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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"

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RangeViT: Towards vision Transformers for 3D Semantic Segmentation in Autonomous Driving

RangeViT: Towards Vision Transformers for 3D Semantic Segmen...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Angelika Ando Spyros Gidaris Andrei Bursuc Gilles Puy Alexandre Boulch Renaud Marlet Valeo.ai Paris France Centre for Robotics Mines Paris Universite PSL Mines Paris Université PSL Paris France LIGM Ecole des Ponts Univ. Gustave Eiffel CNRS Marne-la-Vallée France

Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, e.g., via range projection, is an effective and popular approach. These projection-based methods usually benefit from fast computations and, when combined with techniques which use other point cloud representations, achieve state-of-the-art results. Today, projection-based methods leverage 2D CNNs but recent advances in computer vision show that vision transformers (ViTs) have achieved state-of-the-art results in many image- based benchmarks. In this work, we question if projection- based methods for 3D semantic segmentation can benefit from these latest improvements on ViTs. We answer positively but only after combining them with three key ingredients: (a) ViTs are notoriously hard to train and require a lot of training data to learn powerful representations. By preserving the same backbone architecture as for RGB images, we can exploit the knowledge from long training on large image collections that are much cheaper to acquire and annotate than point clouds. We reach our best results with pre-trained ViTs on large image datasets. (b) We compensate ViTs' lack of inductive bias by substituting a tailored convolutional stem for the classical linear embedding layer. (c) We refine pixel-wise predictions with a convolutional decoder and a skip connection from the convolutional stem to combine low-level but fine-grained features of the the convolutional stem with the high-level but coarse predictions of the ViT encoder. With these ingredients, we show that our method, called RangeViT, outperforms existing projection-based methods on nuScenes and SemanticKITTI. The code is available at https://***/valeoai/rangevit.

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Online Unsupervised Domain Adaptation for Person Re-identification

Online Unsupervised Domain Adaptation for Person Re-identifi...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hamza Rami Matthieu Ospici Sté phane Lathuiliè re LTCI T&#x00E9 l&#x00E9 com Paris Institut Polytechnique de Paris Atos

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target domain. Most of the recent papers that address this problem adopt an offline training setting. More precisely, the training of the Re-ID model is done assuming that we have access to the complete training target domain data set. In this paper, we argue that the target domain generally consists of a stream of data in a practical real-world application, where data is continuously increasing from the different network’s cameras. The Re-ID solutions are also constrained by confidentiality regulations stating that the collected data can be stored for only a limited period, hence the model can no longer get access to previously seen target images. Therefore, we present a new yet practical online setting for Unsupervised Domain Adaptation for person Re-ID with two main constraints: Online Adaptation and Privacy Protection. We then adapt and evaluate the state-of-the-art UDA algorithms on this new online setting using the well-known Market-1501, Duke, and MSMT17 benchmarks.

关键词： Training Adaptation models Privacy computer vision conferences Benchmark testing Data models

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Few-Shot Supervised Prototype Alignment for Pedestrian Detection on Fisheye Images

Few-Shot Supervised Prototype Alignment for Pedestrian Detec...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Thaddä us Wiedemer Stefan Wolf Arne Schumann Kaisheng Ma Jü rgen Beyerer Tsinghua University Karlsruhe Institute of Technology Fraunhofer IOSB Fraunhofer Center for Machine Learning

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Complete and pre-trained models are readily available for download for object detection and can perform well on datasets containing everyday images. Domain adaptation is used to transfer models to more specific datasets with characteristics not present in pre-training. We propose the novel adaptation setting of pedestrian detection in fisheye images, where target samples are scarce but annotated. Our setting provides interesting new challenges for adaptation due to global perspective changes and geometric distortions not found in existing adaptation tasks. To this end, we introduce loss coupling for unsupervised adversarial adaptation and boost prototype-based adaptation with ground-truth information. We additionally propose a novel supervised adaptation head for features in the bounding box regressor. Our method leads to more stable adversarial training and outperforms supervised and unsupervised baselines. Our method requires half the amount of training samples for small datasets to achieve the same performance as supervised fine-tuning.

关键词： Training Couplings Adaptation models Schedules Surveillance Prototypes computer architecture

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Global vision Transformer Pruning with Hessian-Aware Saliency

Global Vision Transformer Pruning with Hessian-Aware Salienc...

