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

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

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ahmed Qazi Taha Razzaq Asim Iqbal Tibbling Technologies

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

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 noninvasive 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： Productivity Legged locomotion Limiting Tagging Livestock Hardware pattern recognition

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IrrNet: Spatio-Temporal Segmentation guided Classification for Irrigation Mapping

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

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Oishee Bintey Hoque Department of Computer Science University of Virginia VA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

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.

关键词： Irrigation computer vision conferences pattern recognition Complexity theory Farming

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Coreset Selection for Object Detection

Coreset Selection for Object Detection

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hojun Lee Suyoung Kim Junhoo Lee Jaeyoung Yoo Nojun Kwak Seoul National University NAVER WEBTOON AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Coreset selection is a method for selecting a small, representative subset of an entire dataset. It has been primarily researched in image classification, assuming there is only one object per image. However, coreset selection for object detection is more challenging as an image can contain multiple objects. As a result, much research has yet to be done on this topic. Therefore, we introduce a new approach, Coreset Selection for Object Detection (CSOD). CSOD generates imagewise and classwise representative feature vectors for multiple objects of the same class within each image. Subsequently, we adopt submodular optimization for considering both representativeness and diversity and utilize the representative vectors in the submodular optimization process to select a subset. When we evaluated CSOD on the Pascal VOC dataset, CSOD outperformed random selection by +6.4%p in AP 50 when selecting 200 images.

关键词： computer vision conferences Object detection Vectors pattern recognition Optimization Image classification

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Relational Edge-Node Graph Attention Network for Classification of Micro-Expressions

Relational Edge-Node Graph Attention Network for Classificat...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Rakesh Kumar, Ankith Jain Bhanu, Bir University of California Department of Electrical and Computer Engineering Riverside United States

ISBN: (纸本)9798350302493

Facial micro-expressions (MEs) refer to subtle, transient, and involuntary muscle movements expressing a person's true feelings. This paper presents a novel two-stream relational edge-node graph attention network-based approach to classify MEs in a video by selecting the high-intensity frames and edge-node features that can provide valuable information about the relationship between nodes and structural information in a graph structure. The paper examines the impact of different edge-node features and their relationships on the graphs. The first step involves extracting high-intensity-emotion frames from the video using optical flow. Second, node feature embeddings are calculated using the node location coordinate features and the patch size information of the optical flow across each node location. Additionally, we obtain the global and local structural similarity score using the jaccard's similarity score and radial basis function as the edge features. Third, a self-attention graph pooling layer helps to remove the nodes with lower attention scores based on the top-k selection. As the final step, the network employs a two-stream edge-node graph attention network that focuses on finding correlations among the edge and node features, such as landmark coordinates, optical flow, and global and local edge features. A three-frame graph structure is designed to obtain spatiotemporal information. For 3 and 5 expression classes, the results are compared for SMIC and CASME II databases. © 2023 ieee.

关键词： Optical flows

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Envisioning a Next Generation Extended Reality Conferencing System with Efficient Photorealistic Human Rendering

Envisioning a Next Generation Extended Reality Conferencing ...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Shen, Chuanyue Zhang, Letian Yang, Zhangsihao Mortazavi, Masood Song, Xiyun Peng, Liang Yu, Heather University of Illinois at Urbana-Champaign United States University of Miami United States Arizona State University United States Futurewei Technologies United States

ISBN: (纸本)9798350302493

Meeting online is becoming the new normal. Creating an immersive experience for online meetings is a necessity towards more diverse and seamless environments. Efficient photorealistic rendering of human 3D dynamics is the core of immersive meetings. Current popular applications achieve real-time conferencing but fall short in delivering photorealistic human dynamics, either due to limited 2D space or the use of avatars that lack realistic interactions between participants. Recent advances in neural rendering, such as the Neural Radiance Field (NeRF), offer the potential for greater realism in metaverse meetings. However, the slow rendering speed of NeRF poses challenges for real-time conferencing. We envision a pipeline for a future extended reality metaverse conferencing system that leverages monocular video acquisition and free-viewpoint synthesis to enhance data and hardware efficiency. Towards an immersive conferencing experience, we explore an accelerated NeRF-based free-viewpoint synthesis algorithm for rendering photorealistic human dynamics more efficiently. We show that our algorithm achieves comparable rendering quality while performing training and inference 44.5% and 213% faster than state-of-the-art methods, respectively. Our exploration provides a design basis for constructing metaverse conferencing systems that can handle complex application scenarios, including dynamic scene relighting with customized themes and multi-user conferencing that harmonizes real-world people into an extended world. © 2023 ieee.

