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检索条件"任意字段=2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011"

共 21099 条记录，以下是11-20 订阅

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Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models

Visual Concept Connectome (VCC): Open World Concept Discover...

引用

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

作者： Kowal, Matthew Wildes, Richard P. Derpanis, Konstantinos G. York Univ Toronto ON Canada Samsung AI Ctr Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9798350353006

Understanding what deep network models capture in their learned representations is a fundamental challenge in computer vision. We present a new methodology to understanding such vision models, the Visual Concept Connectome (VCC), which discovers human interpretable concepts and their interlayer connections in a fully unsupervised manner. Our approach simultaneously reveals fine-grained concepts at a layer, connection weightings across all layers and is amendable to global analysis of network structure (e.g. branching pattern of hierarchical concept assemblies). Previous work yielded ways to extract interpretable concepts from single layers and examine their impact on classification, but did not afford multilayer concept analysis across an entire network architecture. Quantitative and qualitative empirical results show the effectiveness of VCCs in the domain of image classification. Also, we leverage VCCs for the application of failure mode debugging to reveal where mistakes arise in deep networks.

关键词： Concept-based interpretability Interpretability Multilayer interpretability Object recognition

来源：评论

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A Generative Approach for Wikipedia-Scale Visual Entity recognition

A Generative Approach for Wikipedia-Scale Visual Entity Reco...

引用

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

作者： Caron, Mathilde Iscen, Ahmet Fathi, Alireza Schmid, Cordelia Google Res San Francisco CA 94105 USA

ISBN: (纸本)9798350353006

In this paper, we address web-scale visual entity recognition, specifically the task of mapping a given query image to one of the 6 million existing entities in Wikipedia. One way of approaching a problem of such scale is using dual-encoder models (e.g. CLIP), where all the entity names and query images are embedded into a unified space, paving the way for an approximate kNN search. Alternatively, it is also possible to re-purpose a captioning model to directly generate the entity names for a given image. In contrast, we introduce a novel Generative Entity recognition (GER) frame-work, which given an input image learns to auto-regressively decode a semantic and discriminative "code" identifying the target entity. Our experiments demonstrate the efficacy of this GER paradigm, showcasing state-of-the-art performance on the challenging OVEN benchmark. GER surpasses strong captioning, dual-encoder, visual matching and hierarchical classification baselines, affirming its advantage in tackling the complexities of web-scale recognition.

关键词： generative auto-regressive models Image recognition vision-language models

来源：评论

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Learning to Count without Annotations

Learning to Count without Annotations

引用

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

作者： Knobel, Lukas Han, Tengda Asano, Yuki M. Univ Amsterdam Amsterdam Netherlands Univ Oxford Oxford England

ISBN: (纸本)9798350353006

While recent supervised methods for reference-based object counting continue to improve the performance on benchmark datasets, they have to rely on small datasets due to the cost associated with manually annotating dozens of objects in images. We propose UnCounTR, a model that can learn this task without requiring any manual annotations. To this end, we construct "Self-Collages", images with various pasted objects as training samples, that provide a rich learning signal covering arbitrary object types and counts. Our method builds on existing unsupervised representations and segmentation techniques to successfully demonstrate for the first time the ability of reference-based counting without manual supervision. Our experiments show that our method not only outperforms simple base-lines and generic models such as FasterRCNN and DETR, but also matches the performance of supervised counting models in some domains.

关键词： computer vision self-supervision visual counting

来源：评论

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

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

引用

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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One-Shot Open Affordance Learning with Foundation Models

One-Shot Open Affordance Learning with Foundation Models

引用

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

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

引用

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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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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Evidential Active recognition: Intelligent and Prudent Open-World Embodied Perception

Evidential Active Recognition: Intelligent and Prudent Open-...

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

作者： Fan, Lei Liang, Mingfu Li, Yunxuan Hua, Gang Wu, Ying Northwestern Univ Xian Shaanxi Peoples R China Wormpex AI Res Bellevue WA USA

ISBN: (纸本)9798350353006

Active recognition enables robots to intelligently explore novel observations, thereby acquiring more information while circumventing undesired viewing conditions. Recent approaches favor learning policies from simulated or collected data, wherein appropriate actions are more frequently selected when the recognition is accurate. However, most recognition modules are developed under the closed-world assumption, which makes them ill-equipped to handle unexpected inputs, such as the absence of the target object in the current observation. To address this issue, we propose treating active recognition as a sequential evidence-gathering process, providing by-step uncertainty quantification and reliable prediction under the evidence combination theory. Additionally, the reward function developed in this paper effectively characterizes the merit of actions when operating in open-world environments. To evaluate the performance, we collect a dataset from an indoor simulator, encompassing various recognition challenges such as distance, occlusion levels, and visibility. Through a series of experiments on recognition and robustness analysis, we demonstrate the necessity of introducing uncertainties to active recognition and the superior performance of the proposed method.

关键词： Active agents Active vision Embodied vision

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