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A General Framework for Jersey Number recognition in Sports Video

A General Framework for Jersey Number Recognition in Sports ...

引用

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

作者： Koshkina, Maria Elder, James H. York Univ Toronto ON Canada

ISBN: (纸本)9798350365474

Jersey number recognition is an important task in sports video analysis, partly due to its importance for long-term player tracking. It can be viewed as a variant of scene text recognition. However, there is a lack of published attempts to apply scene text recognition models on jersey number data. Here we introduce a novel public jersey number recognition dataset for hockey and study how scene text recognition methods can be adapted to this problem. We address issues of occlusions and assess the degree to which training on one sport (hockey) can be generalized to another (soccer). For the latter, we also consider how jersey number recognition at the single-image level can be aggregated across frames to yield tracklet-level jersey number labels. We demonstrate high performance on image- and tracklet-level tasks, achieving 91.4% accuracy for hockey images and 87.4% for soccer tracklets. Code, models, and data are available at https://***/mkoshkina/jersey-number-pipeline.

关键词： jersey number dataset jersey number recognition player identification scene text recognition

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Text-image Alignment for Diffusion-based Perception

Text-image Alignment for Diffusion-based Perception

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

作者： Kondapanenil, Neehar Marksl, Markus Knott, Manuel Guimaraes, Rogerio Perona, Pietro CALTECH Pasadena CA 91125 USA Swiss Data Sci Ctr Empa ETH Zurich Zurich Switzerland

ISBN: (纸本)9798350353006

Diffusion models are generative models with impressive text-to-image synthesis capabilities and have spurred a new wave of creative methods for classical machine learning tasks. However, the best way to harness the perceptual knowledge of these generative models for visual tasks is still an open question. Specifically, it is unclear how to use the prompting interface when applying diffusion backbones to vision tasks. We find that automatically generated captions can improve text-image alignment and significantly enhance a model's cross-attention maps, leading to better perceptual performance. Our approach improves upon the current state-of-the-art (SOTA) in diffusion-based semantic segmentation on ADE20K and the current overall SOTA for depth estimation on NYUv2. Furthermore, our method generalizes to the cross-domain setting. We use model personalization and caption modifications to align our model to the target domain and find improvements over unaligned baselines. Our cross-domain object detection model, trained on Pascal VOC, achieves SOTA results on Watercolor2K. Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving. Project page: ***/TADP/Code page: ***/damaggu/TADP

关键词： ADE20K Alignment Cityscapes Cross-Attention Cross-Domain Depth Estimation Diffusion Domain Shift Generative Models Model Personalization NYUv2 Object Detection Segmentation vision-Language Models

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SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection

SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object D...

引用

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

作者： Liu, Mingxuan Hayes, Tyler L. Ricci, Elisa Csurka, Gabriela Volpi, Riccardo Univ Trento Trento Italy NAVER LABS Europe Meylan France Fdn Bruno Kessler Trento Italy

ISBN: (纸本)9798350353006

Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference. However, our initial investigation indicates that existing OvOD detectors exhibit significant variability when dealing with vocabularies across various semantic granularities, posing a concern for real-world deployment. To this end, we introduce Semantic Hierarchy Nexus (SHiNe), a novel classifier that uses semantic knowledge from class hierarchies. It runs offline in three steps: i) it retrieves relevant super-/sub-categories from a hierarchy for each target class;ii) it integrates these categories into hierarchy-aware sentences;iii) it fuses these sentence embeddings to generate the nexus classifier vector. Our evaluation on various detection benchmarks demonstrates that SHiNe enhances robustness across diverse vocabulary granularities, achieving up to +31.9% mAP50 with ground truth hierarchies, while retaining improvements using hierarchies generated by large language models. Moreover, when applied to open-vocabulary classification on ImageNet-1k, SHiNe improves the CLIP zero-shot baseline by +2.8% accuracy. SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector, without incurring additional computational overhead during inference. The code is open source.

关键词： Object Detection Open-vocabulary vision-Language

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Semantic-Aware Multi-Label Adversarial Attacks

Semantic-Aware Multi-Label Adversarial Attacks

引用

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

作者： Mahmood, Hassan El Hamifar, Ehsan Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9798350353006

