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共 12859 条记录，以下是121-130 订阅

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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

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Learning unbiased classifiers from biased data with meta-learning

Learning unbiased classifiers from biased data with meta-lea...

引用

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

作者： Ragonesi, Ruggero Morerio, Pietro Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis PAVIS Genoa Italy Univ Verona Dept Comp Sci Verona Italy

ISBN: (纸本)9798350302493

It is well known that large deep architectures are powerful models when adequately trained, but may exhibit undesirable behavior leading to confident incorrect predictions, even when evaluated on slightly different test examples. Test data characterized by distribution shifts (from training data distribution), outliers, and adversarial samples are among the types of data affected by this problem. This situation worsens whenever data are biased, meaning that predictions are mostly based on spurious correlations present in the data. Unfortunately, since such correlations occur in the most of data, a model is prevented from correctly generalizing the considered classes. In this work, we tackle this problem from a meta-learning perspective. Considering the dataset as composed of unknown biased and unbiased samples, we first identify these two subsets by a pseudo-labeling algorithm, even if coarsely. Subsequently, we apply a bi-level optimization algorithm in which, in the inner loop, we look for the best parameters guiding the training of the two subsets, while in the outer loop, we train the final model taking benefit from augmented data generated using Mixup. Properly tuning the contributions of biased and unbiased data, together with the regularization introduced by the mixed data has proved to be an effective training strategy to learn unbiased models, showing superior generalization capabilities. Experimental results on synthetically and realistically biased datasets surpass state-of-the-art performance, as compared to existing methods.

关键词： computer vision

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Challenges in Video-Based Infant Action recognition: A Critical Examination of the State of the Art

Challenges in Video-Based Infant Action Recognition: A Criti...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Hatamimajoumerd, Elaheh Kakhaki, Pooria Daneshvar Huang, Xiaofei Luan, Lingfei Amraee, Somaieh Ostadabbas, Sarah Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Roux Inst Portland ME USA Univ Minnesota Minneapolis MN USA

ISBN: (纸本)9798350370287;9798350370713

Automated human action recognition, a burgeoning field within computer vision, boasts diverse applications spanning surveillance, security, human-computer interaction, tele-health, and sports analysis. Precise action recognition in infants serves a multitude of pivotal purposes, encompassing safety monitoring, developmental milestone tracking, early intervention for developmental delays, fostering parent-infant bonds, advancing computer-aided diagnostics, and contributing to the scientific comprehension of child development. This paper delves into the intricacies of infant action recognition, a domain that has remained relatively uncharted despite the accomplishments in adult action recognition. In this study, we introduce a ground-breaking dataset called "InfActPrimitive", encompassing five significant infant milestone action categories, and we incorporate specialized preprocessing for infant data. We conducted an extensive comparative analysis employing cutting-edge skeleton-based action recognition models using this dataset. Our findings reveal that, although the PoseC3D model achieves the highest accuracy at approximately 71%, the remaining models struggle to accurately capture the dynamics of infant actions. This highlights a substantial knowledge gap between infant and adult action recognition domains and the urgent need for data-efficient pipeline models

关键词： computer vision

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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

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Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing

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

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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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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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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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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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Scene Graph Driven Text-Prompt Generation for Image Inpainting

Scene Graph Driven Text-Prompt Generation for Image Inpainti...

引用

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

作者： Shukla, Tripti Maheshwari, Paridhi Singh, Rajhans Shukla, Ankita Kulkarni, Kuldeep Turaga, Pavan Adobe Res India San Jose CA 95110 USA Stanford Univ Stanford CA USA Arizona State Univ Tempe AZ USA

ISBN: (纸本)9798350302493

Scene editing methods are undergoing a revolution, driven by text-to-image synthesis methods. Applications in media content generation have benefited from a careful set of engineered text prompts, that have been arrived at by the artists by trial and error. There is a growing need to better model prompt generation, for it to be useful for a broad range of consumer-grade applications. We propose a novel method for text prompt generation for the explicit purpose of consumer-grade image inpainting, i.e. insertion of new objects into missing regions in an image. Our approach leverages existing inter-object relationships to generate plausible textual descriptions for the missing object, that can then be used with any text-to-image generator. Given an image and a location where a new object is to be inserted, our approach first converts the given image to an intermediate scene graph. Then, we use graph convolutional networks to 'expand' the scene graph by predicting the identity and relationships of the new object to be inserted, with respect to the existing objects in the scene. The output of the expanded scene graph is cast into a textual description, which is then processed by a text-to-image generator, conditioned on the given image, to produce the final inpainted image. We conduct extensive experiments on the Visual Genome dataset, and show through qualitative and quantitative metrics that our method is superior to other methods.

关键词： computer vision

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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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