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conference on computer vision and pattern recognition (CVPR)

作者： Huanrui Yang Hongxu Yin Maying Shen Pavlo Molchanov Hai Li Jan Kautz NVIDIA University of California Berkeley Duke University

Transformers yield state-of-the-art results across many tasks. However, their heuristically designed architecture impose huge computational costs during inference. This work aims on challenging the common design philosophy of the vision Transformer (ViT) model with uniform dimension across all the stacked blocks in a model stage, where we redistribute the parameters both across transformer blocks and between different structures within the block via the first systematic attempt on global structural pruning. Dealing with diverse ViT structural components, we derive a novel Hessian-based structural pruning criteria comparable across all layers and structures, with latency-aware regularization for direct latency reduction. Performing iterative pruning on the DeiT-Base model leads to a new architecture family called NViT (Novel ViT), with a novel parameter redistribution that utilizes parameters more efficiently. On ImageNet-1K, NViT-Base achieves a $2.6\times FLOPs$ reduction, $5.1\times$ parameter reduction, and $1.9\times run$ -time speedup over the DeiT-Base model in a near lossless manner. Smaller NViT variants achieve more than 1% accuracy gain at the same throughput of the DeiT Small/Tiny variants, as well as a lossless $3.3\times parameter$ reduction over the SWIN-Small model. These results outperform prior art by a large margin. Further analysis is provided on the parameter redistribution insight of NViT, where we show the high prunability of ViT models, distinct sensitivity within ViT block, and unique parameter distribution trend across stacked ViT blocks. Our insights provide viability for a simple yet effective parameter redistribution rule towards more efficient ViTs for off-the-shelf performance boost.

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Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes

Where We Are and What We're Looking At: Query Based Worldwid...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Brandon Clark Alec Kerrigan Parth Parag Kulkarni Vicente Vivanco Cepeda Mubarak Shah Center for Research in Computer Vision University of Central Florida Orlando USA

Determining the exact latitude and longitude that a photo was taken is a useful and widely applicable task, yet it remains exceptionally difficult despite the accelerated progress of other computer vision tasks. Most previous approaches have opted to learn single representations of query images, which are then classified at different levels of geographic granularity. These approaches fail to exploit the different visual cues that give context to different hierarchies, such as the country, state, and city level. To this end, we introduce an end-to-end transformer-based architecture that exploits the relationship between different geographic levels (which we refer to as hierarchies) and the corresponding visual scene information in an image through hierarchical cross-attention. We achieve this by learning a query for each geographic hierarchy and scene type. Furthermore, we learn a separate representation for different environmental scenes, as different scenes in the same location are often defined by completely different visual features. We achieve state of the art accuracy on 4 standard geo-localization datasets : Im2GPS, Im2GPS3k, YFCC4k, and YFCC26k, as well as qualitatively demonstrate how our method learns different representations for different visual hierarchies and scenes, which has not been demonstrated in the previous methods. Above previous testing datasets mostly consist of iconic landmarks or images taken from social media, which makes the dataset a simple memory task, or makes it biased towards certain places. To address this issue we introduce a much harder testing dataset, Google-World-Streets-15k, comprised of images taken from Google Streetview covering the whole planet and present state of the art results. Our code can be found at https://***/AHKerrigan/GeoGuessNet.

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Siamese Image Modeling for Self-Supervised vision Representation Learning

Siamese Image Modeling for Self-Supervised Vision Representa...

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conference on computer vision and pattern recognition (CVPR)

作者： Chenxin Tao Xizhou Zhu Weijie Su Gao Huang Bin Li Jie Zhou Yu Qiao Xiaogang Wang Jifeng Dai Tsinghua University SenseTime Research Shanghai Artificial Intelligence Laboratory University of Science and Technology of China The Chinese University of Hong Kong

Self-supervised learning (SSL) has delivered superior performance on a variety of downstream vision tasks. Two main-stream SSL frameworks have been proposed, i.e., Instance Discrimination (ID) and Masked Image Modeling (MIM). ID pulls together representations from different views of the same image, while avoiding feature collapse. It lacks spatial sensitivity, which requires modeling the local structure within each image. On the other hand, MIM reconstructs the original content given a masked image. It instead does not have good semantic alignment, which requires projecting semantically similar views into nearby representations. To address this dilemma, we observe that (1) semantic alignment can be achieved by matching different image views with strong augmentations; (2) spatial sensitivity can benefit from predicting dense representations with masked images. Driven by these analysis, we propose Siamese Image Modeling (SiameseIM), which predicts the dense representations of an augmented view, based on another masked view from the same image but with different augmentations. SiameseIM uses a Siamese network with two branches. The online branch encodes the first view, and predicts the second view's representation according to the relative positions between these two views. The target branch produces the target by encoding the second view. SiameseIM can surpass both ID and MIM on a wide range of downstream tasks, including ImageNet finetuning and linear probing, COCO and LVIS detection, and ADE20k semantic segmentation. The improvement is more significant in few-shot, long-tail and robustness-concerned scenarios. Code shall be released.