Despite its importance, generating attacks for multi-label learning (MLL) models has received much less attention compared to multi-class recognition. Attacking an MLL model by optimizing a loss on the target set of labels has often the undesired consequence of changing the predictions for other labels. On the other hand, adding a loss on the remaining labels to keep them fixed leads to highly negatively correlated gradient directions, reducing the attack effectiveness. In this paper, we develop a framework for crafting effective and semantic-aware adversarial attacks for MLL. First, to obtain an attack that leads to semantically consistent predictions across all labels, we find a minimal superset of the target labels, referred to as consistent target set. To do so, we develop an efficient search algorithm over a knowledge graph, which encodes label dependencies. Next, we propose an optimization that searches for an attack that modifies the predictions of labels in the consistent target set while ensuring other labels will not get affected. This leads to an efficient algorithm that projects the gradient of the consistent target set loss onto the orthogonal direction of the gradient of the loss on other labels. Our framework can generate attacks on different target set sizes and for MLL with thousands of labels (as in OpenImages). Finally, by extensive experiments on three datasets and several MLL models, we show that our method generates both successful and semantically consistent attacks.(1)

关键词： Adversarial Attacks Adversarial Robustness Multi-label Attacks Multi-label recognition Semantic Attacks

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SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge

SOK-Bench: A Situated Video Reasoning Benchmark with Aligned...

引用

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

作者： Wang, Andong Wu, Bo Chen, Sunli Chen, Zhenfang Guan, Haotian Lee, Wei-Ning Li, Li Erran Gan, Chuang Univ Hong Kong Hong Kong Peoples R China MIT IBM Watson AI Lab Cambridge MA USA Tsinghua Univ Beijing Peoples R China AWS AI Seattle WA USA UMass Amherst Amherst MA USA

ISBN: (纸本)9798350353006

Learning commonsense reasoning from visual contexts and scenes in real-world is a crucial step toward advanced artificial intelligence. However, existing video reasoning benchmarks are still inadequate since they were mainly designed for factual or situated reasoning and rarely involve broader knowledge in the real world. Our work aims to delve deeper into reasoning evaluations, specifically within dynamic, open-world, and structured context knowledge. We propose a new benchmark (SOK-Bench), consisting of 44K questions and 10K situations with instance-level annotations depicted in the videos. The reasoning process is required to understand and apply situated knowledge and general knowledge for problem-solving. To create such a dataset, we propose an automatic and scalable generation method to generate question-answer pairs, knowledge graphs, and rationales by instructing the combina-tions of LLMs and MLLMs. Concretely, we first extract observable situated entities, relations, and processes from videos for situated knowledge and then extend to open-world knowledge beyond the visible content. The task generation is facilitated through multiple dialogues as iterations and subsequently corrected and refined by our designed self-promptings and demonstrations. With a corpus of both explicit situated facts and implicit commonsense, we generate associated question-answer pairs and reasoning processes, finally followed by manual reviews for quality assurance. We evaluated recent mainstream large vision-language models on the benchmark and found several insightful conclusions. For more information, please refer to our benchmark at ***/SOKBench.

关键词： benchmark commonsense reasoning video understanding vision language foundation models visual reasoning

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AnimalFormer: Multimodal vision Framework for Behavior-based Precision Livestock Farming

AnimalFormer: Multimodal Vision Framework for Behavior-based...

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

作者： Qazi, Ahmed Razzaq, Taha Iqbal, Asim Tibbling Technol Redmond WA 98052 USA

ISBN: (纸本)9798350365474

We introduce a multimodal vision framework for precision livestock farming, harnessing the power of GroundingDINO, HQSAM, and ViTPose models. This integrated suite enables comprehensive behavioral analytics from video data without invasive animal tagging. GroundingDINO generates accurate bounding boxes around livestock, while HQSAM segments individual animals within these boxes. ViTPose estimates key body points, facilitating posture and movement analysis. Demonstrated on a sheep dataset with grazing, running, sitting, standing, and walking activities, our framework extracts invaluable insights: activity and grazing patterns, interaction dynamics, and detailed postural evaluations. Applicable across species and video resolutions, this framework revolutionizes non-invasive livestock monitoring for activity detection, counting, health assessments, and posture analyses. It empowers data-driven farm management, optimizing animal welfare and productivity through AI-powered behavioral understanding.

关键词： Livestock

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NICE: cvpr 2023 Challenge on Zero-shot Image Captioning

NICE: CVPR 2023 Challenge on Zero-shot Image Captioning

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

作者： Kim, Taehoon Ahn, Pyunghwan Kim, Sangyun Lee, Sihaeng Marsden, Mark Sala, Alessandra Kim, Seung Hwan Han, Bohyung Lee, Kyoung Mu Lee, Honglak Bae, Kyounghoon Wu, Xiangyu Gao, Yi Zhang, Hailiang Yang, Yang Guo, Weili Lu, Jianfeng Oh, Youngtaek Cho, Jae Won Kim, Dong-Jin Kweon, In So Kim, Junmo Kang, Wooyoung Jhoo, Won Young Roh, Byungseok Mun, Jonghwan Oh, Solgil Ak, Kenan Emir Lee, Gwang-Gook Xu, Yan Shen, Mingwei Hwang, Kyomin Shin, Wonsik Lee, Kamin Park, Wonhark Lee, Dongkwan Kwak, Nojun Wang, Yujin Wang, Yimu Gu, Tiancheng Lv, Xingchang Sun, Mingmao

ISBN: (纸本)9798350365474

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project1 and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.

