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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Hephaestus: A large scale multitask dataset towards InSAR understanding

Hephaestus: A large scale multitask dataset towards InSAR un...

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

作者： Bountos, Nikolaos Ioannis Papoutsis, Ioannis Michail, Dimitrios Karavias, Andreas Elias, Panagiotis Parcharidis, Isaak Natl Observ Athens Inst Astron Astrophys Space Applicat & Remote Sen Athens Greece Harokopio Univ Athens Dept Informat & Telemat Athens Greece Harokopio Univ Athens Dept Geog Athens Greece

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) products in particular, are one of the largest sources of Earth Observation data. InSAR provides unique information on diverse geophysical processes and geology, and on the geotechnical properties of man-made structures. However, there are only a limited number of applications that exploit the abundance of InSAR data and deep learning methods to extract such knowledge. The main barrier has been the lack of a large curated and annotated InSAR dataset, which would be costly to create and would require an interdisciplinary team of experts experienced on InSAR data interpretation. In this work, we put the effort to create and make available the first of its kind, manually annotated dataset that consists of 19,919 individual Sentinel-1 interferograms acquired over 44 different volcanoes globally, which are split into 216,106 InSAR patches. The annotated dataset is designed to address different computer vision problems, including volcano state classification, semantic segmentation of ground deformation, detection and classification of atmospheric signals in InSAR imagery, interferogram captioning, text to InSAR generation, and InSAR image quality assessment.

关键词： Deep learning Image quality computer vision Image segmentation Semantics Volcanoes Task analysis

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Tri-Perspective View for vision-Based 3D Semantic Occupancy Prediction

Tri-Perspective View for Vision-Based 3D Semantic Occupancy ...

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

作者： Huang, Yuanhui Zheng, Wenzhao Zhang, Yunpeng Zhou, Jie Lu, Jiwen Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350301298

Modern methods for vision-centric autonomous driving perception widely adopt the bird's-eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. To address this, we propose a tri-perspective view (TPV) representation which accompanies BEV with two additional perpendicular planes. We model each point in the 3D space by summing its projected features on the three planes. To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively. We employ the attention mechanism to aggregate the image features corresponding to each query in each TPV plane. Experiments show that our model trained with sparse supervision effectively predicts the semantic occupancy for all voxels. We demonstrate for the first time that using only camera inputs can achieve comparable performance with LiDAR-based methods on the LiDAR segmentation task on nuScenes. Code: https://***/wzzheng/TPVFormer.

关键词： Autonomous driving

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Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis

Enhancing Traffic Safety with Parallel Dense Video Captionin...

引用

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

作者： Shoman, Maged Wang, Dongdong Aboah, Armstrong Abdel-Aty, Mohamed Univ Cent Florida Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL 32816 USA North Dakota State Univ Dept Civil Construct & Environm Engn Fargo ND USA Univ Cent Florida Joint Appointment Dept Comp Sci Dept Civil Environm & Construct Engn Smart & Safe Transportat SST Lab Orlando FL USA

ISBN: (纸本)9798350365474

This paper introduces our solution for Track 2 in AI City Challenge 2024. The task aims to solve traffic safety description and analysis with the dataset of Woven Traffic Safety (WTS), a real-world Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding. Our solution mainly focuses on the following points: 1) To solve dense video captioning, we leverage the framework of dense video captioning with parallel decoding (PDVC) to model visual-language sequences and generate dense caption by chapters for video. 2) Our work leverages CLIP to extract visual features to more efficiently perform cross-modality training between visual and textual representations. 3) We conduct domain-specific model adaptation to mitigate domain shift problem that poses recognition challenge in video understanding. 4) Moreover, we leverage BDD-5K captioned videos to conduct knowledge transfer for better understanding WTS videos and more accurate captioning. Our solution has yielded on the test set, achieving 6th place in the competition. The opensource code will be available at https://***/UCF-SST-Lab/AICity2024CVPRW

关键词： computer vision cross-modality learning dense video captioning domain adaptation natural language processing

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GamutMLP: A Lightweight MLP for Color Loss Recovery

GamutMLP: A Lightweight MLP for Color Loss Recovery

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

作者： Le, Hoang M. Price, Brian Cohen, Scott Brown, Michael S. York Univ Toronto ON Canada Adobe Res San Jose CA USA

ISBN: (纸本)9798350301298

Cameras and image-editing software often process images in the wide-gamut ProPhoto color space, encompassing 90% of all visible colors. However, when images are encoded for sharing, this color-rich representation is transformed and clipped to fit within the small-gamut standard RGB (sRGB) color space, representing only 30% of visible colors. Recovering the lost color information is challenging due to the clipping procedure. Inspired by neural implicit representations for 2D images, we propose a method that optimizes a lightweight multi-layer-perceptron (MLP) model during the gamut reduction step to predict the clipped values. GamutMLP takes approximately 2 seconds to optimize and requires only 23 KB of storage. The small memory footprint allows our GamutMLP model to be saved as metadata in the sRGB image-the model can be extracted when needed to restore wide-gamut color values. We demonstrate the effectiveness of our approach for color recovery and compare it with alternative strategies, including pre-trained DNN-based gamut expansion networks and other implicit neural representation methods. As part of this effort, we introduce a new color gamut dataset of 2200 wide-gamut/small-gamut images for training and testing.

