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arXiv 2023年

作者： Sun, Wei Wen, Wen Min, Xiongkuo Lan, Long Zhai, Guangtao Ma, Kede The Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science City University of Hong Kong Kowloon Hong Kong The Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Institute of Image Communication and Information Processing The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science The Shenzhen Research Institute City University of Hong Kong Kowloon Hong Kong

Blind video quality assessment (BVQA) plays an indispensable role in monitoring and improving the end-users’ viewing experience in various real-world video-enabled media applications. As an experimental field, the improvements of BVQA models have been measured primarily on a few human-rated VQA datasets. Thus, it is crucial to gain a better understanding of existing VQA datasets in order to properly evaluate the current progress in BVQA. Towards this goal, we conduct a first-of-its-kind computational analysis of VQA datasets via designing minimalistic BVQA models. By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations. By comparing the quality prediction performance of different model variants on eight VQA datasets with realistic distortions, we find that nearly all datasets suffer from the easy dataset problem of varying severity, some of which even admit blind image quality assessment (BIQA) solutions. We additionally justify our claims by comparing our model generalization capabilities on these VQA datasets, and by ablating a dizzying set of BVQA design choices related to the basic building blocks. Our results cast doubt on the current progress in BVQA, and meanwhile shed light on good practices of constructing next-generation VQA datasets and models. Code is available at https://***/sunwei925/***. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Complete b-symbol weight distribution of some irreducible cyclic codes

arXiv

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arXiv 2021年

作者： Zhu, Hongwei Shi, Minjia Özbudak, Ferruh Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei230601 China Department of Mathematics Institute of Applied Mathematics Middle East Technical University Ankara Turkey

Recently, b-symbol codes are proposed to protect against b-symbol errors in b-symbol read channels. It is an interesting subject of study to consider the complete b-symbol weight distribution of cyclic codes since b-symbol metric is a generalization for Hamming metric. The complete b-symbol Hamming weight distribution of irreducible codes is known in only a few cases. In this paper, we give a complete b-symbol Hamming weight distribution of a class of irreducible codes with two nonzero b-symbol Hamming weights. © 2021, CC0.

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Env-QA: A Video Question Answering Benchmark for Comprehensive Understanding of Dynamic Environments

Env-QA: A Video Question Answering Benchmark for Comprehensi...

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International Conference on Computer Vision (ICCV)

作者： Difei Gao Ruiping Wang Ziyi Bai Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9781665428132

Visual understanding goes well beyond the study of images or videos on the web. To achieve complex tasks in volatile situations, the human can deeply understand the environment, quickly perceive events happening around, and continuously track objects’ state changes, which are still challenging for current AI systems. To equip AI system with the ability to understand dynamic ENVironments, we build a video Question Answering dataset named Env-QA. Env-QA contains 23K egocentric videos, where each video is composed of a series of events about exploring and interacting in the environment. It also provides 85K questions to evaluate the ability of understanding the composition, layout, and state changes of the environment presented by the events in videos. Moreover, we propose a video QA model, Temporal Segmentation and Event Attention network (TSEA), which introduces event-level video representation and corresponding attention mechanisms to better extract environment information and answer questions. Comprehensive experiments demonstrate the effectiveness of our framework and show the formidable challenges of Env-QA in terms of long-term state tracking, multi-event temporal reasoning and event counting, etc.

关键词： Visualization Three-dimensional displays Layout Feature extraction Transformers Cognition Data mining

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Holistic Pose Graph: Modeling Geometric Structure among Objects in a Scene using Graph Inference for 3D Object Prediction

Holistic Pose Graph: Modeling Geometric Structure among Obje...