关键词： Three dimensional computer graphics

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Recursions Are All You Need: Towards Efficient Deep Unfolding Networks

Recursions Are All You Need: Towards Efficient Deep Unfoldin...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Alhejaili, Rawwad Alfarraj, Motaz Luqman, Hamzah Al-Shaikhi, Ali King Fahd University of Petroleum and Minerals Electrical Engineering Department Dhahran31261 Saudi Arabia King Fahd University of Petroleum and Minerals SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran31261 Saudi Arabia King Fahd University of Petroleum and Minerals Information and Computer Science Department Dhahran31261 Saudi Arabia

ISBN: (纸本)9798350302493

The use of deep unfolding networks in compressive sensing (CS) has seen wide success as they provide both simplicity and interpretability. However, since most deep unfolding networks are iterative, this incurs significant redundancies in the network. In this work, we propose a novel recursion-based framework to enhance the efficiency of deep unfolding models. First, recursions are used to effectively eliminate the redundancies in deep unfolding networks. Secondly, we randomize the number of recursions during training to decrease the overall training time. Finally, to effectively utilize the power of recursions, we introduce a learnable unit to modulate the features of the model based on both the total number of iterations and the current iteration index. To evaluate the proposed framework, we apply it to both ISTA-Net+ and COAST. Extensive testing shows that our proposed framework allows the network to cut down as much as 75% of its learnable parameters while mostly maintaining its performance, and at the same time, it cuts around 21% and 42% from the training time for ISTA-Net+ and COAST respectively. Moreover, when presented with a limited training dataset, the recursive models match or even outperform their respective non-recursive baseline. Codes and pretrained models are available at https://***/Rawwad-Alhejaili/Recursions-Are-All-You-Need. © 2023 ieee.

关键词： Redundancy

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OCMCTrack: Online Multi-Target Multi-Camera Tracking with Corrective Matching Cascade

OCMCTrack: Online Multi-Target Multi-Camera Tracking with Co...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Andreas Specker Fraunhofer IOSB Fraunhofer Center for Machine Learning

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The implementation of multi-target multi-camera tracking systems in indoor environments, including shops and warehouses, facilitates strategic product positioning and the improvement of operational workflows. This paper presents the online multi-target multi-camera tracking framework OCMCTrack, which tracks the 3D positions of people in the world. The proposed framework introduces a novel matching cascade to re-evaluate track assignments dynamically, thus minimizing false positive associations often made by online trackers. Additionally, this work presents three effective methods to enhance the transformation of a person’s position in the image to world coordinates, thereby addressing common inaccuracies in positional reference points. The proposed methodology is able to achieve competitive performance in Track 1 of the 2024 AI City Challenge, demonstrating the effectiveness of the framework.

关键词： Technological innovation computer vision Three-dimensional displays Annotations conferences Urban areas Real-time systems

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Connecting NeRFs, Images, and Text

Connecting NeRFs, Images, and Text

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Francesco Ballerini Pierluigi Zama Ramirez Roberto Mirabella Samuele Salti Luigi Di Stefano University of Bologna

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage. Concurrently, significant progress has been made in multimodal representation learning for text and image data. This paper explores a novel research direction that aims to connect the NeRF modality with other modalities, similar to established methodologies for images and text. To this end, we propose a simple framework that exploits pre-trained models for NeRF representations alongside multimodal models for text and image processing. Our framework learns a bidirectional mapping between NeRF embeddings and those obtained from corresponding images and text. This mapping unlocks several novel and useful applications, including NeRF zero-shot classification and NeRF retrieval from images or text.

关键词： Representation learning computer vision Three-dimensional displays Image processing conferences Neural radiance field pattern recognition

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EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

EfficientViT-SAM: Accelerated Segment Anything Model Without...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zhuoyang Zhang Han Cai Song Han Tsinghua University NVIDIA MIT

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present EfficientViT-SAM, a new family of accelerated segment anything models. We retain SAM’s lightweight prompt encoder and mask decoder while replacing the heavy image encoder with EfficientViT. For the training, we begin with the knowledge distillation from the SAM-ViT-H image encoder to EfficientViT. Subsequently, we conduct end-to-end training on the SA-1B dataset. Benefiting from EfficientViT’s efficiency and capacity, EfficientViT-SAM delivers 48.9× measured TensorRT speedup on A100 GPU over SAM-ViT-H without sacrificing performance. Our code and pre-trained models are released at https://***/mit-han-lab/efficientvit.

关键词： Training Image segmentation computer vision Codes conferences Computational modeling Graphics processing units

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Efficient Skeleton-Based Action recognition for Real-Time Embedded Systems

Efficient Skeleton-Based Action Recognition for Real-Time Em...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nadhira Noor Fabianaugie Jametoni Jinbeom Kim Hyunsu Hong In Kyu Park Dept. of Electrical and Computer Engineering Inha University Incheon Korea Finedigital Inc Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Action recognition is vital for various real-world applications, yet its implementation on embedded systems or edge devices faces challenges due to limited computing and memory resources. Our goal is to facilitate lightweight action recognition on embedded systems by utilizing skeleton-based techniques, which naturally require less computing and memory resources. To achieve this, we propose innovative methodologies and optimizations tailored for embedded deployment, including post-training quantization, optimized model architectures, and efficient resource utilization. By enabling real-time and lightweight action recognition on resource-constrained embedded systems, our research opens up new possibilities for applications in areas like autonomous surveillance, driving, and indoor safety monitoring.

关键词： Embedded systems Accuracy Quantization (signal) Surveillance Memory management Network architecture Real-time systems

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