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Feature Erasing and Diffusion Network for Occluded Person Re-Identification

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

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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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Scene Graph Expansion for Semantics-Guided Image Outpainting

Scene Graph Expansion for Semantics-Guided Image Outpainting

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

作者： Yang, Chiao-An Tan, Cheng-Yo Fan, Wan-Cyuan Yang, Cheng-Fu Wu, Meng-Lin Wang, Yu-Chiang Frank Natl Taiwan Univ New Taipei Taiwan Qualcomm Technol Inc San Diego CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we address the task of semantics-guided image outpainting, which is to complete an image by generating semantically practical content. Different from most existing image outpainting works, we approach the above task by understanding and completing image semantics at the scene graph level. In particular, we propose a novel network of Scene Graph Transformer (SGT), which is designed to take node and edge features as inputs for modeling the associated structural information. To better understand and process graph-based inputs, our SGT uniquely performs feature attention at both node and edge levels. While the former views edges as relationship regularization, the latter observes the co-occurrence of nodes for guiding the attention process. We demonstrate that, given a partial input image with its layout and scene graph, our SGT can be applied for scene graph expansion and its conversion to a complete layout. Following state-of-the-art layout-to-image conversions works, the task of image outpainting can be completed with sufficient and practical semantics introduced. Extensive experiments are conducted on the datasets of MS-COCO and Visual Genome, which quantitatively and qualitatively confirm the effectiveness of our proposed SGT and outpainting frameworks.

关键词： Visualization computer vision Image edge detection Computational modeling Semantics Layout Genomics

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Sketch, Ground, and Refine: Top-Down Dense Video Captioning

Sketch, Ground, and Refine: Top-Down Dense Video Captioning

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

作者： Deng, Chaorui Chen, Shizhe Chen, Da He, Yuan Wu, Qi Univ Adelaide Adelaide SA Australia INRIA Rocquencourt France Alibaba Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

The dense video captioning task aims to detect and describe a sequence of events in a video for detailed and coherent storytelling. Previous works mainly adopt a "detect-then-describe" framework, which firstly detects event proposals in the video and then generates descriptions for the detected events. However, the definitions of events are diverse which could be as simple as a single action or as complex as a set of events, depending on different semantic contexts. Therefore, directly detecting events based on video information is ill-defined and hurts the coherency and accuracy of generated dense captions. In this work, we reverse the predominant "detect-then-describe" fashion, proposing a top-down way to first generate paragraphs from a global view and then ground each event description to a video segment for detailed refinement. It is formulated as a Sketch, Ground, and Refine process (SGR). The sketch stage first generates a coarse-grained multi-sentence paragraph to describe the whole video, where each sentence is treated as an event and gets localised in the grounding stage. In the refining stage, we improve captioning quality via refinement-enhanced training and dual-path cross attention on both coarse-grained event captions and aligned event segments. The updated event caption can further adjust its segment boundaries. Our SGR model outperforms state-of-the-art methods on ActivityNet Captioning benchmark under traditional and story-oriented dense caption evaluations.

关键词： Training computer vision Codes Grounding Computational modeling Semantics Benchmark testing

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Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection

Segment and Complete: Defending Object Detectors against Adv...

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

作者： Liu, Jiang Levine, Alexander Lau, Chun Pong Chellappa, Rama Feizi, Soheil Johns Hopkins Univ Baltimore MD 21218 USA Univ Maryland College Pk MD 20742 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Object detection plays a key role in many security-critical systems. Adversarial patch attacks, which are easy to implement in the physical world, pose a serious threat to state-of-the-art object detectors. Developing reliable defenses for object detectors against patch attacks is critical but severely understudied. In this paper, we propose Segment and Complete defense (SAC), a general framework for defending object detectors against patch attacks through detection and removal of adversarial patches. We first train a patch segmenter that outputs patch masks which provide pixel-level localization of adversarial patches. We then propose a self adversarial training algorithm to robustify the patch segmenter. In addition, we design a robust shape completion algorithm, which is guaranteed to remove the entire patch from the images if the outputs of the patch segmenter are within a certain Hamming distance of the ground-truth patch masks. Our experiments on COCO and xView datasets demonstrate that SAC achieves superior robustness even under strong adaptive attacks with no reduction in performance on clean images, and generalizes well to unseen patch shapes, attack budgets, and unseen attack methods. Furthermore, we present the APRICOT-Mask dataset, which augments the APRICOT dataset with pixel-level annotations of adversarial patches. We show SAC can significantly reduce the targeted attack success rate of physical patch attacks. Our code is available at https://***/joellliu/SegmentAndComplete.

