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检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020"

共 11281 条记录，以下是21-30 订阅

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Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing

Probabilistic Sampling of Balanced K-Means using Adiabatic Q...

引用

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

作者： Zaech, Jan-Nico Danelljan, Martin Birdal, Tolga Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Univ Sofia INSAIT Sofia Bulgaria Imperial Coll London London England

ISBN: (纸本)9798350353006

Adiabatic quantum computing (AQC) is a promising approach for discrete and often NP-hard optimization problems. Current AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for many computer vision tasks. Despite requiring multiple measurements from the noisy AQC, current approaches only utilize the best measurement, discarding information contained in the remaining ones. In this work, we explore the potential of using this information for probabilistic balanced k-means clustering. Instead of discarding non-optimal solutions, we propose to use them to compute calibrated posterior probabilities with little additional compute cost. This allows us to identify ambiguous solutions and data points, which we demonstrate on a D-Wave AQC on synthetic tasks and real visual data.

关键词： Clustering computer vision Quantum Computing uncertainty estimation

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An Empirical Study of Scaling Law for Scene Text recognition

An Empirical Study of Scaling Law for Scene Text Recognition

引用

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

作者： Rang, Miao Bi, Zhenni Liu, Chuanjian Wang, Yunhe Han, Kai Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350353006

The laws of model size, data volume, computation and model performance have been extensively studied in the field of Natural Language Processing (NLP). However, the scaling laws in Scene Text recognition (STR) have not yet been investigated. To address this, we conducted comprehensive studies that involved examining the correlations between performance and the scale of models, data volume and computation in the field of text recognition. Conclusively, the study demonstrates smooth power laws between performance and model size, as well as training data volume, when other influencing factors are held constant. Additionally, we have constructed a large-scale dataset called REBU-Syn, which comprises 6 M real samples and 18 M synthetic samples. Based on the disclosed scaling law and new dataset, we successfully trained a scene text recognition model, achieving a new state-of-the-art on 6 common test benchmarks with top-1 average accuracy of 97.42%. The models and dataset are publicly available at large-ocr-model.

关键词： REBU-Syn scaling law STR

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Masked AutoDecoder is Effective Multi-Task vision Generalist

Masked AutoDecoder is Effective Multi-Task Vision Generalist

引用

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

作者： Qiu, Han Huang, Jiaxing Gao, Peng Lu, Lewei Zhang, Xiaoqin Lu, Shijian Nanyang Technol Univ S Lab Singapore Singapore Shanghai Artificial Intelligence Lab Shanghai Peoples R China Sensetime Res Beijing Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China

ISBN: (纸本)9798350353006

Inspired by the success of general-purpose models in NLP, recent studies attempt to unify different vision tasks in the same sequence format and employ autoregressive Transformers for sequence prediction. They apply uni-directional attention to capture sequential dependencies and generate task sequences recursively. However, such autoregressive Transformers may not fit vision tasks well, as vision task sequences usually lack the sequential dependencies typically observed in natural languages. In this work, we design Masked AutoDecoder (MAD), an effective multi-task vision generalist. MAD consists of two core designs. First, we develop a parallel decoding framework that introduces bi-directional attention to capture contextual dependencies comprehensively and decode vision task sequences in parallel. Second, we design a masked sequence modeling approach that learns rich task contexts by masking and reconstructing task sequences. In this way, MAD handles all the tasks by a single network branch and a simple cross-entropy loss with minimal task-specific designs. Extensive experiments demonstrate the great potential of MAD as a new paradigm for unifying various vision tasks. MAD achieves superior performance and inference efficiency compared to autoregressive counterparts while obtaining competitive accuracy with task-specific models. Code will be released at https://***/hanqiu-hq/MAD.

