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2nd ACM International Conference on Multimedia in Asia, MMAsia 2020

作者： Cui, Zhe Su, Li Zhang, Weigang Huang, Qingming University of Chinese Academy of Sciences China Harbin Institute of Technology Weihai China University of Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781450383080

Convolutional neural network (CNN) based salient object detection (SOD) has achieved great development in recent years. However, in some challenging cases, i.e. small-scale salient object, low contrast salient object and cluttered background, existing salient object detect methods are still not satisfying. In order to accurately detect salient objects, SOD networks need to fix the position of most salient part. Fixation prediction (FP) focuses on the most visual attractive regions, so we think it could assist in locating salient objects. As far as we know, there are few methods jointly consider SOD and FP tasks. In this paper, we propose a fixation guided salient object detection network (FGNet) to leverage the correlation between SOD and FP. FGNet consists of two branches to deal with fixation prediction and salient object detection respectively. Further, an effective feature cooperation module (FCM) is proposed to fuse complementary information between the two branches. Extensive experiments on four popular datasets and comparisons with twelve state-of-the-art methods show that the proposed FGNet well captures the main context of images and locates salient objects more accurately. © 2021 ACM.

关键词： Computer vision

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EFSA: Towards Event-Level Financial Sentiment Analysis

arXiv

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arXiv 2024年

作者： Chen, Tianyu Zhang, Yiming Yu, Guoxin Zhang, Dapeng Zeng, Li He, Qing Ao, Xiang Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China School of IoT Engineering Jiangsu Vocational College of Information Technology Wuxi China Information Technology Department I Shenzhen Stock Exchange China

In this paper, we extend financial sentiment analysis (FSA) to event-level since events usually serve as the subject of the sentiment in financial text. Though extracting events from the financial text may be conducive to accurate sentiment predictions, it has specialized challenges due to the lengthy and discontinuity of events in a financial text. To this end, we reconceptualize the event extraction as a classification task by designing a categorization comprising coarse-grained and finegrained event categories. Under this setting, we formulate the Event-Level Financial Sentiment Analysis (EFSA for short) task that outputs quintuples consisting of (company, industry, coarse-grained event, fine-grained event, sentiment) from financial text. A large-scale Chinese dataset containing 12, 160 news articles and 13, 725 quintuples is publicized as a brand new testbed for our task. A four-hop Chainof- Thought LLM-based approach is devised for this task. Systematically investigations are conducted on our dataset, and the empirical results demonstrate the benchmarking scores of existing methods and our proposed method can reach the current state-of-the-art. Our dataset and framework implementation are availab.e at https://***/cty1934/EFSA. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Combination of graphics, uncertainty, and semantics: A survey

Combination of graphics, uncertainty, and semantics: A surve...

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作者： Gao, Yuan Rafi, Muhammad Adnan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China Department of Computer Science Aston University Birmingham United Kingdom

Graphics, uncertainty, and semantics are three approaches to building models. The combination of the three approaches is a way to develop a stronger modeling method. This article surveys the research efforts toward combining these aspects, which can be divided into two routes: One is to combine graphics and uncertainty as probabilistic graphical models and then incorporate semantics, and the other is to combine graphics and semantics and then incorporate probability to handle uncertainty. The models and methods involved in these efforts are introduced and their expressiveness, pros, and cons are discussed. © 2021 John Wiley & Sons Ltd.

关键词： Semantics

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Accurate Interpolation of Scattered Data Via Learning Relation Graph

Accurate Interpolation of Scattered Data Via Learning Relati...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shizhe Ding Boyang Xia Jingyan Sui Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Central China Institute for Artificial Intelligence Technologies Zhengzhou China

Interpolation of scattered data is crucial across various domains, and neural networks have proved effective in developing accurate interpolators. While these neural network-based approaches excel in capturing data distributions, their failure to leverage inherent locality in computations can lead to overly dense correlation modeling. This might result in capturing spurious correlations and thereby affecting accuracy. To address these shortcomings, we propose a relation-aware interpolation framework named REIN. REIN uses a relational inference module to efficiently identify neighboring observed data points for each interpolation location, and integrate the relation graph as constraints into a neural interpolator. Experimental results on both synthetic and real-world datasets show that REIN outperforms the existing interpolation methods, even those employing heuristic local constraints. The analysis also suggests that compared with the widely-used heuristic local constraints, the learned local relation graphs exhibit improved adaptability and interpretability.

