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Enriched Robust Multi-View Kernel Subspace Clustering

Enriched Robust Multi-View Kernel Subspace Clustering

引用

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

作者： Zhang, Mengyuan Liu, Kai Clemson Univ Clemson SC 29631 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity learning, multi-view information fusion and clustering. Second, they assume the data lies in a linear subspace which may fail in practice as most real-world datasets may have non-linearity structures. To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering. Due to the objective and constraints which is difficult to optimize, we propose an iterative optimization method which is easy to implement and can yield closed solution in each step. Extensive experiments have validated the superiority of our method over state-of-the-art clustering methods.

关键词： computer vision Clustering methods conferences Optimization methods Benchmark testing Iterative methods Kernel

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Attenuating Catastrophic Forgetting by Joint Contrastive and Incremental Learning

Attenuating Catastrophic Forgetting by Joint Contrastive and...

引用

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

作者： Ferdinand, Quentin Clement, Benoit Oliveau, Quentin Le Chenadec, Gilles Papadakis, Panagiotis Naval Grp Res Cherbourg En Cotentin France ENSTA Bretagne Lab STICC UMR 6285 Brest France IMT Atlantique Lab STICC UMR 6285 Brest France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In class incremental learning, discriminative models are trained to classify images while adapting to new instances and classes incrementally. Training a model to adapt to new classes without total access to previous class data, however, leads to the known problem of catastrophic forgetting of the previously learnt classes. To alleviate this problem, we show how we can build upon recent progress on contrastive learning methods. In particular, we develop an incremental learning approach for deep neural networks operating both at classification and representation level which alleviates forgetting and learns more general features for data classification. Experiments performed on several datasets demonstrate the superiority of the proposed method with respect to well known state-of-the-art methods.

关键词： Learning systems Training Deep learning Adaptation models computer vision conferences Computational modeling

来源：评论

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Unstructured Object Matching using Co-Salient Region Segmentation

Unstructured Object Matching using Co-Salient Region Segment...

引用

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

作者： Stoian, Ioana-Sabina Sandu, Ionut-Catalin Voinea, Daniel Popa, Alin-Ionut Amazon Bucharest Romania

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Unstructured object matching is a less-explored and very challenging topic in the scientific literature. This includes matching scenarios where the context, appearance and the geometrical integrity of the objects to be matched changes drastically from one image to another (e.g. a pair of pyjamas which in one image is folded and in the other is worn by a person), making it impossible to determine a transformation which aligns the matched regions. Traditional approaches like keypoint-based feature matching perform poorly on this use case due to the high complexity in terms of viewpoint, scene context variety, background variations or high degrees of freedom concerning structural configurations. In this paper we propose a deep learning framework consisting of a twins based matching approach leveraging a co-salient region segmentation task and a cosine-similarity based region descriptor pairing technique. The importance of our proposed framework is demonstrated on a novel use case consisting of image pairs with various objects used by children. Additionally, we evaluate on Human3.6M and Market-1501, two datasets with humans depicting various appearances and kinematic configurations captured under different backgrounds.

关键词： Deep learning Image segmentation computer vision conferences Kinematics Search problems pattern recognition

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Alleviating Representational Shift for Continual Fine-tuning

Alleviating Representational Shift for Continual Fine-tuning

引用

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

作者： Jie, Shibo Deng, Zhi-Hong Li, Ziheng Peking Univ Sch Artificial Intelligence Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We study a practical setting of continual learning: fine-tuning on a pre-trained model continually. Previous work has found that, when training on new tasks, the features (penultimate layer representations) of previous data will change, called representational shift. Besides the shift of features, we reveal that the intermediate layers' representational shift (IRS) also matters since it disrupts batch normalization, which is another crucial cause of catastrophic forgetting. Motivated by this, we propose ConFiT, a fine-tuning method incorporating two components, cross-convolution batch normalization (Xconv BN) and hierarchical fine-tuning. Xconv BN maintains pre-convolution running means instead of post-convolution, and recovers post-convolution ones before testing, which corrects the inaccurate estimates of means under IRS. Hierarchical fine-tuning leverages a multi-stage strategy to fine-tune the pre-trained network, preventing massive changes in Conv layers and thus alleviating IRS. Experimental results on four datasets show that our method remarkably outperforms several state-of-the-art methods with lower storage overhead.

关键词： Training computer vision conferences pattern recognition Task analysis Testing

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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/CVF conference on computer vision and pattern recognition (CVPR)

作者： Specker, Andreas Florin, Lucas Cormier, Mickael Beyerer, Juergen Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning St Augustin Germany

ISBN: (纸本)9781665487399

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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Ice hockey player identification via transformers and weakly supervised learning

Ice hockey player identification via transformers and weakly...

引用

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

作者： Vats, Kanav McNally, William Walters, Pascale Clausi, David A. Zelek, John S. Univ Waterloo Syst Design Engn Waterloo ON Canada Stathletes Inc St Catharines ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Identifying players in video is a foundational step in computer vision-based sports analytics. Obtaining player identities is essential for analyzing the game and is used in downstream tasks such as game event recognition. Transformers are the existing standard in natural language processing (NLP) and are swiftly gaining traction in computer vision. Motivated by the increasing success of transformers in computer vision, we introduce a transformer network for recognizing players through their jersey numbers in broadcast National Hockey League (NHL) videos. The transformer takes temporal sequences of player frames (called player tracklets) as input and outputs the probabilities of jersey numbers present in the frames. The proposed network performs better than the previous benchmark on the same dataset. We implement a weakly-supervised training approach by generating approximate frame-level labels for jersey number presence and use the frame-level labels for faster training. We also utilize player shifts available in the NHL play-by-play data by reading the game time using optical character recognition (OCR) to get the players on the ice rink at a certain game time. Using player-shifts improved the player identification accuracy by 6%.

