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A Data-Driven Approach based on Dynamic Mode Decomposition for Efficient Encoding of Dynamic Light Fields

A Data-Driven Approach based on Dynamic Mode Decomposition f...

引用

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

作者： Joshitha Ravishankar Sally Khaidem Mansi Sharma Department of Electrical Engineering Indian Institute of Technology Madras India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

Dynamic light fields provide a richer, more realistic 3D representation of a moving scene. However, this leads to higher data rates since excess storage and transmission requirements are needed. We propose a novel approach to efficiently represent and encode dynamic light field data for display applications based on dynamic mode decomposition (DMD). Acquired images are firstly obtained through optimized coded aperture patterns for each temporal frame/camera viewpoint of a dynamic light field. The underlying spatial, angular, and temporal correlations are effectively exploited by a data-driven DMD on these acquired images arranged as time snapshots. Next, High Efficiency Video Coding (HEVC) removes redundancies in light field data, including intra-frame and inter-frame redundancies, while maintaining high reconstruction quality. The proposed scheme is the first of its kind to treat light field videos as mathematical dynamical systems, leverage on dynamic modes of acquired images, and gain flexible coding at various bitrates. Experimental results demonstrate our scheme’s superior compression efficiency and bitrate savings compared to the direct encoding of acquired images using HEVC codec.

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Two-Person Interaction Augmentation with Skeleton Priors

Two-Person Interaction Augmentation with Skeleton Priors

引用

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

作者： Baiyi Li Edmond S. L. Ho Hubert P. H. Shum He Wang University of Leeds United Kingdom University of Glasgow United Kingdom Durham University United Kingdom University College London United Kingdom

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Close and continuous interaction with rich contacts is a crucial aspect of human activities (e.g. hugging, dancing) and of interest in many domains like activity recognition, motion prediction, character animation, etc. However, acquiring such skeletal motion is challenging. While direct motion capture is expensive and slow, motion editing/generation is also non-trivial, as complex contact patterns with topological and geometric constraints have to be retained. To this end, we propose a new deep learning method for two-body skeletal interaction motion augmentation, which can generate variations of contact-rich interactions with varying body sizes and proportions while retaining the key geometric/topological relations between two bodies. Our system can learn effectively from a relatively small amount of data and generalize to drastically different skeleton sizes. Through exhaustive evaluation and comparison, we show it can generate high-quality motions, has strong generalizability and outperforms traditional optimization-based methods and alternative deep learning solutions.

关键词： Deep learning computer vision conferences Activity recognition Animation Skeleton Motion capture

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Generation of Structurally Realistic Retinal Fundus Images with Diffusion Models

Generation of Structurally Realistic Retinal Fundus Images w...

引用

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

作者： Sojung Go Younghoon Ji Sang Jun Park Soochahn Lee Department of Ophthalmology Seoul National University College of Medicine Seoul National University Bundang Hospital Seongnam Korea VUNO Inc. Seoul Korea School of Electrical Engineering Kookmin University Seoul Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce a new technique for generating retinal fundus images that have anatomically accurate vascular structures, using diffusion models. We generate artery/vein masks to create the vascular structure, which we then condition to produce retinal fundus images. The proposed method can generate high-quality images with more realistic vascular structures and can create a diverse range of images based on the strengths of the diffusion model. We present quantitative evaluations that demonstrate the performance improvement using our method for data augmentation on vessel segmentation and artery/vein classification. We also present Turing test results by clinical experts, showing that our generated images are difficult to distinguish with real images. We believe that our method can be applied to construct stand-alone datasets that are irrelevant of patient privacy.

关键词： Image segmentation Data privacy computer vision Accuracy conferences Retina Diffusion models

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CrisisHateMM: Multimodal Analysis of Directed and Undirected Hate Speech in Text-Embedded Images from Russia-Ukraine Conflict

CrisisHateMM: Multimodal Analysis of Directed and Undirected...

