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Feature Erasing and Diffusion Network for Occluded Person Re-Identification

Feature Erasing and Diffusion Network for Occluded Person Re...

引用

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

作者： Wang, Zhikang Zhu, Feng Tang, Shixiang Zhao, Rui He, Lihuo Song, Jiangning Monash Univ Clayton Vic Australia SenseTime Res Hong Kong Peoples R China Univ Sydney Sydney NSW Australia Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China Xidian Univ Xian Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Occluded person re-identification (ReID) aims at matching occluded person images to holistic ones across different camera views. Target Pedestrians (TP) are often disturbed by Non-Pedestrian Occlusions (NPO) and Non-Target Pedestrians (NTP). Previous methods mainly focus on increasing the model's robustness against NPO while ignoring feature contamination from NTP. In this paper, we propose a novel Feature Erasing and Diffusion Network (FED) to simultaneously handle challenges from NPO and NTP. Specifically, aided by the NPO augmentation strategy that simulates NPO on holistic pedestrian images and generates precise occlusion masks, NPO features are explicitly eliminated by our proposed Occlusion Erasing Module (OEM). Subsequently, we diffuse the pedestrian representations with other memorized features to synthesize the NTP characteristics in the feature space through the novel Feature Diffusion Module (FDM). With the guidance of the occlusion scores from OEM, the feature diffusion process is conducted on visible body parts, thereby improving the quality of the synthesized NTP characteristics. We can greatly improve the model's perception ability towards TP and alleviate the influence of NPO and NTP by jointly optimizing OEM and FDM. Furthermore, the proposed FDM works as an auxiliary module for training and will not be engaged in the inference phase, thus with high flexibility. Experiments on occluded and holistic person ReID benchmarks demonstrate the superiority of FED over state-of-the-art methods.

关键词： Training Representation learning Deep learning computer vision Diffusion processes Benchmark testing Robustness

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Encapsulating the impact of transfer learning, domain knowledge and training strategies in deep-learning based architecture: A biometric based case study 31

Encapsulating the impact of transfer learning, domain knowle...

引用

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

作者： Singh, Avantika Nigam, Aditya Indian Inst Technol Mandi Mandi India

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

In this paper, efforts have been made to analyze the impact of training strategies, transfer learning and domain knowledge on two biometric-based problems namely: three class oculus classification and fingerprint sensor classification. For analyzing these problems we have considered deep-learning based architecture and evaluated our results on benchmark contact-lens datasets like IIIT-D, ND, IIT-K ( our model is publicly available) and on fingerprint datasets like FVC-2002, FVC-2004, FVC-2006, IIITD-MOLF, IIT-K. In-depth feature analysis of various proposed deep-learning models has been done in order to infer that indeed training in different ways along with transfer learning and domain knowledge plays a vital role in deciding the learning ability of any network.

关键词： Lenses Iris recognition Training Biological system modeling Knowledge engineering pattern recognition Analytical models

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Modeling Relationships in Referential Expressions with Compositional Modular Networks 30

Modeling Relationships in Referential Expressions with Compo...

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30th ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hu, Ronghang Rohrbach, Marcus Andreas, Jacob Darrell, Trevor Saenko, Kate Univ Calif Berkeley Berkeley CA 94720 USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781538604571

People often refer to entities in an image in terms of their relationships with other entities. For example, the black cat sitting under the table refers to both a black cat entity and its relationship with another table entity. Understanding these relationships is essential for interpreting and grounding such natural language expressions. Most prior work focuses on either grounding entire referential expressions holistically to one region, or localizing relationships based on a fixed set of categories. In this paper we instead present a modular deep architecture capable of analyzing referential expressions into their component parts, identifying entities and relationships mentioned in the input expression and grounding them all in the scene. We call this approach Compositional Modular Networks (CMNs): a novel architecture that learns linguistic analysis and visual inference end-to-end. Our approach is built around two types of neural modules that inspect local regions and pairwise interactions between regions. We evaluate CMNs on multiple referential expression datasets, outperforming state-of-the-art approaches on all tasks.

关键词： Visualization Grounding Feature extraction Natural languages computer vision Cats

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Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning

Rethinking Class Relations: Absolute-relative Supervised and...