关键词： Training Location awareness Image segmentation computer vision Shape Detectors Object detection

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Semi-Supervised Few-shot Learning via Multi-Factor Clustering

Semi-Supervised Few-shot Learning via Multi-Factor Clusterin...

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

作者： Ling, Jie Liao, Lei Yang, Meng Shuai, Jia Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp SYSU Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The scarcity of labeled data and the problem of model overfitting have been the challenges in few-shot learning. Recently, semi-supervised few-shot learning has been developed to obtain pseudo-labels of unlabeled samples for expanding the support set. However, the relationship between unlabeled and labeled data is not well exploited in generating pseudo labels, the noise of which will directly harm the model learning. In this paper, we propose a Clustering-based semi-supervised Few-Shot Learning (cluster-FSL) method to solve the above problems in image classification. By using multi-factor collaborative representation, a novel Multi-Factor Clustering (MFC) is designed to fuse the information of few-shot data distribution, which can generate soft and hard pseudo-labels for unlabeled samples based on labeled data. And we exploit the pseudo labels of unlabeled samples by MFC to expand the support set for obtaining more distribution information. Furthermore, robust data augmentation is used for support set in the fine-tuning phase to increase the labeled samples' diversity. We verified the validity of the cluster-FSL by comparing it with other few-shot learning methods on three popular benchmark datasets, miniImageNet, tieredImageNet, and CUB-200-2011. The ablation experiments further demonstrate that our MFC can effectively fuse distribution information of labeled samples and provide high-quality pseudo-labels. Our code is available at: https://***/smartllvlab/cluster-fsl

关键词： Manifolds Learning systems computer vision Codes Fuses Collaboration Benchmark testing

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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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Scan2CAD: Learning CAD Model Alignment in RGB-D Scans 32

Scan2CAD: Learning CAD Model Alignment in RGB-D Scans

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32nd ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Avetisyan, Armen Dahnert, Manuel Dai, Angela Savva, Manolis Chang, Angel X. Niessner, Matthias Tech Univ Munich Munich Germany Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781728132938

We present Scan2CAD, a novel data-driven method that learns to align clean 3D CAD models from a shape database to the noisy and incomplete geometry of a commodity RGB-D scan. For a 3D reconstruction of an indoor scene, our method takes as input a set of CAD models, and predicts a 9DoF pose that aligns each model to the underlying scan geometry. To tackle this problem, we create a new scan-to-CAD alignment dataset based on 1506 ScanNet scans with 97607 annotated keypoint pairs between 14225 CAD models from ShapeNet and their counterpart objects in the scans. Our method selects a set of representative keypoints in a 3D scan for which we find correspondences to the CAD geometry. To this end, we design a novel 3D CNN architecture that learns a joint embedding between real and synthetic objects, and from this predicts a correspondence heatmap. Based on these correspondence heatmaps, we formulate a variational energy minimization that aligns a given set of CAD models to the reconstruction. We evaluate our approach on our newly introduced Scan2CAD benchmark where we outperform both handcrafted feature descriptor as well as state-of-the-art CNN based methods by 21.39%.