关键词： Masked AutoDecoder Masked Sequence Modeling Transformer vision Generalist

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One-Shot Open Affordance Learning with Foundation Models

One-Shot Open Affordance Learning with Foundation Models

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

作者： Li, Gen Sun, Deqing Sevilla-Lara, Laura Jampani, Varun Univ Edinburgh Edinburgh Midlothian Scotland Google Res Mountain View CA USA Stabil AI London England

ISBN: (纸本)9798350353013;9798350353006

We introduce One-shot Open Affordance Learning (OOAL), where a model is trained with just one example per base object category, but is expected to identify novel objects and affordances. While vision-language models excel at recognizing novel objects and scenes, they often struggle to understand finer levels of granularity such as affordances. To handle this issue, we conduct a comprehensive analysis of existing foundation models, to explore their inherent understanding of affordances and assess the potential for data-limited affordance learning. We then propose a vision-language framework with simple and effective designs that boost the alignment between visual features and affordance text embeddings. Experiments on two affordance segmentation benchmarks show that the proposed method outperforms state-of-the-art models with less than 1% of the full training data, and exhibits reasonable generalization capability on unseen objects and affordances. Project page: https://***/ooal.

关键词： Affordance Foundation Models vision-Language Models Visual Affordance Learning

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Frozen Feature Augmentation for Few-Shot Image Classification

Frozen Feature Augmentation for Few-Shot Image Classificatio...

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

作者： Bar, Andreas Houlsby, Neil Dehghani, Mostafa Kumar, Manoj Google DeepMind London England Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

ISBN: (纸本)9798350353006

Training a linear classifier or lightweight model on top of pretrained vision model outputs, so-called 'frozen features', leads to impressive performance on a number of downstream few-shot tasks. Currently, frozen features are not modified during training. On the other hand, when networks are trained directly on images, data augmentation is a standard recipe that improves performance with no substantial overhead. In this paper, we conduct an extensive pilot study on few-shot image classification that explores applying data augmentations in the frozen feature space, dubbed 'frozen feature augmentation (FroFA)', covering twenty augmentations in total. Our study demonstrates that adopting a deceptively simple pointwise FroFA, such as brightness, can improve few-shot performance consistently across three network architectures, three large pre-training datasets, and eight transfer datasets.

关键词： data augmentation feature augmentation few-shot learning frozen features image classification large-scale pretraining transfer learning vision transformer

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JoAPR: Cleaning the Lens of Prompt Learning for vision-Language Models

JoAPR: Cleaning the Lens of Prompt Learning for Vision-Langu...

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

作者： Guo, Yuncheng Guo, Xiaodong Fudan Univ Dept Elect Engn Shanghai 200438 Peoples R China

ISBN: (纸本)9798350353006

Leveraging few-shot datasets in prompt learning for vision-Language Models eliminates the need for manual prompt engineering while highlighting the necessity of accurate annotations for the labels. However, high-level or complex label noise challenges prompt learning for vision-Language Models. Aiming at this issue, we propose a new framework for improving its robustness. Specifically, we introduce the Joint Adaptive Partitioning for Label Refurbishment (JoAPR), a structured framework encompassing two key steps. 1) Data Partitioning, where we differentiate between clean and noisy data using joint adaptive thresholds. 2) Label Refurbishment, where we correct the labels based on the partition outcomes before retraining the network. Our comprehensive experiments confirm that JoAPR substantially enhances the robustness of prompt learning for vision-Language Models against label noise, offering a promising direction for future research.

关键词： joint adaptive partitioning label noise label refurbishment prompt learning vision-Language Pre-Trained Models

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Selective, Interpretable and Motion Consistent Privacy Attribute Obfuscation for Action recognition

Selective, Interpretable and Motion Consistent Privacy Attri...

引用

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

作者： Ilic, Filip Zhao, He Pock, Thomas Wildes, Richard P. Graz Univ Technol Graz Austria York Univ York N Yorkshire England

ISBN: (纸本)9798350353006

Concerns for the privacy of individuals captured in public imagery have led to privacy-preserving action recognition. Existing approaches often suffer from issues arising through obfuscation being applied globally and a lack of interpretability. Global obfuscation hides privacy sensitive regions, but also contextual regions important for action recognition. Lack of interpretability erodes trust in these new technologies. We highlight the limitations of current paradigms and propose a solution: Human selected privacy templates that yield interpretability by design, an obfuscation scheme that selectively hides attributes and also induces temporal consistency, which is important in action recognition. Our approach is architecture agnostic and directly modifies input imagery, while existing approaches generally require architecture training. Our approach offers more flexibility, as no training is required, and outperforms alternatives on three widely used datasets.

