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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

共 11888 条记录，以下是41-50 订阅

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

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

引用

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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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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Semantic Shield: Defending vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment

Semantic Shield: Defending Vision-Language Models Against Ba...

引用

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

作者： Ishmam, Alvi Md Thomas, Christopher Virginia Tech Blacksburg VA 24061 USA

ISBN: (纸本)9798350353006

In recent years there has been enormous interest in vision-language models trained using self-supervised objectives. However, the use of large-scale datasets scraped from the web for training also makes these models vulnerable to potential security threats, such as backdooring and poisoning attacks. In this paper, we propose a method for mitigating such attacks on contrastively trained vision-language models. Our approach leverages external knowledge extracted from a language model to prevent models from learning correlations between image regions which lack strong alignment with external knowledge. We do this by imposing constraints to enforce that attention paid by the model to visual regions is proportional to the alignment of those regions with external knowledge. We conduct extensive experiments using a variety of recent backdooring and poisoning attacks on multiple datasets and architectures. Our results clearly demonstrate that our proposed approach is highly effective at defending against such attacks across multiple settings, while maintaining model utility and without requiring any changes at inference time.

关键词： Adversarial attack and defense vision languge model

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FairDeDup: Detecting and Mitigating vision-Language Fairness Disparities in Semantic Dataset Deduplication

FairDeDup: Detecting and Mitigating Vision-Language Fairness...

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

作者： Slyman, Eric Lee, Stefan Cohen, Scott Kafle, Kushal Oregon State Univ Dept EECS Corvallis OR 97331 USA Adobe Res San Francisco CA 94107 USA

ISBN: (纸本)9798350353006

Worst-GroupRecent dataset deduplication techniques have demonstrated that content-aware dataset pruning can dramatically reduce the cost of training vision-Language Pre-trained (VLP) models without significant performance losses compared to training on the original dataset. These results have been based on pruning commonly used image-caption datasets collected from the web - datasets that are known to harbor harmful social biases that may then be codified in trained models. In this work, we evaluate how deduplication affects the prevalence of these biases in the resulting trained models and introduce an easy-to-implement modification to the recent SemDeDup algorithm that can reduce the negative effects that we observe. When examining CLIP-style models trained on deduplicated variants of LAION-400M, we find our proposed FairDeDup algorithm consistently leads to improved fairness metrics over SemDeDup on the FairFace and FACET datasets while maintaining zero-shot performance on CLIP benchmarks.

关键词： deduplication fairness foundation models human-centered ai language multimodal pretraining vision vision-language

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An Empirical Study of Scaling Law for Scene Text recognition

An Empirical Study of Scaling Law for Scene Text Recognition

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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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HumMUSS: Human Motion Understanding using State Space Models

HumMUSS: Human Motion Understanding using State Space Models

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

作者： Mondal, Arnab Alletto, Stefano Tome, Denis Mila Montreal PQ Canada Apple Cupertino CA 95014 USA

ISBN: (纸本)9798350353013;9798350353006

Understanding human motion from video is essential for a range of applications, including pose estimation, mesh recovery and action recognition. While state-of-the-art methods predominantly rely on transformer-based architectures, these approaches have limitations in practical scenarios. Transformers are slower when sequentially predicting on a continuous stream of frames in real-time, and do not generalize to new frame rates. In light of these constraints, we propose a novel attention-free spatiotemporal model for human motion understanding building upon recent advancements in state space models. Our model not only matches the performance of transformer-based models in various motion understanding tasks but also brings added benefits like adaptability to different video frame rates and enhanced training speed when working with longer sequences of keypoints. Moreover, the proposed model supports both offline and real-time applications. For real-time sequential prediction, our model is both memory efficient and several times faster than transformer-based approaches while maintaining their high accuracy.

关键词： action recognition human motion understanding mesh recovery pose estimation self-supervised learning spatiotemporal modeling state space models

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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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Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation

Animate Anyone: Consistent and Controllable Image-to-Video S...

引用

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

作者： Hu, Li Alibaba Grp Inst Intelligent Comp Hangzhou Peoples R China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013;9798350353006

Character Animation aims to generating character videos from still images through driving signals. Currently, diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. However, challenges persist in the realm of image-to-video, especially in character animation, where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper, we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image, we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity, we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data, our approach can animate arbitrary characters, yielding superior results in character animation compared to other image-to-video methods. Furthermore, we evaluate our method on image animation benchmarks, achieving state-of-the-art results.

关键词： Visualization computer vision Training data Benchmark testing Animation Diffusion models Controllability

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Learning Correlation Structures for vision Transformers

Learning Correlation Structures for Vision Transformers

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

作者： Kim, Manjin Seo, Paul Hongsuck Schmid, Cordelia Cho, Minsu POSTECH Pohang South Korea Korea Univ Seoul South Korea Google Res Mountain View CA USA

ISBN: (纸本)9798350353006

We introduce a new attention mechanism, dubbed structural self-attention (StructSA), that leverages rich correlation patterns naturally emerging in key-query interactions of attention. StructSA generates attention maps by recognizing space-time structures of key-query correlations via convolution and uses them to dynamically aggregate local contexts of value features. This effectively leverages rich structural patterns in images and videos such as scene layouts, object motion, and inter-object relations. Using StructSA as a main building block, we develop the structural vision transformer (StructViT) and evaluate its effectiveness on both image and video classification tasks, achieving state-of-the-art results on ImageNet-1K, Kinetics-400, Something-Something V1 & V2, Diving-48, and FineGym.

关键词： correlation modeling image classification self-attention video classification vision Transformers visual representation 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...

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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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