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Generative Quanta Color Imaging

Generative Quanta Color Imaging

引用

conference on computer vision and pattern recognition (CVPR)

作者： Vishal Purohit Junjie Luo Yiheng Chi Qi Guo Stanley H. Chan Qiang Qiu Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The astonishing development of single-photon cameras has created an unprecedented opportunity for scientific and industrial imaging. However, the high data throughput generated by these 1-bit sensors creates a significant bottleneck for low-power applications. In this paper, we explore the possibility of generating a color image from a single binary frame of a single-photon camera. We evidently find this problem being particularly difficult to standard colorization approaches due to the substantial degree of exposure variation. The core innovation of our paper is an exposure synthesis model framed under a neural ordinary differential equation (Neural ODE) that allows us to generate a contin-uum of exposures from a single observation. This innovation ensures consistent exposure in binary images that col-orizers take on, resulting in notably enhanced colorization. We demonstrate applications of the method in single-image and burst colorization and show superior generative performance over baselines. Project website can be found at https://***/projects/2023/generative_quanta_***

关键词： Technological innovation computer vision Color Ordinary differential equations Cameras Throughput Mathematical models

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Thermal Image Super-Resolution Challenge Results - PBVS 2022

Thermal Image Super-Resolution Challenge Results - PBVS 2022

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Rafael E. Rivadeneira Angel D. Sappa Boris X. Vintimilla Jin Kim Dogun Kim Zhihao Li Yingchun Jian Bo Yan Leilei Cao Fengliang Qi Hongbin Wang Rongyuan Wu Lingchen Sun Yongqiang Zhao Lin Li Kai Wang Yicheng Wang Xuanming Zhang Huiyuan Wei Chonghua Lv Qigong Sun Xiaolin Tian Zhuang Jia Jiakui Hu Chenyang Wang Zhiwei Zhong Xianming Liu Junjun Jiang Escuela Superior Polit&#x00E9 cnica del Litoral ESPOL Guayaquil Ecuador Computer Vision Center Campus UAB Barcelona Spain

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year’s challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community’s interest in this hot topic.

关键词： Training Deep learning conferences Superresolution Transformers Particle measurements pattern recognition

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Semi-Supervised Video Inpainting with Cycle Consistency Constraints

Semi-Supervised Video Inpainting with Cycle Consistency Cons...

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conference on computer vision and pattern recognition (CVPR)

作者： Zhiliang Wu Hanyu Xuan Changchang Sun Weili Guan Kang Zhang Yan Yan School of Computer Science and Engineering Nanjing University of Science and Technology China School of Big Data and Statistics Anhui University China Department of Computer Science Illinois Institute of Technology USA School of Information Technology Monash University Australia Peng Cheng Laboratory China

Deep learning-based video inpainting has yielded promising results and gained increasing attention from re-searchers. Generally, these methods assume that the cor-rupted region masks of each frame are known and easily ob-tained. However, the annotation of these masks are labor-intensive and expensive, which limits the practical application of current methods. Therefore, we expect to relax this assumption by defining a new semi-supervised inpainting setting, making the networks have the ability of completing the corrupted regions of the whole video using the anno-tated mask of only one frame. Specifically, in this work, we propose an end-to-end trainable framework consisting of completion network and mask prediction network, which are designed to generate corrupted contents of the current frame using the known mask and decide the regions to be filled of the next frame, respectively. Besides, we introduce a cycle consistency loss to regularize the training parameters of these two networks. In this way, the completion network and the mask prediction network can constrain each other, and hence the overall performance of the trained model can be maximized. Furthermore, due to the natural existence of prior knowledge (e.g., corrupted contents and clear bor-ders), current video inpainting datasets are not suitable in the context of semi-supervised video inpainting. Thus, we create a new dataset by simulating the corrupted video of real-world scenarios. Extensive experimental results are reported to demonstrate the superiority of our model in the video inpainting task. Remarkably, although our model is trained in a semi-supervised manner, it can achieve compa-rable performance as fully-supervised methods.

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CSG0: Continual Urban Scene Generation with Zero Forgetting

CSG0: Continual Urban Scene Generation with Zero Forgetting

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Himalaya Jain Tuan-Hung Vu Patrick Pé rez Matthieu Cord Valeo.ai Paris France Sorbonne University Paris France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains; ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, i.e., with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin.

关键词： Training Visualization Costs Semantics Memory management Generative adversarial networks pattern recognition

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