to photo synthesis by universal replacement of all matched photo patches. Code and model are available at https://***/buptLinfy/ZSE-SBIR.

关键词： detection recognition: Categorization retrieval

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VINDLU : A Recipe for Effective Video-and-Language Pretraining

VINDLU : A Recipe for Effective Video-and-Language Pretraini...

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

作者： Cheng, Feng Wang, Xizi Lei, Jie Crandall, David Bansal, Mohit Bertasius, Gedas UNC Chapel Hill Chapel Hill NC 27599 USA Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9798350301298

The last several years have witnessed remarkable progress in video-and-language (VidL) understanding. However, most modern VidL approaches use complex and specialized model architectures and sophisticated pretraining protocols, making the reproducibility, analysis and comparisons of these frameworks difficult. Hence, instead of proposing yet another new VidL model, this paper conducts a thorough empirical study demystifying the most important factors in the VidL model design. Among the factors that we investigate are (i) the spatiotemporal architecture design, (ii) the multimodal fusion schemes, (iii) the pretraining objectives, (iv) the choice of pretraining data, (v) pretraining and finetuning protocols, and (vi) dataset and model scaling. Our empirical study reveals that the most important design factors include: temporal modeling, video-to-text multimodal fusion, masked modeling objectives, and joint training on images and videos. Using these empirical insights, we then develop a step-by-step recipe, dubbed VINDLU, for effective VidL pretraining. Our final model trained using our recipe achieves comparable or better than state-of-the-art results on several VidL tasks without relying on external CLIP pretraining. In particular, on the text-to-video retrieval task, our approach obtains 61.2% on DiDeMo, and 55.0% on ActivityNet, outperforming current SOTA by 7.8% and 6.1% respectively. Furthermore, our model also obtains state-of-the-art video question-answering results on ActivityNet-QA, MSRVTT-QA, MSRVTT-MC and TVQA. Our code and pretrained models are publicly available at: https://***/klauscc/VindLU.

关键词： language reasoning vision

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VectorFloorSeg: Two-Stream Graph Attention Network for Vectorized Roughcast Floorplan Segmentation

VectorFloorSeg: Two-Stream Graph Attention Network for Vecto...

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

作者： Yang, Bingchen Jiang, Haiyong Pan, Hao Xiao, Jun Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

Vector graphics (VG) are ubiquitous in industrial designs. In this paper, we address semantic segmentation of a typical VG, i.e., roughcast floorplans with bare wall structures, whose output can be directly used for further applications like interior furnishing and room space modeling. Previous semantic segmentation works mostly process well-decorated floorplans in raster images and usually yield aliased boundaries and outlier fragments in segmented rooms, due to pixel-level segmentation that ignores the regular elements (e.g. line segments) in vector floorplans. To overcome these issues, we propose to fully utilize the regular elements in vector floorplans for more integral segmentation. Our pipeline predicts room segmentation from vector floorplans by dually classifying line segments as room boundaries, and regions partitioned by line segments as room segments. To fully exploit the structural relationships between lines and regions, we use two-stream graph neural networks to process the line segments and partitioned regions respectively, and devise a novel modulated graph attention layer to fuse the heterogeneous information from one stream to the other. Extensive experiments show that by directly operating on vector floorplans, we outperform image-based methods in both mIoU and mAcc. In addition, we propose a new metric that captures room integrity and boundary regularity, which confirms that our method produces much more regular segmentations. Source code is available at https://***/DrZiji/VecFloorSeg.

关键词： vision + graphics

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HaGRID - HAnd Gesture recognition Image Dataset

HaGRID - HAnd Gesture Recognition Image Dataset

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Alexander, Kapitanov Karina, Kvanchiani Alexander, Nagaev Roman, Kraynov Andrei, Makhliarchuk SaluteDevices Moscow Russia

ISBN: (纸本)9798350318920;9798350318937

This paper introduces an enormous dataset, HaGRID (HAnd Gesture recognition Image Dataset), to build a hand gesture recognition (HGR) system concentrating on interaction with devices to manage them. That is why all 18 chosen gestures are endowed with the semiotic function and can be interpreted as a specific action. Although the gestures are static, they were picked up, especially for the ability to design several dynamic gestures. It allows the trained model to recognize not only static gestures such as "like" and "stop" but also "swipes" and "drag and drop" dynamic gestures. The HaGRID contains 554,800 images and bounding box annotations with gesture labels to solve hand detection and gesture classification tasks. The low variability in context and subjects of other datasets was the reason for creating the dataset without such limitations. Utilizing crowdsourcing platforms allowed us to collect samples recorded by 37,583 subjects in at least as many scenes with subject-to-camera distances from 0.5 to 4 meters in various natural light conditions. The influence of the diversity characteristics was assessed in ablation study experiments. Also, we demonstrate the HaGRID ability to be used for pretraining models in HGR tasks. The HaGRID and pre-trained models are publicly available(12).

关键词： Algorithms Algorithms Algorithms Biometrics body pose Datasets and evaluations face gesture Image recognition and understanding

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Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning

Multiple Instance Learning via Iterative Self-Paced Supervis...

