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Pre-training Multi-party Dialogue Models with Latent Discourse Inference

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arXiv 2023年

作者： Li, Yiyang Huang, Xinting Bi, Wei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China NLP Center Tencent AI-Lab Hong Kong

Multi-party dialogues are more difficult for models to understand than one-to-one two-party dialogues, since they involve multiple interlocutors, resulting in interweaving reply-to relations and information flows. To step over these obstacles, an effective way is to pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. However, due to the lack of explicitly annotated discourse labels in multi-party dialogue corpora, previous works fail to scale up the pre-training process by putting aside the unlabeled multi-party conversational data for nothing. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model by unsupervised latent variable inference methods. Experiments on multiple downstream tasks show that our pre-trained model outperforms strong baselines by large margins and achieves state-of-the-art (SOTA) results, justifying the effectiveness of our method. The official implementation of this paper is available at https://***/EricLee8/MPD_EMVI. © 2023, CC BY.

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Multi-level Gate Feature Aggregation with Spatially Adaptive Batch-Instance Normalization for Semantic Image Synthesis 27th

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Multi-level Gate Feature Aggregation with Spatially Adaptive...

27th International Conference on MultiMedia Modeling, MMM 2021

作者： Long, Jia Lu, Hongtao Key Lab of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783030678319

In this paper, we focus on the task of generating realistic images given an input semantic layout, which is also called semantic image synthesis. Most of previous methods are based on conditional generative adversarial networks mechanism, which is stacks of convolution, normalization, and non-linearity layers. However, these methods easily generate blurred regions and distorted structures. There are two limits existing: their normalization layers are unable to make a good balance between keeping semantic layout information and geometric changes;and cannot effectively aggregated multi-level feature. To address the above problems, we propose a novel method which incorporates multi-level gate feature aggregation mechanism (GFA) and spatially adaptive batch-instance normalization (SPAda-BIN) for semantic image synthesis. Experiments on several challenging datasets demonstrate the advantage of the proposed method over existing approaches, in terms of both visual fidelity and quantitative metrics. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

Spatial Gradient Guided Learning and Semantic Relation Transfer for Facial Landmark Detection 27th

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Spatial Gradient Guided Learning and Semantic Relation Trans...

27th International Conference on MultiMedia Modeling, MMM 2021

作者： Wang, Jian Li, Yaoyi Lu, Hongtao Key Lab of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783030678319

Pixel-wise losses are widely used in heatmap regression networks to detect facial landmarks, however, those losses are not consistent with the evaluation criteria in testing, which is evaluating the error between the highest pixel position in the predicted heatmap and the ground-truth heatmap. In this paper, we proposed a novel spatial-gradient consistency loss function (called Grad loss), which maintains a similar spatial structure in the heatmap with ground-truth. To reduce the quantization error caused by downsampling in the network, we also propose a new post-processing strategy based on the Gaussian prior. To further improve face alignment accuracy, we introduce Spatial-Gradient Enhance attention and Relation-based Reweighing Module to transfer semantic information and spatial information between high-resolution and low-resolution representations. Extensive experiments on several benchmarks (e.g., 300W, AFLW, COFW, WFLW) show that our method outperforms the state-of-the-art by impressive margins. © 2021, Springer Nature Switzerland AG.

关键词： Face recognition

MCCNet: Multi-Color Cascade Network with Weight Transfer for Single Image Depth Prediction on Outdoor Relief Images 25th

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MCCNet: Multi-Color Cascade Network with Weight Transfer for...