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International Conference on Computer Vision (ICCV)

作者： Jiwei Xiao Ruiping Wang Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9781665428132

Due to the missing depth cues, it is essentially ambiguous to detect 3D objects from a single RGB image. Existing methods predict the 3D pose for each object independently or merely by combining local relationships within limited surroundings, but rarely explore the inherent geometric relationships from a global perspective. To address this issue, we argue that modeling geometric structure among objects in a scene is very crucial, and thus elaborately devise the Holistic Pose Graph (HPG) that explicitly integrates all geometric poses including the object pose treated as nodes and the relative pose treated as edges. The inference of the HPG uses GRU to encode the pose features from their corresponding regions in a single RGB image, and passes messages along the graph structure iteratively to improve the predicted poses. To further enhance the correspondence between the object pose and the relative pose, we propose a novel consistency loss to explicitly measure the deviations between them. Finally, we apply Holistic Pose Estimation (HPE) to jointly evaluate both the independent object pose and the relative pose. Our experiments on the SUN RGB-D dataset demonstrate that the proposed method provides a significant improvement on 3D object prediction.

关键词： Solid modeling Computer vision Three-dimensional displays Image edge detection Pose estimation Layout Predictive models

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Local Feature Enhancement Network for Set-based Face Recognition

Local Feature Enhancement Network for Set-based Face Recogni...

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International Conference on Automatic Face and Gesture Recognition

作者： Ziyi Bai Ruiping Wang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing CAS China University of Chinese Academy of Sciences Beijing China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9781665431774

Set-based Face Recognition is widely applied in scenarios like law enforcement and online media data management. Compared with face recognition using a single image, the faces in the set often contain abundant appearance changes. Therefore, how to make full use of the rich information from the set and integrate them into a unified set representation become the key to set-based face recognition. Inspired by the fact that humans usually complete this fine-grained task through integrating the information from the congruent local regions (e.g. an eye to an eye) of multiple faces in a set, we propose a novel method called Local Feature Enhancement Network (LFENet), which can automatically enhance the local feature through transferring the local information across the images. Specifically, we retain the spatial semantic information of the feature maps and apply different relational functions to establish the correlation among the local features. The contained local information will be transferred to the relevant local features to enhance their discriminability. By doing so, the valuable local information carried in some local features can complement those with incomplete information. Besides, the various local information is aligned across faces under different conditions to help the model learn intra-set-compact face representations. Our method achieves state-of-the-art performances on two mainstream set-based face recognition benchmarks: IJB-A and IJB-C, which fully reflects the rationality and effectiveness of our local feature enhancement mechanism.

关键词： Correlation Law enforcement Face recognition Conferences Semantics Gesture recognition Media

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Principled design of translation, scale, and rotation invariant variation operators for metaheuristics

arXiv

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arXiv 2021年

作者： Tian, Ye Zhang, Xingyi He, Cheng Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Guangdong Provincial Key Laboratory of Braininspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science University of Surrey Guildford SurreyGU2 7XH United Kingdom

In the past three decades, a large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed, this paper aims to design new operators automatically, which are expected to be search space independent and thus exhibit robust performance on different problems. For this purpose, this work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. Then, we deduce the generic form of translation, scale, and rotation invariant operators. Afterwards, a principled approach is proposed for the automated design of operators, which searches for highperformance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-theart ones on a variety of problems with complex landscapes and up to 1000 decision variables. © 2021, CC BY.

关键词： Heuristic algorithms

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Topic Scene Graph Generation by Attention Distillation from Caption

Topic Scene Graph Generation by Attention Distillation from ...

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International Conference on Computer Vision (ICCV)

作者： Wenbin Wang Ruiping Wang Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9781665428132

If an image tells a story, the image caption is the briefest narrator. Generally, a scene graph prefers to be an omniscient "generalist", while the image caption is more willing to be a "specialist", which outlines the gist. Lots of previous studies have found that a scene graph is not as practical as expected unless it can reduce the trivial contents and noises. In this respect, the image caption is a good tutor. To this end, we let the scene graph borrow the ability from the image caption so that it can be a specialist on the basis of remaining all-around, resulting in the socalled Topic Scene Graph. What an image caption pays attention to is distilled and passed to the scene graph for estimating the importance of partial objects, relationships, and events. Specifically, during the caption generation, the attention about individual objects in each time step is collected, pooled, and assembled to obtain the attention about relationships, which serves as weak supervision for regularizing the estimated importance scores of relationships. In addition, as this attention distillation process provides an opportunity for combining the generation of image caption and scene graph together, we further transform the scene graph into linguistic form with rich and free-form expressions by sharing a single generation model with image caption. Experiments show that attention distillation brings significant improvements in mining important relationships without strong supervision, and the topic scene graph shows great potential in subsequent applications.

