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IoU-CLIP: IoU-Aware Language-image Model Tuning for Open Vocabulary Object Detection

IoU-CLIP: IoU-Aware Language-Image Model Tuning for Open Voc...

引用

IEEE visual communications and image processing (VCIP)

作者： Mingzhou He Qingbo Wu King Ngi Ngan Yiming Xiao Fanman Meng Heqian Qiu Hongliang Li School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Open vocabulary object detection (OVD), which detects novel categories through detectors trained on base categories, has achieved remarkable advancement attributable to large-scale vision-language models, such as CLIP. The prior OVD works mainly focused on improving the classification accuracy of proposals, ignoring the ability of localization for novel categories. In this work, we propose IoU-aware language-image model tuning (IoU-CLIP) for open vocabulary object detection. Specifically, we construct a region image dataset with different IoU and adopt IoU values as labels to fine-tune the CLIP model to learn IoU-aware and class-agnostic semantic prompts and visual embeddings. The fine-tuned IoU-CLIP can predict IoU scores for proposals, which interact with classification scores. Meanwhile, IoU-aware and class-agnostic visual embeddings are utilized for box regression to enhance the generalization of the localization capability. We evaluate our method on the COCO and LVIS OVD benchmarks, outperforming the baseline (RegionCLIP) by 5.5% AP 50 and 5.8% AP on novel categories, respectively, achieving state-of-the-art performance.

关键词： Location awareness Vocabulary visualization visual communication image processing Semantics Object detection Detectors Proposals Tuning

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Learned Lossless Coding for Ultra-high-speed Spike Streams via Intensity Remapping

Learned Lossless Coding for Ultra-high-speed Spike Streams v...

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IEEE visual communications and image processing (VCIP)

作者： Fanke Dong Chuanmin Jia Wangxuan Institute of Computer Technology Peking University Beijing China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

As a novel bio-inspired imaging device, the spike camera shows remarkable potential in capturing ultra-high-speed motion scenes by simulating the mechanism of the retinal fovea. It achieves a temporal resolution of tens of thousands of Hz through spike emission. However, this capability presents significant challenges in terms of large-scale data storage and transmission, along with stringent fidelity requirements for spike data, thus posing a formidable obstacle to the lossless compression of continuous spike streams. In this paper, we introduce an effective image representation method for spikes, along with an intensity remapping technique to mitigate noise effects in spike streams. Building on this, we propose a learned lossless spike data compression model. To our knowledge, it is the first learning-based model for lossless spike stream compression. Experimental results demonstrate that our method can realize state-of-the-art performance for spike data lossless compression.

关键词： image coding image resolution visual communication Noise Memory image representation Retina Propagation losses Data models Streams

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Learning Multi-dimensional Parallax Prior for Stereo image Super-Resolution 28th

Learning Multi-dimensional Parallax Prior for Stereo Image S...

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28th International conference on Neural Information processing

作者： Li, Changyu Zhang, Dongyang Jiang, Chunlin Xie, Ning Shao, Jie Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Sichuan Artificial Intelligence Res Inst Yibin 644000 Peoples R China

ISBN: (纸本)9783030923099;9783030923105

Recent years have witnessed great success in image super-resolution (SR). However, how to effectively exploit stereo information for the SR purpose is still challenging. This paper focuses on proposing a general solution to stereo image SR. We propose a novel module named Parallax Multi-Dimensional Attention (PMDA) that could not only be seamlessly integrated into most of existing SISR networks but also explore cross-view information from stereo images. Specifically, a pair of stereo images are fed into two identical SISR networks. The extracted middle features are transferred into PMDA to capture the inherent correlation within stereo image pairs. Finally, the internal-view and cross-view information is mixed by SISR network to generate the final output. We also introduce Self Multi-Dimensional Attention (SMDA) to effectively improve the feature representation capacity of a single image. Based on PMDA and SMDA, we design a stereo image SR model named Progressive Attention Stereo SR (PASR), which illustrates the flexibility of PMDA and performance-boosting guided by PMDA and SMDA. Extensive experiments show its superiority in the aspects of visual quality and quantitative comparison.