关键词： Categorization recognition: Detection Retrieval Scene Analysis and Understanding vision + Graphics

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Adversarial Normalization: I Can visualize Everything (ICE)

Adversarial Normalization: I Can visualize Everything (ICE)

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

作者： Choi, Hoyoung Jin, Seungwan Han, Kyungsik Hanyang Univ Seoul South Korea

ISBN: (纸本)9798350301298

vision transformers use [CLS] tokens to predict image classes. Their explainability visualization has been studied using relevant information from [CLS] tokens or focusing on attention scores during self-attention. Such visualization, however, is challenging because of the dependence of the structure of a vision transformer on skip connections and attention operators, the instability of non-linearities in the learning process, and the limited reflection of self-attention scores on relevance. We argue that the output vectors for each input patch token in a vision transformer retain the image information of each patch location, which can facilitate the prediction of an image class. In this paper, we propose ICE (Adversarial Normalization: I Can visualize Everything), a novel method that enables a model to directly predict a class for each patch in an image;thus, advancing the effective visualization of the explainability of a vision transformer. Our method distinguishes background from foreground regions by predicting background classes for patches that do not determine image classes. We used the DeiT-S model, the most representative model employed in studies, on the explainability visualization of vision transformers. On the ImageNet-Segmentation dataset, ICE outperformed all explainability visualization methods for four cases depending on the model size. We also conducted quantitative and qualitative analyses on the tasks of weakly-supervised object localization and unsupervised object discovery. On the CUB200-2011 and PASCALVOC07/12 datasets, ICE achieved comparable performance to the state-of-the-art methods. We incorporated ICE into the encoder of DeiT-S and improved efficiency by 44.01% on the ImageNet dataset over that achieved by the original DeiT-S model. We showed performance on the accuracy and efficiency comparable to EViT, the state-of-the-art pruning model, demonstrating the effectiveness of ICE. The code is available at https://***/Hanyang-HCC-La

关键词： Explainable computer vision

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BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster

BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to ...

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

作者： Dai, Jason (Jinquan) Ding, Ding Shi, Dongjie Huang, Shengsheng Wang, Jiao Qiu, Xin Huang, Kai Song, Guoqiong Wang, Yang Gong, Qiyuan Song, Jiaming Yu, Shan Zheng, Le Chen, Yina Deng, Junwei Song, Ge Intel Santa Clara CA 95054 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Most AI projects start with a Python notebook running on a single laptop;however, one usually needs to go through a mountain of pains to scale it to handle larger dataset (for both experimentation and production deployment). These usually entail many manual and error-prone steps for the data scientists to fully take advantage of the available hardware resources (e.g., SIMD instructions, multi-processing, quantization, memory allocation optimization, data partitioning, distributed computing, etc.). To address this challenge, we have open sourced BigDL 2.0 at https://***/intel-analytics/BigDL/ under Apache 2.0 license (combining the original BigDL [19] and Analytics Zoo [18] projects);using BigDL 2.0, users can simply build conventional Python notebooks on their laptops (with possible AutoML support), which can then be transparently accelerated on a single node (with up-to 9.6x speedup in our experiments), and seamlessly scaled out to a large cluster (across several hundreds servers in real-world use cases). BigDL 2.0 has already been adopted by many real-world users (such as Mastercard, Burger King, Inspur, etc.) in production.

关键词： Portable computers Quantization (signal) Pipelines Distributed databases Production Credit cards pattern recognition

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Motion-from-Blur: 3D Shape and Motion Estimation of Motion-blurred Objects in Videos

Motion-from-Blur: 3D Shape and Motion Estimation of Motion-b...

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

作者： Rozumnyi, Denys Oswald, Martin R. Ferrari, Vittorio Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Univ Amsterdam Amsterdam Netherlands Google Res Mountain View CA USA Czech Tech Univ Prague Czech Republic

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. To this end, we model the blurred appearance of a fast moving object in a generative fashion by parametrizing its 3D position, rotation, velocity, acceleration, bounces, shape, and texture over the duration of a predefined time window spanning multiple frames. Using differentiable rendering, we are able to estimate all parameters by minimizing the pixel-wise reprojection error to the input video via backpropagating through a rendering pipeline that accounts for motion blur by averaging the graphics output over short time intervals. For that purpose, we also estimate the camera exposure gap time within the same optimization. To account for abrupt motion changes like bounces, we model the motion trajectory as a piece-wise polynomial, and we are able to estimate the specific time of the bounce at sub frame accuracy. Experiments on established benchmark datasets demonstrate that our method outperforms previous methods for fast moving object deblurring and 3D reconstruction.

关键词： Solid modeling Three-dimensional displays Shape Motion estimation Pipelines Rendering (computer graphics) Cameras

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