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

作者： Liu, Kangning Zhu, Weicheng Shen, Yiqiu Liu, Sheng Razavian, Narges Geras, Krzysztof J. Fernandez-Granda, Carlos NYU Ctr Data Sci New York NY 10003 USA NYU Grossman Sch Med New York NY 10003 USA Courant Inst Math Sci New York NY USA

ISBN: (纸本)9798350301298

Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown promising results using contrastive self-supervised learning (CSSL), which learns to push apart representations corresponding to two different randomly-selected instances. Unfortunately, in real-world applications such as medical image classification, there is often class imbalance, so randomly-selected instances mostly belong to the same majority class, which precludes CSSL from learning inter-class differences. To address this issue, we propose a novel framework, Iterative Self-paced Supervised Contrastive Learning for MIL Representations (ItS2CLR), which improves the learned representation by exploiting instance-level pseudo labels derived from the bag-level labels. The framework employs a novel self-paced sampling strategy to ensure the accuracy of pseudo labels. We evaluate ItS2CLR on three medical datasets, showing that it improves the quality of instance-level pseudo labels and representations, and outperforms existing MIL methods in terms of both bag and instance level accuracy. (1)

关键词： cell microscopy Medical and biological vision

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DINN360: Deformable Invertible Neural Network for Latitude-aware 360° Image Rescaling

DINN360: Deformable Invertible Neural Network for Latitude-a...

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

作者： Guo, Yichen Xu, Mai Jiang, Lai Sigal, Leonid Chen, Yunjin Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Univ British Columbia Dept Comp Sci Vancouver BC Canada

ISBN: (纸本)9798350301298

With the rapid development of virtual reality, 360 degrees images have gained increasing popularity. Their wide field of view necessitates high resolution to ensure image quality. This, however, makes it harder to acquire, store and even process such 360 degrees images. To alleviate this issue, we propose the first attempt at 360 degrees image rescaling, which refers to downscaling a 360 degrees image to a visually valid low-resolution (LR) counterpart and then upscaling to a high-resolution (HR) 360 degrees image given the LR variant. Specifically, we first analyze two 360 degrees image datasets and observe several findings that characterize how 360 degrees images typically change along their latitudes. Inspired by these findings, we propose a novel deformable invertible neural network (INN), named DINN360, for latitude-aware 360 degrees image rescaling. In DINN360, a deformable INN is designed to downscale the LR image, and project the high-frequency (HF) component to the latent space by adaptively handling various deformations occurring at different latitude regions. Given the downscaled LR image, the high-quality HR image is then reconstructed in a conditional latitude-aware manner by recovering the structure-related HF component from the latent space. Extensive experiments over four public datasets show that our DINN360 method performs considerably better than other state-of-the-art methods for 2x, 4x and 8x 360 degrees image rescaling.

关键词： Low-level vision

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Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising

Spatially Adaptive Self-Supervised Learning for Real-World I...

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

作者： Li, Junyi Zhang, Zhilu Liu, Xiaoyu Feng, Chaoyu Wang, Xiaotao Lei, Lei Zuo, Wangmeng Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and they have little practicality on real-world sRGB images with spatially correlated noise. Although pixel-shuffle downsampling has been suggested for breaking the noise correlation, it breaks the original information of images, which limits the denoising performance. In this paper, we propose a novel perspective to solve this problem, i.e., seeking for spatially adaptive supervision for real-world sRGB image denoising. Specifically, we take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. For flat areas, the supervision can be safely derived from non-adjacent pixels, which are much far from the current pixel for excluding the influence of the noise-correlated ones. And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas. For textured regions, the supervision has to be closely related to the content of adjacent pixels. And we present a locally aware network (LAN) to meet the requirement, while LAN itself is selectively supervised with the output of BNN. Combining these two supervisions, a denoising network (e.g., U-Net) can be well-trained. Extensive experiments show that our method performs favorably against state-of-the-art SSID methods on real-world sRGB photographs. The code is available at https://***/nagejacob/SpatiallyAdaptiveSSID.

关键词： Low-level vision

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Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation

Bidirectional Copy-Paste for Semi-Supervised Medical Image S...

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

作者： Bai, Yunhao Chen, Duowen Li, Qingli Shen, Wei Wang, Yan East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9798350301298

In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution. The knowledge learned from the labeled data may be largely discarded if treating labeled and unlabeled data separately or in an inconsistent manner. We propose a straightforward method for alleviating the problem - copy-pasting labeled and unlabeled data bidirectionally, in a simple Mean Teacher architecture. The method encourages unlabeled data to learn comprehensive common semantics from the labeled data in both inward and outward directions. More importantly, the consistent learning procedure for labeled and unlabeled data can largely reduce the empirical distribution gap. In detail, we copy-paste a random crop from a labeled image (foreground) onto an unlabeled image (background) and an unlabeled image (foreground) onto a labeled image (background), respectively. The two mixed images are fed into a Student network and supervised by the mixed supervisory signals of pseudo-labels and ground-truth. We reveal that the simple mechanism of copy-pasting bidirectionally between labeled and unlabeled data is good enough and the experiments show solid gains (e.g., over 21% Dice improvement on ACDC dataset with 5% labeled data) compared with other state-of-the-arts on various semi-supervised medical image segmentation datasets. Code is avaiable at https://***/DeepMed-Lab-ECNU/BCP.

关键词： cell microscopy Medical and biological vision

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