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TaMMa: Target-driven Multi-subscene Mobile Manipulation 8

TaMMa: Target-driven Multi-subscene Mobile Manipulation

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8th Conference on Robot Learning, CoRL 2024

作者： Hou, Jiawei Wang, Tianyu Pan, Tongying Wang, Shouyan Xue, Xiangyang Fu, Yanwei School of Computer Science Fudan University China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China School of Data Science Fudan University China Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence Shanghai Key Lab of Intelligent Information Processing Technology Innovation Center of Calligraphy and Painting Digital Generation Ministry of Culture and Tourism China

For everyday service robotics, the ability to navigate back and forth based on tasks in multi-subscene environments and perform delicate manipulations is crucial and highly practical. While existing robotics primarily focus on complex tasks within a single scene or simple tasks across scalab.e scenes individually, robots consisting of a mobile base with a robotic arm face the challenge of efficiently representing multiple subscenes, coordinating the collab.ration between the mobile base and the robotic arm, and managing delicate tasks in scalab.e environments. To address this issue, we propose Target-driven Multi-subscene Mobile Manipulation (TaMMa), which efficiently handles mobile base movement and fine-grained manipulation across subscenes. Specifically, we obtain a reliable 3D Gaussian initialization of the whole scene using a sparse 3D point cloud with encoded semantics. Through querying the coarse Gaussians, we acquire the approximate pose of the target, navigate the mobile base to approach it, and reduce the scope of precise target pose estimation to the corresponding subscene. Optimizing while moving, we employ diffusion-based depth completion to optimize fine-grained Gaussians and estimate the target's refined pose. For target-driven manipulation, we adopt Gaussians inpainting to obtain precise poses for the origin and destination of the operation in an imagine before you do it manner, enabling fine-grained manipulation. We conduct various experiments on a real robotic to demonstrate our method in effectively and efficiently achieving precise operation tasks across multiple tabletop subscenes. © 2024 Proceedings of Machine Learning Research.

关键词： Gaussian distribution

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DASC-SPT: Towards Self-Supervised Panoramic Semantic Segmentation

DASC-SPT: Towards Self-Supervised Panoramic Semantic Segment...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Tianlong Tan Bin Chen Hongliang Cao Chenggang Yan Yike Ma Feng Dai Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing China Shandong University University of Chinese Academy of Sciences Beijing China Hangzhou Dianzi University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Self-Supervised Semantic Segmentation, aiming to leverage masses of unlab.led data for boosting semantic segmentation, has been rapidly emerging as an active task in recent years. However, existing self-supervised semantic segmentation approaches mainly focus on planar images, leaving multiple distorted objects encountered in panoramic images unexplored due to the formidable challenge of handling heterogeneous degrees of distortions across different locations. In this paper, we propose a novel Self-Supervised Panoramic Semantic Segmentation model, termed DASC-SPT, built upon the mainstream contrastive learning framework. Towards distortions in panoramic images, we present two structures to better learn from distorted features by applying planar images. For the input images of self-supervision, we design a Spherical Projection Transformation (SPT) strategy that involves randomly projecting planar images onto various locations of the sphere to introduce the distortions. For pixel-wise distorted features, we construct a Deformation-aware Sampling Consistency (DASC) framework to further utilize the shared content and discrepancies caused by different distortions of paired views, where the deformation-aware consistency can be quantified on pixel-wise features. Both of the two components facilitate the model to adapt to distortions and boost panoramic semantic segmentation. Extensive comprehensive experiments on three panoramic datasets demonstrate the effectiveness and superiority of DASC-SPT approach.

关键词： Training Deformable models Adaptation models Computer vision Semantic segmentation Computational modeling Crops Contrastive learning Distortion Boosting

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R-calculus for post three-valued description logic 7

R-calculus for post three-valued description logic

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7th International Conference on Fuzzy Systems and Data Mining, FSDM 2021

作者： Cao, Cungen Hu, Lanxi Sui, Yuefei Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China

ISBN: (纸本)9781643682143

R-calculus is a belief revision operator satisfying AGM postulates, and belief revision in ontology engineering is ontology revision, which based logic is description logics. In Post three-valued description logic, a tableau proof system Tt will be given such that Tt is sound and complete for t-satisfiability, and nonmonotonic, that is, a theory Δis t-satisfiable if and only if Δis deducible in Tt. Based on the tableau proof system, an R-calculus Rt will be given such that a configuration Î"|C(a) is reducible to C(a),Δif and only if C(a) is t-satisfiable with Î", if and only if reduction Î"|C(a) â‡' C(a),Δis deducible in Rt. © 2021 The authors and IOS Press.