关键词： Low-level vision

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Semi-Supervised Learning with Scarce Annotations

Semi-Supervised Learning with Scarce Annotations

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

作者： Rebuffi, Sylvestre-Alvise Ehrhardt, Sebastien Han, Kai Vedaldi, Andrea Zisserman, Andrew Univ Oxford Visual Geometry Grp Oxford England

ISBN: (纸本)9781728193601

While semi-supervised learning (SSL) algorithms provide an efficient way to make use of both labelled and unlabelled data, they generally struggle when the number of annotated samples is very small. In this work, we consider the problem of SSL multi-class classification with very few labelled instances. We introduce two key ideas. The first is a simple but effective one: we leverage the power of transfer learning among different tasks and self-supervision to initialize a good representation of the data without making use of any label. The second idea is a new algorithm for SSL that can exploit well such a pre-trained representation. The algorithm works by alternating two phases, one fitting the labelled points and one fitting the unlabelled ones, with carefully-controlled information flow between them. The benefits are greatly reducing overfitting of the labelled data and avoiding issue with balancing labelled and unlabelled losses during training. We show empirically that this method can successfully train competitive models with as few as 10 labelled data points per class. More in general, we show that the idea of bootstrapping features using self-supervised learning always improves SSL on standard benchmarks. We show that our algorithm works increasingly well compared to other methods when refining from other tasks or datasets.

关键词： computer vision

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How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting

How many Observations are Enough? Knowledge Distillation for...

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

作者： Monti, Alessio Porrello, Angelo Calderara, Simone Coscia, Pasquale Ballan, Lamberto Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy Univ Padua Padua Italy

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Accurate prediction of future human positions is an essential task for modern video-surveillance systems. Current state-of-the-art models usually rely on a "history" of past tracked locations (e.g., 3 to 5 seconds) to predict a plausible sequence of future locations (e.g., up to the next 5 seconds). We feel that this common schema neglects critical traits of realistic applications: as the collection of input trajectories involves machine perception (i.e., detection and tracking), incorrect detection and fragmentation errors may accumulate in crowded scenes, leading to tracking drifts. On this account, the model would be fed with corrupted and noisy input data, thus fatally affecting its prediction performance. In this regard, we focus on delivering accurate predictions when only few input observations are used, thus potentially lowering the risks associated with automatic perception. To this end, we conceive a novel distillation strategy that allows a knowledge transfer from a teacher network to a student one, the latter fed with fewer observations (just two ones). We show that a properly defined teacher supervision allows a student network to perform comparably to state-of-the-art approaches that demand more observations. Besides, extensive experiments on common trajectory forecasting datasets highlight that our student network better generalizes to unseen scenarios.

关键词： Training Tracking Robot vision systems Predictive models Transformers Trajectory pattern recognition

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A Probabilistic Collaborative Representation based Approach for pattern Classification 29

A Probabilistic Collaborative Representation based Approach ...

引用

2016 IEEE conference on computer vision and pattern recognition (CVPR)

作者： Cai, Sijia Zhang, Lei Zuo, Wangmeng Feng, Xiangchu Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China Xidian Univ Dept Appl Math Xian Peoples R China

ISBN: (纸本)9781467388511

Conventional representation based classifiers, ranging from the classical nearest neighbor classifier and nearest subspace classifier to the recently developed sparse representation based classifier (SRC) and collaborative representation based classifier (CRC), are essentially distance based classifiers. Though SRC and CRC have shown interesting classification results, their intrinsic classification mechanism remains unclear. In this paper we propose a probabilistic collaborative representation framework, where the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and computed. Consequently, we present a probabilistic collaborative representation based classifier (ProCRC), which jointly maximizes the likelihood that a test sample belongs to each of the multiple classes. The final classification is performed by checking which class has the maximum likelihood. The proposed ProCRC has a clear probabilistic interpretation, and it shows superior performance to many popular classifiers, including SRC, CRC and SVM. Coupled with the CNN features, it also leads to state-of-the-art classification results on a variety of challenging visual datasets.

关键词： Classification (of information)

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PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery

PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationshi...