关键词： Action recognition Privacy Preservation Video Understanding

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TransLoc4D: Transformer-based 4D Radar Place recognition

TransLoc4D: Transformer-based 4D Radar Place Recognition

引用

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

作者： Peng, Guohao Li, Heshan Zhao, Yangyang Zhang, Jun Wu, Zhenyu Zheng, Pengyu Wang, Danwei Nanyang Technol Univ Singapore Singapore

ISBN: (纸本)9798350353006

Place recognition is crucial for unmanned vehicles in terms of localization and mapping. Recent years have witnessed numerous explorations in the field, where 2D cameras and 3D LiDARs are mostly employed. Despite their admirable performance, they may encounter challenges in adverse weather such as rain and fog. Hopefully, 4D millimeter-wave radar emerges as a promising alternative, as its longer wavelength makes it virtually immune to interference from tiny particles of fog and rain. Therefore, in this work, we propose a novel 4D radar place recognition model, TransLoc4D, based on sparse convolutions and Transformer structures. Specifically, a MinkLoc4D backbone is first proposed to leverage the multi-modal information from 4D radar scans. Rather than merely capturing geometric structures of point clouds, MinkLoc4D additionally explores their intensity and velocity properties. After feature extraction, a Transformer layer is introduced to enhance local features before aggregation, where linear self-attention captures the long-range dependencies of the point cloud, alleviating its sparsity and noise. To validate TransLoc4D, we construct two datasets and set up benchmarks for 4D radar place recognition. Experiments validate the feasibility of TransLoc4D and demonstrate it can robustly deal with dynamic and adverse environments.

关键词： localization Place recognition Scene Understanding SLAM

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Label Propagation for Zero-shot Classification with vision-Language Models

Label Propagation for Zero-shot Classification with Vision-L...

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

作者： Stojnic, Vladan Kalantidis, Yannis Tolias, Giorgos Czech Tech Univ FEE VRG Prague Czech Republic NAVER LABS Europe Meylan France

ISBN: (纸本)9798350353006

vision-Language Models (VLMs) have demonstrated impressive performance on zero-shot classification, i.e. classification when provided merely with a list of class names. In this paper, we tackle the case of zero-shot classification in the presence of unlabeled data. We leverage the graph structure of the unlabeled data and introduce ZLaP, a method based on label propagation (LP) that utilizes geodesic distances for classification. We tailor LP to graphs containing both text and image features and further propose an efficient method for performing inductive inference based on a dual solution and a sparsification step. We perform extensive experiments to evaluate the effectiveness of our method on 14 common datasets and show that ZLaP outperforms the latest related works. Code: https://***/vladan-stojnic/ZLaP

关键词： label propagation vision-language models zero-shot classification

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Florence-2: Advancing a Unified Representation for a Variety of vision Tasks

Florence-2: Advancing a Unified Representation for a Variety...

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

作者： Xiao, Bin Wu, Haiping Xu, Weijian Dai, Xiyang Hu, Houdong Lu, Yumao Zeng, Michael Liu, Ce Yuan, Lu Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9798350353013;9798350353006

We introduce Florence-2, a novel vision foundation model with a unified, prompt-based representation for various computer vision and vision-language tasks. While ex-isting large vision models excel in transfer learning, they struggle to perform diverse tasks with simple instructions, a capability that implies handling the complexity of various spatial hierarchy and semantic granularity. Florence-2 was designed to take text-prompt as task instructions and generate desirable results in text forms, whether it be captioning, object detection, grounding or segmentation. This multi-task learning setup demands large-scale, high-quality annotated data. To this end, we co-developed FLD-5B that consists of 5.4 billion comprehensive visual annotations on 126 million images, using an iterative strategy of automated image annotation and model refinement. We adopted a sequence-to-sequence structure to train Florence-2 to perform versatile and comprehensive vision tasks. Extensive evaluations on numerous tasks demonstrated Florence-2 to be a strong vision foundation model contender with unprecedented zero-shot and fine-tuning capabilities.

关键词： Image annotation

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