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共 23241 条记录，以下是91-100 订阅

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JRDB-Social: A Multifaceted Robotic Dataset for Understanding of Context and Dynamics of Human Interactions Within Social Groups

JRDB-Social: A Multifaceted Robotic Dataset for Understandin...

引用

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

作者： Jahangard, Simindokht Cai, Zhixi Wen, Shiki Rezatofighi, Hamid Monash Univ Clayton Vic Australia

ISBN: (纸本)9798350353006

Understanding human social behaviour is crucial in computer vision and robotics. Micro-level observations like individual actions fall short, necessitating a comprehensive approach that considers individual behaviour, intra-group dynamics, and social group levels for a thorough understanding. To address dataset limitations, this paper introduces JRDB-Social, an extension of JRDB [2]. Designed to fill gaps in human understanding across diverse indoor and outdoor social contexts, JRDB-Social provides annotations at three levels: individual attributes, intra-group interactions, and social group context. This dataset aims to enhance our grasp of human social dynamics for robotic applications. Utilizing the recent cutting-edge multi-modal large language models, we evaluated our benchmark to explore their capacity to decipher social human behaviour.

关键词： dataset human attributes human human interaction human social behaviour understanding interaction large language model multifaceted robotic dataset social group social robot vision language model visual question answering visual reasoning

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GRAFIQS: Face Image Quality Assessment Using Gradient Magnitudes

GRAFIQS: Face Image Quality Assessment Using Gradient Magnit...

引用

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

作者： Kolf, Jan Niklas Damer, Naser Boutros, Fadi Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9798350365474

Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset. To achieve that, we propose quantifying the discrepancy in Batch Normalization statistics (BNS), including mean and variance, between those recorded during FR training and those obtained by processing testing samples through the pretrained FR model. We then generate gradient magnitudes of pretrained FR weights by backpropagating the BNS through the pretrained model. The cumulative absolute sum of these gradient magnitudes serves as the FIQ for our approach. Through comprehensive experimentation, we demonstrate the effectiveness of our training-free and quality labeling-free approach, achieving competitive performance to recent state-of-the-art FIQA approaches without relying on quality labeling, the need to train regression networks, specialized architectures, or designing and optimizing specific loss functions.

关键词： Biometrics computer vision Face Image Quality Assessment Face recognition

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ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain vision Transformers

ALGM: Adaptive Local-then-Global Token Merging for Efficient...

引用

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

作者： Norouzi, Narges Orlova, Svetlana de Geus, Daan Dubbelman, Gijs Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9798350353006

This work presents Adaptive Local-then-Global Merging (ALGM), a token reduction method for semantic segmentation networks that use plain vision Transformers. ALGM merges tokens in two stages: (1) In the first network layer, it merges similar tokens within a small local window and (2) halfway through the network, it merges similar tokens across the entire image. This is motivated by an analysis in which we found that, in those situations, tokens with a high cosine similarity can likely be merged without a drop in segmentation quality. With extensive experiments across multiple datasets and network configurations, we show that ALGM not only significantly improves the throughput by up to 100%, but can also enhance the mean IoU by up to +1.1, thereby achieving a better trade-off between segmentation quality and efficiency than existing methods. Moreover, our approach is adaptive during inference, meaning that the same model can be used for optimal efficiency or accuracy, depending on the application. Code is available at https://***/ALGM.

关键词： Efficient vision Transformers Semantic Segmentation Token Merging

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A Dual-Mode Approach for vision-Based Navigation in a Lunar Landing Scenario

A Dual-Mode Approach for Vision-Based Navigation in a Lunar ...

引用

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

作者： Ostrogovich, Luca Del Prete, Roberto Tomasicchio, Giuseppe Longepe, Nicolas Renga, Alfredo Univ Naples Federico II Dept Ind Engn Naples Italy Telespazio SRL Rome Italy ESA ESRIN Lab Frascati Italy