关键词： Training computer vision Optical character recognition Supervised learning Games Transformers Ice

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Lidar Positioning for Indoor Precision Navigation

Lidar Positioning for Indoor Precision Navigation

引用

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

作者： Holmberg, Max Karlsson, Oskar Tulldahl, Michael Swedish Def Res Agcy FOI Linkoping Sweden

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Lidar based simultaneous localization and mapping methods can be adapted for deployment on small autonomous vehicles operating in unmapped indoor environments. For this purpose, we propose a method which combines inertial data, low-drift lidar odometry, planar primitives, and loop closing in a graph-based structure. The accuracy of our method is experimentally evaluated, using a high-resolution lidar, and compared to the state-of-the-art methods LIO-SAM and Cartographer. We specifically address the lateral positioning accuracy when passing through narrow openings, where high accuracy is a prerequisite for safe operation of autonomous vehicles. The test cases include doorways, slightly wider reference passages, and a larger corridor environment. We observe a reduced lateral accuracy for all three methods when passing through the narrow openings compared to operation in larger spaces. Compared to state-of-the-art, our method shows better results in the narrow passages, and comparable results in the other environments with reasonably low usage of CPU and memory resources.

关键词： computer vision Laser radar Simultaneous localization and mapping Navigation conferences Random access memory pattern recognition

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Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels

Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Se...

引用

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

作者： Kalluri, Tarun Chandraker, Manmohan Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Domain adaptation for semantic segmentation across datasets consisting of the same categories has seen several recent successes. However, a more general scenario is when the source and target datasets correspond to non-overlapping label spaces. For example, categories in segmentation datasets change vastly depending on the type of environment or application, yet share many valuable semantic relations. Existing approaches based on feature alignment or discrepancy minimization do not take such category shift into account. In this work, we present Cluster-to-Adapt (C2A), a computationally efficient clustering-based approach for domain adaptation across segmentation datasets with completely different, but possibly related categories. We show that such a clustering objective enforced in a transformed feature space serves to automatically select categories across source and target domains that can be aligned for improving the target performance, while preventing negative transfer for unrelated categories. We demonstrate the effectiveness of our approach through experiments on the challenging problem of outdoor to indoor adaptation for semantic segmentation in few-shot as well as zero-shot settings, with consistent improvements in performance over existing approaches and baselines in all cases.

关键词： Training Image segmentation computer vision conferences Semantics Minimization pattern recognition

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Unsupervised Change Detection Based on Image Reconstruction Loss

Unsupervised Change Detection Based on Image Reconstruction ...

引用

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

作者： Noh, Hyeoncheol Ju, Jingi Seo, Minseok Park, Jongchan Choi, Dong-Geol Hanbat Natl Univ Daejeon South Korea SI Analyt Inc Mainz Germany Lunit Inc Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

To train a change detector, bi-temporal images taken at different times in the same area are used. However, collecting labeled bi-temporal images is expensive and time consuming. To solve this problem, various unsupervised change detection methods have been proposed, but they still require unlabeled bi-temporal images. In this paper, we propose an unsupervised change detection method based on image reconstruction loss, which uses only a single-temporal unlabeled image. The image reconstruction model was trained to reconstruct the original source image by receiving the source image and photometrically transformed source image as a pair. During inference, the model receives bi-temporal images as input and aims to reconstruct one of the inputs. The changed region between bi-temporal images shows high reconstruction loss. Our change detector demonstrated significant performance on various change detection benchmark datasets even though only a single-temporal source image was used. The code and trained models are available in https://***/cjf8899/CDRL

关键词： computer vision Codes conferences Semantics Detectors Benchmark testing pattern recognition

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Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Multi-Scale Aggregation and Anthropic Prior Knowledge

Teeth-SEG: An Efficient Instance Segmentation Framework for ...

引用

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

作者： Zou, Bo Wang, Shaofeng Liu, Hao Sun, Gaoyue Wang, Yajie Zuo, FeiFei Quan, Chengbin Zhaot, Youjian Tsinghua Univ Beijing Peoples R China Capital Med Univ Beijing Peoples R China Imperial Coll London London England LargeV Inc Beijing Peoples R China Tsinghua Univ Zhongguancun Lab Beijing Peoples R China

ISBN: (纸本)9798350353006

Teeth localization, segmentation, and labeling in 2D images have great potential in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, general instance segmentation frameworks are incompetent due to 1) the subtle differences between some teeth' shapes ( e.g., maxillary first premolar and second premolar), 2) the teeth's position and shape variation across subjects, and 3) the presence of abnormalities in the dentition (e.g., caries and edentulism). To address these problems, we propose a ViT-based framework named TeethSEG, which consists of stacked Multi-Scale Aggregation (MSA) blocks and an Anthropic Prior Knowledge (APK) layer. Specifically, to compose the two modules, we design a unique permutation-based upscaler to ensure high efficiency while establishing clear segmentation boundaries with multi-head self/cross-gating layers to emphasize particular semantics meanwhile maintaining the divergence between token embeddings. Besides, we collect the first open-sourced intraoral image dataset IO150K, which comprises over 150k intraoral photos, and all photos are annotated by orthodontists using a human-machine hybrid algorithm. Experiments on IO150K demonstrate that our TeethSEG outperforms the state-of-the-art segmentation models on dental image segmentation.

关键词： Image Segmentation Intra-Oral Image Medical vision Teeth Segmentation

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