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

作者： Zhang, Hongguang Koniusz, Piotr Jian, Songlei Li, Hongdong Torr, Philip H. S. AMS Syst Engn Inst Beijing Peoples R China Australian Natl Univ Canberra ACT Australia Data61 CSIRO Sydney NSW Australia Univ Oxford Oxford England Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781665445092

The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only the similarity via relation labels, but they are not exposed to class concepts associated with objects, which is likely detrimental to the classification performance due to underutilization of the available class labels. For instance, children learn the concept of tiger from a few of actual examples as well as from comparisons of tiger to other animals. Thus, we hypothesize that both similarity and class concept learning must be occurring simultaneously. With these observations at hand, we study the fundamental problem of simplistic class modeling in current few-shot learning methods. We rethink the relations between class concepts, and propose a novel Absolute-relative Learning paradigm to fully take advantage of label information to refine the image an relation representations in both supervised and unsupervised scenarios. Our proposed paradigm improves the performance of several state-of-the-art models on publicly available datasets.

关键词： Training Learning systems computer vision Protocols Annotations Animals Semantics

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φ-SfT: Shape-from-Template with a Physics-Based Deformation Model

φ-SfT: Shape-from-Template with a Physics-Based Deformation...

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

作者： Kairanda, Navami Tretschk, Edith Elgharib, Mohamed Theobalt, Christian Golyanik, Vladislav Max Planck Inst Informat SIC Saarbrucken Germany Saarland Univ SIC Saarbrucken Germany

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Shape-from-Template (SIT) methods estimate 3D surface deformations from a single monocular RGB camera while assuming a 3D state known in advance (a template). This is an important yet challenging problem due to the under-constrained nature of the monocular setting. Existing SfT techniques predominantly use geometric and simplified deformation models, which often limits their reconstruction abilities. In contrast to previous works, this paper proposes a new SfT approach explaining 2D observations through physical simulations accounting for forces and material properties. Our differentiable physics simulator regularises the surface evolution and optimises the material elastic properties such as bending coefficients, stretching stiffness and density. We use a differentiable renderer to minimise the dense reprojection error between the estimated 3D states and the input images and recover the deformation parameters using an adaptive gradient-based optimisation. For the evaluation, we record with an RGB-D camera challenging real surfaces exposed to physical forces with various material properties and textures. Our approach significantly reduces the 3D reconstruction error compared to multiple competing methods.

关键词： Deformable models Surface reconstruction Three-dimensional displays Cameras Rendering (computer graphics) Data models Surface texture

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Two-stage Network For Single Image Super-Resolution

Two-stage Network For Single Image Super-Resolution

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

作者： Han, Yuzhuo Du, Xiaobiao Yang, Zhi Dalian Univ Technol Dalian Peoples R China Jilin Univ Zhuhai Coll Zhuhai Peoples R China Dibaocheng Shanghai Med Imaging Technol Co Ltd Shanghai Peoples R China

ISBN: (纸本)9781665448994

The task of single-image super-resolution (SISR) is a highly inverse problem because it is very challenging to reconstruct rich details from blurred images. Most previous super-resolution (SR) methods based on the convolutional neural networks (CNN) tend to design more complex network structures to directly learn the mapping between low-resolution images and high-resolution images. However, this is not the best choice to blindly increase the network depth, because the performance improvement may not increase, but it will increase the computational cost. To solve this problem, we propose an effective method that learns high frequency information in high-resolution images to enhance the image reconstruction. In this work, we propose a two-stage network (TSN) to recover clear SR images. The proposed TSN firstly learns the high frequency information in high-resolution images, then learns how to transform to high-resolution images. A large number of experiments show that our TSN achieves satisfactory performance.

关键词： computer vision Inverse problems conferences Computational modeling Superresolution Transforms pattern recognition

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HEAL-SWIN: A vision Transformer On The Sphere

HEAL-SWIN: A Vision Transformer On The Sphere

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

作者： Carlsson, Oscar Gerken, Jan E. Linander, Hampus Spiess, Heiner Ohlsson, Fredrik Petersson, Christoffer Persson, Daniel Univ Gothenburg Chalmers Univ Technol Dept Math Sci SE-41296 Gothenburg Sweden Tech Univ Berlin Neural Informat Proc Sci Intelligence DE-10623 Berlin Germany Umea Univ Dept Math & Math Stat Umea degrees SE-90187 Umea Sweden Zenseact SE-41756 Gothenburg Sweden

ISBN: (纸本)9798350353013;9798350353006

High-resolution wide-angle fisheye images are becoming more and more important for robotics applications such as autonomous driving. However, using ordinary convolutional neural networks or vision transformers on this data is problematic due to projection and distortion losses introduced when projecting to a rectangular grid on the plane. We introduce the HEAL-SWIN transformer, which combines the highly uniform Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid used in astrophysics and cosmology with the Hierarchical Shifted-Window (SWIN) transformer to yield an efficient and flexible model capable of training on high-resolution, distortion-free spherical data. In HEAL-SWIN, the nested structure of the HEALPix grid is used to perform the patching and windowing operations of the SWIN transformer, enabling the network to process spherical representations with minimal computational overhead. We demonstrate the superior performance of our model on both synthetic and real automotive datasets, as well as a selection of other image datasets, for semantic segmentation, depth regression and classification tasks. Our code is publicly available(1).