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

作者： Zhang, Tianyi Lin, Jie Hu, Peng Zhao, Bin Aly, Mohamed M. Sabry ASTAR I2R Singapore Singapore Sichuan Univ Chengdu Peoples R China ASTAR IME Singapore Singapore Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9781665445092

Non-maximum Suppression (NMS) is an essential post-processing step in modern convolutional neural networks for object detection. Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines. MaxpoolNMS is introduced as a parallelizable alternative to GreedyNMS, which in turn enables faster speed than GreedyNMS at comparable accuracy. However, MaxpoolNMS is only capable of replacing the GreedyNMS at the first stage of two-stage detectors like Faster-RCNN. There is a significant drop in accuracy when applying MaxpoolNMS at the final detection stage, due to the fact that MaxpoolNMS fails to approximate GreedyNMS precisely in terms of bounding box selection. In this paper, we propose a general, parallelizable and configurable approach PSRR-MaxpoolNMS, to completely replace GreedyNMS at all stages in all detectors. By introducing a simple Relationship Recovery module and a Pyramid Shifted MaxpoolNMS module, our PSRR-MaxpoolNMS is able to approximate GreedyNMS more precisely than MaxpoolNMS. Comprehensive experiments show that our approach outperforms MaxpoolNMS by a large margin, and it is proven faster than GreedyNMS with comparable accuracy. For the first time, PSRR-MaxpoolNMS provides a fully parallelizable solution for customized hardware design, which can be reused for accelerating NMS everywhere.

关键词： computer vision Pipelines Detectors Object detection Hardware pattern recognition Convolutional neural networks

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Strengthen Learning Tolerance for Weakly Supervised Object Localization

Strengthen Learning Tolerance for Weakly Supervised Object L...

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

作者： Guo, Guangyu Han, Junwei Wan, Fang Zhang, Dingwen Northwestern Polytech Univ Brain & Artificial Intelligence Lab Xian Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781665445092

Weakly supervised object localization (WSOL) aims at learning to localize objects of interest by only using the image-level labels as the supervision. While numerous efforts have been made in this field, recent approaches still suffer from two challenges: one is the part domination issue while the other is the learning robustness issue. Specifically, the former makes the localizer prone to the local discriminative object regions rather than the desired whole object, and the latter makes the localizer over-sensitive to the variations of the input images so that one can hardly obtain localization results robust to the arbitrary visual stimulus. To solve these issues, we propose a novel framework to strengthen the learning tolerance, referred to as SLT-Net, for WSOL. Specifically, we consider two fold learning tolerance strengthening mechanisms. One is the semantic tolerance strengthening mechanism, which allows the localizer to make mistakes for classifying similar semantics so that it will not concentrate too much on the discriminative local regions. The other is the visual stimuli tolerance strengthening mechanism, which enforces the localizer to be robust to different image transformations so that the prediction quality will not be sensitive to each specific input image. Finally, we implement comprehensive experimental comparisons on two widely-used datasets CUB and ILSVRC2012, which demonstrate the effectiveness of our proposed approach.

关键词： Location awareness Visualization computer vision Computational modeling Semantics Robustness pattern recognition

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Efficient Feature Transformations for Discriminative and Generative Continual Learning

Efficient Feature Transformations for Discriminative and Gen...

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

作者： Verma, Vinay Kumar Liang, Kevin J. Mehta, Nikhil Rai, Piyush Carin, Lawrence Duke Univ Durham NC 27706 USA Facebook AI Menlo Pk CA USA IIT Kanpur Kanpur Uttar Pradesh India

ISBN: (纸本)9781665445092

As neural networks are increasingly being applied to real-world applications, mechanisms to address distributional shift and sequential task learning without forgetting are critical. Methods incorporating network expansion have shown promise by naturally adding model capacity for learning new tasks while simultaneously avoiding catastrophic forgetting. However, the growth in the number of additional parameters of many of these types of methods can be computationally expensive at larger scales, at times prohibitively so. Instead, we propose a simple task-specific feature map transformation strategy for continual learning, which we call Efficient Feature Transformations (EFTs). These EFTs provide powerful flexibility for learning new tasks, achieved with minimal parameters added to the base architecture. We further propose a feature distance maximization strategy, which significantly improves task prediction in class incremental settings, without needing expensive generative models. We demonstrate the efficacy and efficiency of our method with an extensive set of experiments in discriminative (CIFAR-100 and ImageNet-1K) and generative (LSUN, CUB-200, Cats) sequences of tasks. Even with low single-digit parameter growth rates, EFTs can outperform many other continual learning methods in a wide range of settings.

关键词： Learning systems computer vision Computational modeling Scalability Neural networks Transforms Predictive models

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