关键词： Computer vision Transforms Linguistics Guidelines

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PROM: A persistent routing optimization method based on supervised learning

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Journal of Network and Computer Applications 2025年 242卷

作者： Yingya Guo Zebo Huang Mingjie Ding Bin Lin Huan Luo College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China

Traditional Traffic Engineering (TE) typically improves network performance by formulating and solving routing optimization problems based on measured or predicted Traffic Matrices (TMs). However, since network traffic is dynamic, routing strategies optimized for the current measured TMs may not be suitable for future traffic patterns. To adapt to dynamic traffic, one possible solution is to perform more frequent routing updates, but this may lead to service disruptions. Another approach is the two-step method of prediction followed by optimization, which could lead to degraded performance of routing strategies due to inaccuracies in traffic prediction. Additionally, existing approaches focus solely on either distributed or centralized networks, lacking flexibility to adapt to different network architectures. To address these challenges, we propose PROM, a Machine Learning (ML)-based routing optimization model that directly predicts an effective routing strategy in an end-to-end manner based on historical traffic. This routing strategy is applicable across multiple future TMs, thereby reducing the need for frequent routing updates. Furthermore, PROM can be easily extended from a centralized implementation to a distributed one, adapting to different network architectures. To enhance model performance, we have also designed a custom loss function for routing optimization scenarios to avoid overfitting. Extensive simulation results demonstrate that PROM can predict high-quality routing strategies in future unknown traffic scenarios and is adaptable to both centralized and distributed network architectures.

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Critical Node Detection for Sequential Attacks in Complex Networks via Multi-objective Optimization

Critical Node Detection for Sequential Attacks in Complex Ne...

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International Conference on Industrial Artificial intelligence (IAI)

作者： Lei Zhang Qing Liu Jiajun Xia Haipeng Yang Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University China

The following topics are dealt with: learning (artificial intelligence); adaptive control; optimisation; control system synthesis; nonlinear control systems; neural nets; probability; pattern classification; feature e... 详细信息

ISBN: (数字)9781728182162

ISBN: (纸本)9781728182179

关键词： Statistics Sociology Complex networks Search problems Power system protection Power system faults Optimization

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Texture-Aware Network for Enhancing Inner Smoke Representation in Visual Smoke Density Estimation

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IET Computer Vision 2025年第1期19卷

作者： Xia, Xue Peng, Yajing Li, Zichen Shi, Jinting Fang, Yuming School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics Nanchang China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Nanchang China School of Vocational Education and Technology Jiangxi Agricultural University Nanchang China

Smoke often appears before visible flames in the early stages of fire disasters, making accurate pixel-wise detection essential for fire alarms. Although existing segmentation models effectively identify smoke pixels, they generally treat all pixels within a smoke region as having the same prior probability. This assumption of rigidity, common in natural object segmentation, fails to account for the inherent variability within smoke. We argue that pixels within smoke exhibit a probabilistic relationship with both smoke and background, necessitating density estimation to enhance the representation of internal structures within the smoke. To this end, we propose enhancements across the entire network. First, we improve the backbone by adaptively integrating scene information into texture features through separate paths, enabling smoke-tailored feature representation for further exploit. Second, we introduce a texture-aware head with long convolutional kernels to integrate both global and orientation-specific information, enhancing representation for intricate smoke structure. Third, we develop a dual-task decoder for simultaneous density and location recovery, with the frequency-domain alignment in the final stage to preserve internal smoke details. Extensive experiments on synthetic and real smoke datasets demonstrate the effectiveness of our approach. Specifically, comparisons with 17 models show the superiority of our method, with mean IoU improvements of 4.88%, 2.63%, and 3.17% on three test sets. (The code will be available on https://***/xia-xx-cv/TANet_smoke). © 2025 The Author(s). IET Computer Vision published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Image segmentation

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