关键词： depth estimation fisheye images image classification omni-directional images semantic segmentation spherical grid transformer

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Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds...

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

作者： Hu, Qingyong Yang, Bo Khalid, Sheikh Xiao, Wen Trigoni, Niki Markham, Andrew Univ Oxford Oxford England Hong Kong Polytech Univ Hong Kong Peoples R China Sensat Ltd London England Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781665445092

An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. However, publicly available datasets are either in relative small spatial scales or have limited semantic annotations due to the expensive cost of data acquisition and data annotation, which severely limits the development of fine-grained semantic understanding in the context of 3D point clouds. In this paper, we present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is three times the number of labeled points than the existing largest photogrammetric point cloud dataset. Our dataset consists of large areas from three UK cities, covering about 7.6 km(2) of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes. We extensively evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results. In particular;we identify several key challenges towards urban-scale point cloud understanding. The dataset is available at https://***/QingyongHu/SensatUrban.

关键词： Deep learning computer vision Three-dimensional displays Costs Annotations Semantics Urban areas

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Anchor-based Robust Finetuning of vision-Language Models

Anchor-based Robust Finetuning of Vision-Language Models

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

作者： Han, Jinwei Lin, Zhiwen Sun, Zhongyisun Gao, Yingguo Yan, Ke Ding, Shouhong Gao, Yuan Xia, Gui-Song Wuhan Univ Sch Comp Sci Wuhan Peoples R China Tencent YouTu Lab Shenzhen Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China

ISBN: (纸本)9798350353006

We aim at finetuning a vision-language model without hurting its out-of-distribution (OOD) generalization. We address two types of OOD generalization, i.e., i) domain shift such as natural to sketch images, and ii) zero-shot capability to recognize the category that was not contained in the finetune data. Arguably, the diminished OOD generalization after finetuning stems from the excessively simplified finetuning target, which only provides the class information, such as "a photo of a [CLASS]". This is distinct from the process in that CLIP was pretrained, where there is abundant text supervision with rich semantic information. Therefore, we propose to compensate for the finetune process using auxiliary supervision with rich semantic information, which acts as anchors to preserve the OOD generalization. Specifically, two types of anchors are elaborated in our method, including i) text-compensated anchor which uses the images from the finetune set but enriches the text supervision from a pretrained captioner, ii) image-text-pair anchor which is retrieved from the dataset similar to pre-training data of CLIP according to the downstream task, associating with the original CLIP text with rich semantics. Those anchors are utilized as auxiliary semantic information to maintain the original feature space of CLIP, thereby preserving the OOD generalization capabilities. Comprehensive experiments demonstrate that our method achieves in-distribution performance akin to conventional finetuning while attaining new state-of-the-art results on domain shift and zero-shot learning benchmarks.

关键词： Anchor Robust Finetuning

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RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding

RegionPLC: Regional Point-Language Contrastive Learning for ...

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

作者： Yang, Jihan Ding, Runyu Deng, Weipeng Wang, Zhe Qi, Xiaojuan Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9798350353006

We propose a lightweight and scalable Regional Point-Language Contrastive learning framework, namely RegionPLC, for open-world 3D scene understanding, aiming to identify and recognize open-set objects and categories. Specifically, based on our empirical studies, we introduce a 3D-aware SFusion strategy that fuses 3D vision-language pairs derived from multiple 2D foundation models, yielding high-quality, dense region-level language descriptions without human 3D annotations. Subsequently, we devise a region-aware point-discriminative contrastive learning objective to enable robust and effective 3D learning from dense regional language supervision. We carry out extensive experiments on ScanNet, ScanNet200, and nuScenes datasets, and our model outperforms prior 3D open-world scene understanding approaches by an average of 17.2% and 9.1% for semantic and instance segmentation, respectively, while maintaining greater scalability and lower resource demands. Furthermore, our method has the flexibility to be effortlessly integrated with language models to enable open-ended grounded 3D reasoning without extra task-specific training. Code will be released at github.

关键词： 3D vision embodied AI Open-world

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