关键词： image super-resolution Deep learning Stereo image

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Attention to Emotions: Body Emotion Recognition In-the-Wild Using Self-attention Transformer Network 18th

Attention to Emotions: Body Emotion Recognition In-the-Wild ...

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18th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 18th International conference on Computer Graphics Theory and Applications

作者： Paiva, Pedro V. V. Ramos, Josue J. G. Gavrilova, Marina Carvalho, Marco A. G. Univ Estadual Campinas Sch Technol Limeira Brazil Renato Archer IT Ctr Cyber Phys Syst Div Campinas Brazil Univ Calgary Dept Comp Sci Calgary AB Canada

ISBN: (纸本)9783031667428;9783031667435

Body movements are an essential part of non-verbal communication as they help to express and interpret human emotions. The potential of Body Emotion Recognition (BER) is immense, as it can provide insights into user preferences, automate real-time exchanges and enable machines to respond to human emotions. BER finds applications in customer service, healthcare, entertainment, emotion-aware robots, and other areas. While face expression-based techniques are extensively researched, detecting emotions from body movements in the realworld presents several challenges, including variations in body posture, occlusions, and background. Recent research has established the efficacy of transformer deep-learning models beyond the language domain to solve video and image-related problems. A key component of transformers is the self-attention mechanism, which captures relationships among features across different spatial locations, allowing contextual information extraction. In this study, we aim to understand the role of body movements in emotion expression and to explore the use of transformer networks for body emotion recognition. Our method proposes a novel linear projection function of the visual transformer, which enables the transformation of 2D joint coordinates into a conventional matrix representation. Using an original method of contextual information learning, the developed approach enables a more accurate recognition of emotions by establishing unique correlations between individual's body motions over time. Our results demonstrated that the self-attention mechanism was able to achieve high accuracy in predicting emotions from body movements, surpassing the performance of other recent deep-learning methods. In addition, the impact of dataset size and frame rate on classification performance is analyzed.

关键词： Body emotion recognition Affective computing Video and image processing Gait analysis Attention-based design

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Adaptive and Collaborative Multi-scale Alignment for Text-Based Person Search

Adaptive and Collaborative Multi-scale Alignment for Text-Ba...

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IEEE visual communications and image processing (VCIP)

作者： Xinxin Yang Renjie Pan Hua Yang Shanghai Key Lab of Digital Media Processing and Transmission Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

Text-to-image person search is challenging due to the cross-scale correspondences and information inequality between modalities. Specifically, images and text are complexly linked at different scales and images are usually more informative and complete than text. It is crucial to establish semantic correlations between modalities and focus on task-relevant information in images. In this paper, we propose a novel Adaptive and Collaborative Multi-scale Alignment network (ACMA) for text-based person search that learns semantically consistent and information-aligned multi-modal representations. Firstly, we introduce a novel joint embedding module that adaptively integrates features of different pixels and words, thereby extracting semantically consistent multi-modal features at different scales. Second, we design a cross-modal fusion feature-based auxiliary visual branch to guide the extraction of key visual features that are beneficial for cross-modal matching. Extensive experiments validate that ACMA outperforms the state-of-the-art method.

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Leveraging Content Based image Retrieval Using Data Mining for Efficient image Exploration 1

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4th International conference on Mobile Radio communications and 5G Networks, MRCN 2023

作者： Kaur, Jaspreet Gupta, Divya Singh, Amrinder Shah, Syed Hassan Ahmed Department of Computer Science and Engineering Chandigarh University Mohali 140413 India University Institute of Teachers Training and Research ChandigarhUniversity Punjab Mohali India California State University FullertonCA United States