关键词： Ontology

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Two Heads Are Better Than One: Improving Fake News Video Detection by Correlating with Neighbors

arXiv

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arXiv 2023年

作者： Qi, Peng Zhao, Yuyang Shen, Yufeng Ji, Wei Cao, Juan Chua, Tat-Seng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China National University of Singapore Singapore

The prevalence of short video platforms has spawned a lot of fake news videos, which have stronger propagation ability than textual fake news. Thus, automatically detecting fake news videos has been an important countermeasure in practice. Previous works commonly verify each news video individually with multimodal information. Nevertheless, news videos from different perspectives regarding the same event are commonly posted together, which contain complementary or contradictory information and thus can be used to evaluate each other mutually. To this end, we introduce a new and practical paradigm, i.e., cross-sample fake news video detection, and propose a novel framework, Neighbor-Enhanced fakE news video Detection (NEED), which integrates the neighborhood relationship of new videos belonging to the same event. NEED can be readily combined with existing single-sample detectors and further enhance their performances with the proposed graph aggregation (GA) and debunking rectification (DR) modules. Specifically, given the feature representations obtained from single-sample detectors, GA aggregates the neighborhood information with the dynamic graph to enrich the features of independent samples. After that, DR explicitly leverages the relationship between debunking videos and fake news videos to refute the candidate videos via textual and visual consistency. Extensive experiments on the public benchmark demonstrate that NEED greatly improves the performance of both single-modal (up to 8.34% in accuracy) and multimodal (up to 4.97% in accuracy) base detectors. Codes are availab.e in https://***/ICTMCG/NEED. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Glance and focus: memory prompting for multi-event video question answering 23

Glance and focus: memory prompting for multi-event video que...

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Proceedings of the 37th International Conference on Neural information processing Systems

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

Video Question Answering (VideoQA) has emerged as a vital tool to evaluate agents' ability to understand human daily behaviors. Despite the recent success of large vision language models in many multi-modal tasks, complex situation reasoning over videos involving multiple human-object interaction events still remains challenging. In contrast, humans can easily tackle it by using a series of episode memories as anchors to quickly locate question-related key moments for reasoning. To mimic this effective reasoning strategy, we propose the Glance-Focus model. One simple way is to apply an action detection model to predict a set of actions as key memories. However, these actions within a closed set vocabulary are hard to generalize to various video domains. Instead of that, we train an Encoder-Decoder to generate a set of dynamic event memories at the glancing stage. Apart from using supervised bipartite matching to obtain the event memories, we further design an unsupervised memory generation method to get rid of dependence on event annotations. Next, at the focusing stage, these event memories act as a bridge to establish the correlation between the questions with high-level event concepts and low-level lengthy video content. Given the question, the model first focuses on the generated key event memory, then focuses on the most relevant moment for reasoning through our designed multi-level cross-attention mechanism. We conduct extensive experiments on four Multi-Event VideoQA benchmarks including STAR, EgoTaskQA, AGQA, and NExT-QA. Our proposed model achieves state-of-the-art results, surpassing current large models in various challenging reasoning tasks. The code and models are availab.e at https://***/ByZ0e/Glance-Focus.

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Gaussian lab.l Distribution Learning for Spherical Image Object Detection

Gaussian Label Distribution Learning for Spherical Image Obj...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hang Xu Xinyuan Liu Qiang Zhao Yike Ma Chenggang Yan Feng Dai Hangzhou Dianzi University Hangzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing China

Spherical image object detection emerges in many applications from virtual reality to robotics and automatic driving, while many existing detectors use $l_{n}$ -norms loss for regression of spherical bounding boxes. There are two intrinsic flaws for $l_{n}$ -norms loss, i.e., independent optimization of parameters and inconsistency between metric (dominated by IoU) and loss. These problems are common in planar image detection but more significant in spherical image detection. Solution for these problems has been extensively discussed in planar image detection by using IoU loss and related variants. However, these solutions cannot be migrated to spherical image object detection due to the undifferentiable of the Spherical IoU (SphIoU). In this paper, we design a simple but effective regression loss based on Gaussian lab.l Distribution Learning (GLDL) for spherical image object detection. Besides, we observe that the scale of the object in a spherical image varies greatly. The huge differences among objects from different categories make the sample selection strategy based on SphIoU challenging. Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance. Extensive results on various two datasets with different baseline detectors show the effectiveness of our approach.

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