引用

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

作者： Aashish Bhandari Siddhant B. Shah Surendrabikram Thapa Usman Naseem Mehwish Nasim Department of CSE Delhi Technological University India Department of Computer Science Virginia Tech USA School of Computer Science The University of Sydney Australia School of Physics Mathematics and Computing The University of Western Australia Australia College of Science and Engineering Flinders University Australia

Text-embedded images are frequently used on social media to convey opinions and emotions, but they can also be a medium for disseminating hate speech, propaganda, and extremist ideologies. During the Russia-Ukraine war, both sides used text-embedded images extensively to spread propaganda and hate speech. To aid in moderating such content, this paper introduces CrisisHateMM, a novel multimodal dataset of over 4,700 text-embedded images from the Russia-Ukraine conflict, annotated for hate and non-hate speech. The hate speech is annotated for directed and undirected hate speech, with directed hate speech further annotated for individual, community, and organizational targets. We benchmark the dataset using unimodal and multimodal algorithms, providing insights into the effectiveness of different approaches for detecting hate speech in text-embedded images. Our results show that multimodal approaches outperform unimodal approaches in detecting hate speech, highlighting the importance of combining visual and textual features. This work provides a valuable resource for researchers and practitioners in automated content moderation and social media analysis. The CrisisHateMM dataset and codes are made publicly available at https://***/aabhandari/CrisisHateMM.

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Exploring Facial Expression recognition through Semi-Supervised Pre-training and Temporal Modeling

Exploring Facial Expression Recognition through Semi-Supervi...

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

作者： Jun Yu Zhihong Wei Zhongpeng Cai Gongpeng Zhao Zerui Zhang Yongqi Wang Guochen Xie Jichao Zhu Wangyuan Zhu Qingsong Liu Jiaen Liang University of Science and Technology of China Unisound AI Technology Co. Ltd

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Facial Expression recognition (FER) plays a crucial role in computer vision and finds extensive applications across various fields. This paper aims to present our approach for the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition, scheduled to be held at CVPR2024. In the facial expression recognition task, the limited size of the FER dataset poses a challenge to the expression recognition model’s generalization ability, resulting in subpar recognition performance. To address this problem, we employ a Semi-supervised learning technique to generate expression category pseudo labels for unlabeled face data. At the same time, we uniformly sampled the labeled facial expression samples and implemented a debiased feedback learning strategy to address the problem of category imbalance in the dataset and the possible data bias in semi-supervised learning. Moreover, to further compensate for the limitation and bias of features obtained only from static images, we introduced a Temporal Encoder to capture temporal relationships between neighbouring expression image features. In the 6th ABAW competition, our method achieved the third place in the official test set, a result that fully demonstrates the effectiveness and competitiveness of our proposed method.

关键词： computer vision Image recognition Accuracy Face recognition conferences Genetic expression Semisupervised learning

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PCQA: A Strong Baseline for AIGC Quality Assessment Based on Prompt Condition

PCQA: A Strong Baseline for AIGC Quality Assessment Based on...

引用

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

作者： Xi Fang Weigang Wang Xiaoxin Lv Jun Yan DP Technology Cisco Shopee TongJi University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The development of Large Language Models (LLM) and Diffusion Models brings the boom of Artificial Intelligence Generated Content (AIGC). It is essential to build an effective quality assessment framework to provide a quantifiable evaluation of different images or videos based on the AIGC technologies. The content generated by AIGC methods is driven by the crafted prompts. Therefore, it is intuitive that the prompts can also serve as the foundation of the AIGC quality assessment. This study proposes an effective AIGC quality assessment (QA) framework. First, we propose a hybrid prompt encoding method based on a dual-source CLIP (Contrastive Language-Image Pre-Training) text encoder to understand and respond to the prompt conditions. Second, we propose an ensemble-based feature mixer module to effectively blend the adapted prompt and vision features. The empirical study practices in two datasets: AIGIQA-20K (AI-Generated Image Quality Assessment database) and T2VQA-DB (Text-to-Video Quality Assessment DataBase), which validates the effectiveness of our proposed method: Prompt Condition Quality Assessment (PCQA). Our proposed simple and feasible framework may promote research development in the multimodal generation field.

关键词： Image quality Databases Large language models Semantics Quality assessment pattern recognition Ensemble learning

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Towards Learning Image Similarity from General Triplet Labels

Towards Learning Image Similarity from General Triplet Label...

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

作者： Radu Dondera Greenfield Vision

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Metric learning for images has so far focused overwhelmingly on a class-based definition of similarity: two images are similar if they belong to the same class and dissimilar otherwise. Impressive results were achieved on datasets for fine grained categorization, but performance is nearing saturation. Recent work in neuroscience and psychology produced datasets with other types of similarity labels, e.g. the outlier in a group of three, but traditional metric learning methods are ill-suited to such data because of low density of labels. To overcome this difficulty, we propose a novel approach in the teacher-student learning paradigm. Multiple teacher models learn to embed images based only on relations with other images, and then a student model learns to embed images based on both content and dense relations provided by the teachers. We show significant improvement over existing triplet based metric learning methods, both in result quality and in training efficiency. Additionally, through experiments on class based datasets, we show the generality of approaching metric learning via knowledge transfer. Code is available at ***/greenfieldvision/tdml.