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

A content-based image Retrieval (CBIR) has become an essential tool for managing and searching large-scale images. However, the accuracy and performance of CBIR systems can be improved by combining data mining techniques. Content-based retrieval (CBR) uses the properties and characteristics of the content itself to search for and retrieve information from a big database instead of depending on text or metadata. CBR is very helpful in research, where it is necessary to swiftly and effectively examine vast amounts of data. According to the results, data mining techniques can considerably increase the retrieval process accuracy and effectiveness. Similar photos can be grouped together using clustering algorithms, common patterns of visual features can be found using association rule mining, and images can be classified using classification techniques. In order to create a system for content-based image retrieval and processing, we studied the retrieval of images from huge databases using a variety of feature extraction and matching techniques. The demand for CBIR development came as a result of the sharp rise in image database volumes and their widespread use in several applications. The description of basic feature extraction methods including texture, color, and form is provided in this study. Once these features are retrieved and then used for comparing photos based on similarity. This study suggests a cutting-edge system design for CBIR system that integrates content-based picture and color analysis with data mining methods. This work is intended to develop a segmentation module for the CBIR system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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image Coding for Machines based on Non-Uniform Importance Allocation

Image Coding for Machines based on Non-Uniform Importance Al...

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IEEE visual communications and image processing (VCIP)

作者： Yunpeng Qi Ruoyu Feng Zhizheng Zhang Zhibo Chen University of Science and Technology of China Hefei China Microsoft Research Beijing China

In the Internet era, the explosive growth of media data processing poses significant challenges for the research of image Coding for Machines (ICM) in improving the efficiency of AI models while reducing the burdens of data storage and transmission. Existing ICM methods face challenges in achieving sufficient generalization ability when developing a single codec to handle diverse downstream tasks. To address these issues, we propose a unified ICM framework that facilitates diverse downstream tasks with a novel importance allocation mechanism. Equipped with a spatially variable-rate image compression codec, we introduce two options: online updating and offline predicting the non-uniform quality map, which governs the quality distribution of reconstructed images based on specific downstream tasks. Our proposed method is rigorously evaluated through extensive experiments on diverse and comprehensive fine-grained image classification datasets. The experiment results conclusively demonstrate the effectiveness of the proposed method in achieving a superior rate-distortion trade-off for ICM.

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Coding of Multi-Focused Plenoptic image using Disparity Shift and Sharpness-Aware Constraints

Coding of Multi-Focused Plenoptic Image using Disparity Shif...

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IEEE visual communications and image processing (VCIP)

作者： Thuc Nguyen Huu Vinh Van Duong Jonghoon Yim Byeungwoo Jeon Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea

Multi-focused plenoptic images possess many special characteristics related to the micro-images (MIs) array, which are expected to be useful in further increasing its compression performance. Those special characteristics come from the much overlap and sharpness variance among its micro-images, and proper handling of such properties can lead to better patch-based prediction. In this paper, for multi-focused plenoptic image data, we design a new prediction model taking into account the disparity shift constraint coming from the overlaps and the sharpness variation. Experiment results show coding gain respectively of 21% over the HEVC Intra and 27% when the proposed method is combined with the Intra Block Copy (IBC) tool which is reported very effective in plenoptic image coding.

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Foreground-Background Disentanglement based on image and Feature Co-Learning for 3D-Aware Generative Models

Foreground-Background Disentanglement based on Image and Fea...

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IEEE visual communications and image processing (VCIP)

作者： Sanghyuk Lee Daeha Kim Byung Cheol Song Department of Electrical and Computer Engineering Inha University Incheon Republic of Korea

Recently, studies on generative models using 3D information are active. GIRAFFE, one of the latest 3D-aware generative models, shows better feature disentanglement than existing generative models because it generates an image through volume rendering of independently formed 3D neural feature fields. However, GIRAFFE still suffers from an issue where foreground and background disentanglement is not smooth. In order to accomplish better disentanglement performance than GIRAFFE, we propose co-adversarial learning of the generative model at both image- and feature-levels. As a result of rich simulation experiments, the proposed generative model can produce photo-realistic images with only fewer parameters than existing 3D-aware generative models, along with excellent foreground-background disentanglement performance.

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3rd International conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd International conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819708918

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-Learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite image Classification;primitive Roots and Euler Totient Function-Based Progressive visual Cryptography;approach for Fire Detection Using image processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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