关键词： Image captioning Multimodal representation vision-language models

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IrrNet: Spatio-Temporal Segmentation guided Classification for Irrigation Mapping

IrrNet: Spatio-Temporal Segmentation guided Classification f...

引用

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

作者： Hoque, Oishee Bintey Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA

ISBN: (纸本)9798350365474

Irrigation systems can vary widely in scale, from smallscale subsistence farming to large commercial agriculture (see Fig. 1 ). The heterogeneity in irrigation practices and systems across different regions adds to the complexity of mapping (see Fig. 1 ). Distinguishing between irrigated and non-irrigated areas is challenging due to the spectral characteristics of various irrigation systems and practices across different regions, further complicating the task of mapping different types of irrigation. For example, rainfed agriculture is prevalent in the Midwest, Southeast, and parts of the Northeast U.S., while irrigation is common in arid Western and Southwestern states. Rainfed farming can result in highly variable patterns of cultivation. Farmers may practice rainfed agriculture in some fields while irrigating others, leading to a complex mosaic of irrigated and non-irrigated areas within the same region. © 2024 ieee.

关键词： Deep Learning Irrigation Mapping MTL Remote Sensing Segmentation Transfer Learning vision in Agriculture

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Harnessing Large Language Models for Training-free Video Anomaly Detection

Harnessing Large Language Models for Training-free Video Ano...

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

作者： Zanella, Luca Menapace, Willi Mancini, Massimiliano Wang, Yiming Ricci, Elisa Univ Trento Trenton NJ 38122 USA Fdn Bruno Kessler Trenton NJ USA

ISBN: (纸本)9798350353006

Video anomaly detection (VAD) aims to temporally locate abnormal events in a video. Existing works mostly rely on training deep models to learn the distribution of normality with either video-level supervision, one-class supervision, or in an unsupervised setting. Training-based methods are prone to be domain-specific, thus being costly for practical deployment as any domain change will involve data collection and model training. In this paper, we radically depart from previous efforts and propose LAnguage-based VAD (LAVAD), a method tackling VAD in a novel, training-free paradigm, exploiting the capabilities of pre-trained large language models (LLMs) and existing vision-language models (VLMs). We leverage VLM-based captioning models to generate textual descriptions for each frame of any test video. With the textual scene description, we then devise a prompting mechanism to unlock the capability of LLMs in terms of temporal aggregation and anomaly score estimation, turning LLMs into an effective video anomaly detector. We further leverage modality-aligned VLMs and propose effective techniques based on cross-modal similarity for cleaning noisy captions and refining the LLM-based anomaly scores. We evaluate LAVAD on two large datasets featuring real-world surveillance scenarios (UCF-Crime and XD-Violence), showing that it outperforms both unsupervised and one-class methods without requiring any training or data collection.

关键词： large language models video anomaly detection vision-language models

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Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding

Chat-UniVi: Unified Visual Representation Empowers Large Lan...

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

作者： Jin, Peng Takanobu, Ryuichi Zhang, Wancai Cao, Xiaochun Yuan, Li Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Peking Univ Ai Sci AI4S Preferred Program Shenzhen Grad Sch Shenzhen Peoples R China Nari Technol Co Ltd Beijing Peoples R China Sun Yat Sen Univ Sch Cyber Sci & Tech Shenzhen Campus Shenzhen Peoples R China

ISBN: (纸本)9798350353006

Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multi-modal conversations. However, existing methods encounter challenges in effectively handling both image and video understanding, particularly with limited visual tokens. In this work, we introduce Chat-UniVi, a Unified vision-language model capable of comprehending and engaging in conversations involving images and videos through a unified visual representation. Specifically, we employ a set of dynamic visual tokens to uniformly represent images and videos. This representation framework empowers the model to efficiently utilize a limited number of visual tokens to simultaneously capture the spatial details necessary for images and the comprehensive temporal relationship required for videos. Moreover, we leverage a multi-scale representation, enabling the model to perceive both high-level semantic concepts and low-level visual details. Notably, ChatUniVi is trained on a mixed dataset containing both images and videos, allowing direct application to tasks involving both mediums without requiring any modifications. Extensive experimental results demonstrate that Chat-UniVi consistently outperforms even existing methods exclusively designed for either images or videos. Code is available at https://***/PKU-YuanGroup/Chat-UniVi.

关键词： Image Understanding Large Language Models Video Understanding vision Language Model

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