ISBN: (纸本)9798350365474

In this research, a novel approach for autonomous spacecraft navigation, particularly in lunar contexts, is presented, focusing on vision-based techniques. The system incorporates lunar crater recognition in conjunction with feature tracking to enhance the accuracy of spacecraft navigation. This system underwent comprehensive evaluation through a purpose-built software simulation, replicating lunar conditions for thorough evaluation and refinement. The methodology integrates established navigational methods with advanced artificial intelligence algorithms, resulting in significant navigational accuracy. The system demonstrates precise capabilities in determining the spacecraft position, with an average accuracy of approximately 270 m for the absolute navigation mode, while the relative mode exhibited an average error of 27.4 m and 0.8 m in determining the horizontal and vertical lander displacements relative to terrain. Initial tests on embedded systems-akin to those on-board spacecraft-were conducted. These tests are pivotal in demonstrating the system's operational viability within the constraints of limited bandwidth and rapid processing requirements characteristic of space missions. The promising results from these tests suggest potential applicability in real-world space missions, enhancing autonomous navigation capabilities in lunar and potentially other extraterrestrial environments.

关键词： Lunar Navigation Onboard AI Visual Based Navigation

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Dense vision Transformer Compression with Few Samples

Dense Vision Transformer Compression with Few Samples

引用

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

作者： Zhang, Hanxiao Zhou, Yifan Wang, Guo-Hua Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sch Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350353006

Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a leading technique in achieving high accuracy and low latency in few-shot CNN compression. But, few-shot compression for vision Transformers (ViT) remains largely unexplored, which presents a new challenge. In particular, the issue of sparse compression exists in traditional CNN few-shot methods, which can only produce very few compressed models of different model sizes. This paper proposes a novel framework for few-shot ViT compression named DC-ViT. Instead of dropping the entire block, DC-ViT selectively eliminates the attention module while retaining and reusing portions of the MLP module. DC-ViT enables dense compression, which outputs numerous compressed models that densely populate the range of model complexity. DC-ViT outperforms state-of-the-art few-shot compression methods by a significant margin of 10 percentage points, along with lower latency in the compression of ViT and its variants.

关键词： Few-shot Model Compression Pruning vision Transformer

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Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation

Repurposing Diffusion-Based Image Generators for Monocular D...

引用

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

作者： Ke, Bingxin Obukhov, Anton Huang, Shengyu Metzger, Nando Daudt, Rodrigo Caye Schindler, Konrad Swiss Fed Inst Technol Photogrammetry & Remote Sensing Zurich Switzerland

ISBN: (纸本)9798350353006

Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding, so it is not surprising that the rise of deep learning has led to a breakthrough. The impressive progress of monocular depth estimators has mirrored the growth in model capacity, from relatively modest CNNs to large Transformer architectures. Still, monocular depth estimators tend to struggle when presented with images with unfamiliar content and layout, since their knowledge of the visual world is restricted by the data seen during training, and challenged by zero-shot generalization to new domains. This motivates us to explore whether the extensive priors captured in recent generative diffusion models can enable better, more generalizable depth estimation. We introduce Marigold, a method for affine-invariant monocular depth estimation that is derived from Stable Diffusion and retains its rich prior knowledge. The estimator can be fine-tuned in a couple of days on a single GPU using only synthetic training data. It delivers state-of-the-art performance across a wide range of datasets, including over 20% performance gains in specific cases. Project page: https://***.

关键词： ddim ddpm depth estimation diffusion generative LDM vision

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Multi-Modal Hit Detection and Positional Analysis in Padel Competitions

Multi-Modal Hit Detection and Positional Analysis in Padel C...

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

作者： Decorte, Robbe Pare, Martin Vanhaeverbeke, Jelle Taelman, Joachim Slembrouck, Maarten Verstockt, Steven Univ Ghent imec IDLab Ghent Belgium

ISBN: (纸本)9798350365474

Padel is a rapidly growing racquet sport and has gained popularity globally due to its accessibility and exciting gameplay dynamics. Effective coordination between teammates hinges on maintaining an appropriate distance, allowing for seamless transitions between offensive and defensive maneuvers. A balanced inter-player distance and distance to the net not only facilitates efficient communication but also enhances the team's ability to exploit openings in the opponent's defense while minimizing vulnerabilities. We introduce a new open dataset of padel rallies with annotations for hits and player-ball interactions, a predictive model for detecting hits based on audio signals, a reidentification algorithm for pose tracking, and a framework for calculating inter-player and player-net distances during rallies. Our predictive model achieves an average F1-score of 92% for hit detection, demonstrating robust performance across different match conditions. Furthermore, we develop a system for accurately assigning hits to individual players, achieving an overall accuracy of 83.70% for player-specific assignment and 86.83% for team-based assignment.