关键词： Measurement Training Learning systems Visualization Neuroscience Psychology Focusing

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Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in computer vision Tasks for Robotic Grasping on the Edge

Fast GraspNeXt: A Fast Self-Attention Neural Network Archite...

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

作者： Alexander Wong Yifan Wu Saad Abbasi Saeejith Nair Yuhao Chen Mohammad Javad Shafiee Vision and Image Processing Research Group University of Waterloo Waterloo Artificial Intelligence Institute Waterloo ON DarwinAI Corp. Waterloo ON

Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architectural and computational complexity can result in poor suitability for deployment on embedded devices that are typically leveraged in robotic arms for real-world manufacturing and warehouse environments. As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments. Motivated by this, we propose Fast GraspNeXt, a fast self-attention neural network architecture tailored for embedded multi-task learning in computer vision tasks for robotic grasping. To build Fast GraspNeXt, we leverage a generative network architecture search strategy with a set of architectural constraints customized to achieve a strong balance between multitask learning performance and embedded inference efficiency. Experimental results on the MetaGraspNet benchmark dataset show that the Fast GraspNeXt network design achieves the highest performance (average precision (AP), accuracy, and mean squared error (MSE)) across multiple computer vision tasks when compared to other efficient multi-task network architecture designs, while having only 17.8M parameters (about >5× smaller), 259 GFLOPs (as much as >5× lower) and as much as >3.15× faster on a NVIDIA Jetson TX2 embedded processor.

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Camera-based Recovery of Cardiovascular Signals from Unconstrained Face Videos using an Attention Network

Camera-based Recovery of Cardiovascular Signals from Unconst...

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

作者： Yogesh Deshpande Surendrabikram Thapa Abhijit Sarkar A. Lynn Abbott Bradley Department of Electrical and Computer Engineering Virginia Tech USA Department of Computer Science Virginia Tech USA Virginia Tech Transportation Institute USA

This paper addresses the problem of recovering the shape morphology of blood volume pulse (BVP) information from a video of a person’s face. Video-based remote plethysmography methods have shown promising results in estimating vital signs such as heart rate and breathing rate. However, recovering the instantaneous pulse rate signals is still a challenge for the community. This is due to the fact that most of the previous methods concentrate on capturing the temporal average of the cardiovascular signals. In contrast, we present an approach in which BVP signals are extracted with a focus on the recovery of the signal shape morphology as a generalized form for the computation of physiological metrics. We also place emphasis on allowing natural movements by the subject. Furthermore, our system is capable of extracting individual BVP instances with sufficient signal detail to facilitate candidate re-identification. These improvements have resulted in part from the incorporation of a robust skin-detection module into the overall imaging-based photoplethysmography (iPPG) framework. We present extensive experimental results using the challenging UBFC-Phys dataset and the well-known COHFACE dataset. The source code is available at https://***/yogeshd21/CVPM-2023-iPPG-Paper.

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Motion Matters: Difference-based Multi-scale Learning for Infrared UAV Detection

Motion Matters: Difference-based Multi-scale Learning for In...

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

作者： Ruian He Shili Zhou Ri Cheng Yuqi Sun Weimin Tan Bo Yan School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University Shanghai China

Unmanned Aerial Vehicle (UAV) detection in the wild is a challenging task due to the presence of background noise and the varying size of the object. To address these obstacles, we propose a novel learning framework for robust UAV detectors, which we call Difference-based Multi-scale Learning (DML). We argue that motion information matters in UAV detection because of the low recognition in one frame. Our method utilizes the frame difference of multiple previous frames, extracting motion information and blocking background noise. We also fuse multiple spatial-temporal scales for training and inferencing, enabling fusion from different sources. In addition, to better evaluate the performance of UAV detection in different scales, we propose Multi-Scale Average Precision (MSAP) metric to aggregate the detection accuracy over multiple scales. Through extensive experiments, we demonstrate that our proposed approach improves the detection accuracy of baseline models. Notably, we achieve SOTA performance in the 3rd Anti-UAV Challenge, with 2nd place in Track 2 and 4th place in Track 1. 1

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