关键词： computer vision Deep Learning Player Tracking Sound Event Detection Sports Data Science

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Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning

Adaptive Hyper-graph Aggregation for Modality-Agnostic Feder...

引用

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

作者： Qi, Fan Li, Shuai Tianjin Univ Technol Tianjin Peoples R China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353006

In Federated Learning (FL), the issue of statistical data heterogeneity has been a significant challenge to the field's ongoing development. This problem is further exacerbated when clients' data vary in modalities. In response to these issues of statistical heterogeneity and modality incompatibility, we propose the Adaptive Hyper-graph Aggregation framework, a novel solution for Modality-Agnostic Federated Learning. We design a Modular Architecture for Local Model with single modality, setting the stage for efficient intra-modality sharing and inter-modality complementarity. An innovative Global Consensus Prototype Enhancer is crafted to assimilate and broadcast global consensus knowledge within the network. At the core of our approach lies the Adaptive Hyper-graph Learning Strategy, which effectively tackles the inherent challenges of modality incompatibility and statistical heterogeneity within federated learning environments, accomplishing this adaptively even without the server being aware of the clients' modalities. Our approach, tested on three multimodal benchmark datasets, demonstrated strong performance across diverse data distributions, affirming its effectiveness in multimodal federated learning.

关键词： Knowledge engineering computer vision Adaptation models Federated learning Prototypes computer architecture Benchmark testing

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Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling

Label-free Anomaly Detection in Aerial Agricultural Images w...

引用

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

作者： Shikhar, Sambal Sobti, Anupam Plaksha Univ Mohali Punjab India

ISBN: (纸本)9798350365474

Detecting various types of stresses (nutritional, water, nitrogen, etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However, stresses show up in different shapes and sizes across different crop types and varieties. Hence, this is posed as an anomaly detection task in agricultural images. Accurate anomaly detection in agricultural UAV images is vital for early identification of field irregularities. Traditional supervised learning faces challenges in adapting to diverse anomalies, necessitating extensive annotated data. In this work, we overcome this limitation with self-supervised learning using a masked image modeling approach. Masked Autoencoders (MAE) extract meaningful normal features from unlabeled image samples which produces high reconstruction error for the abnormal pixels during reconstruction. To remove the need of using only "normal" data while training, we use an anomaly suppression loss mechanism that effectively minimizes the reconstruction of anomalous pixels and allows the model to learn anomalous areas without explicitly separating "normal" images for training. Evaluation on the Agriculture-vision data challenge shows a 6.3% mIOU score improvement in comparison to prior state of the art in unsupervised and self-supervised methods. A single model generalizes across all the anomaly categories in the Agri-vision Challenge Dataset [5].

关键词： Anomaly Detection computer vision Masked Image Modelling Precision Agriculture UAV Images

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Iterated Learning Improves Compositionality in Large vision-Language Models

Iterated Learning Improves Compositionality in Large Vision-...

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

作者： Zheng, Chenhao Zhang, Jieyu Kembhavi, Aniruddha Krishna, Ranjay Univ Washington Seattle WA 98195 USA Univ Michigan Ann Arbor MI 48109 USA Allen Inst Artificial Intelligence Seattle WA USA

ISBN: (纸本)9798350353006

A fundamental characteristic common to both human vision and natural language is their compositional nature. Yet, despite the performance gains contributed by large vision and language pretraining, recent investigations find that most-if not all-our state-of-the-art vision-language models struggle at compositionality. They are unable to distinguish between images of "a girl in white facing a man in black" and "a girl in black facing a man in white". Moreover, prior work suggests that compositionality doesn't arise with scale: larger model sizes or training data don't help. This paper develops a new iterated training algorithm that incentivizes compositionality. We draw on decades of cognitive science research that identifies cultural transmission-the need to teach a new generation-as a necessary inductive prior that incentivizes humans to develop compositional languages. Specifically, we reframe vision-language contrastive learning as the Lewis Signaling Game between a vision agent and a language agent, and operationalize cultural transmission by iteratively resetting one of the agent's weights during training. After every iteration, this training paradigm induces representations that become "easier to learn", a property of compositional languages: e.g. our model trained on CC3M and CC12M improves standard CLIP by 4.7%, 4.0% respectfully in the SugarCrepe benchmark.

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