25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Frisky, Aufaclav Zatu Kusuma Putranto, Andi Zambanini, Sebastian Sablatnig, Robert Computer Vision Lab Institute of Visual Computing and Human-Centered Technology Faculty of Informatics TU Wien Vienna Austria Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Department of Archaeology Faculty of Cultural Science Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9783030687861

Single image depth prediction is considerably difficult since depth cannot be estimated from pixel correspondences. Thus, prior knowledge, such as registered pixel and depth information from the user is required. Another problem rises when targeting a specific domain requirement as the number of freely available training datasets is limited. Due to color problem in relief images, we present a new outdoor Registered Relief Depth (RRD) Prambanan dataset, consisting of outdoor images of Prambanan temple relief with registered depth information supervised by archaeologists and computer scientists. In order to solve the problem, we also propose a new depth predictor, called Multi-Color Cascade Network (MCCNet), with weight transfer. Applied on the new RRD Prambanan dataset, our method performs better in different materials than the baseline with 2.53 mm RMSE. In the NYU Depth V2 dataset, our method’s performance is better than the baselines and in line with other state-of-the-art works. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

Collaborative Positional-Motion Excitation Module for Efficient Action Recognition 18th

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Collaborative Positional-Motion Excitation Module for Effici...

18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Alsarhan, Tamam Lu, Hongtao Key Lab of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China MOE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783030893699

Massive progress for vision-based action recognition has been made in the last few years, owing to the advancement of deep convolutional neural networks (CNNs). In contrast with 2D CNN-based approaches, 3D CNN-based approaches can effectively capture spatial and temporal features. However, they are computationally intensive. To boost 2D-CNN performance, most of the existing methods leverage channel attention (e.g. squeeze and excitation), which despite its strong impact on the model performance, operates only on the channel space and ignores the spatial space. In this work, we design a generic and collaborative excitation module, namely the Collaborative Positional-Motion Excitation Module (CPME) for action recognition. CPME is a dual-pathway excitation module designed to embed the crucial types of information, mainly the positional information and the motion information, for efficient action recognition. Positional Enhancement Pathway (PEP), the first pathway of CPME, considers encoding direction-aware and position-sensitive information. Motion Enhancement Pathway (MEP), the second pathway, encodes the motion information by emphasizing the informative features in each frame and excite motion-sensitive channels. We integrate the proposed CPME into 2D CNNs to form a simple yet effective CPME-Net with limited extra computational cost. Finally, a discriminative and diverse video-level representation for action recognition is generated by end-to-end training. Experiments on two popular action recognition datasets demonstrate that CPME blocks bring performance improvements on 2D CNN baseline, and our method achieves competitive results against the state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： Signal encoding

Toward Anxiety-Reducing Pocket Robots for Children

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arXiv 2025年

作者： Frederiksen, Morten Roed Stoy, Kasper Matarić, Maja Computer science department of The IT-University of Copenhagen Rued Langgaards vej 7 Copenhagen2300 Denmark Interaction Lab in the Computer Science Department The University of Southern California United States

A common denominator for most therapy treatments for children who suffer from an anxiety disorder is daily practice routines to learn techniques needed to overcome anxiety. However, applying those techniques while experiencing anxiety can be highly challenging. This paper presents the design, implementation, and pilot study of a tactile hand-held pocket robot "AffectaPocket", designed to work alongside therapy as a focus object to facilitate coping during an anxiety attack. The robot does not require daily practice to be used, has a small form factor, and has been designed for children 7 to 12 years old. The pocket robot works by sensing when it is being held and attempts to shift the child’s focus by presenting them with a simple three-note rhythm-matching game. We conducted a pilot study of the pocket robot involving four children aged 7 to 10 years, and then a main study with 18 children aged 6 to 8 years;neither study involved children with anxiety. Both studies aimed to assess the reliability of the robot’s sensor configuration, its design, and the effectiveness of the user tutorial. The results indicate that the morphology and sensor setup performed adequately and the tutorial process enabled the children to use the robot with little practice. This work demonstrates that the presented pocket robot could represent a step toward developing low-cost accessible technologies to help children suffering from anxiety disorders. © 2025, CC BY-NC-ND.

关键词： Machine design

Incremental Feature Selection for Dynamic Incomplete Data Using Sub-Tolerance Relations

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SSRN

SSRN 2023年

作者： Zhao, Jie Ling, Yun See-To, Wing-Kuen Eric Huang, Faliang Wang, Jiahai School of Management Guangdong University of Technology Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

Feature selection is an important problem in Rough Set and is widely used in data analysis. The extended theory of tolerance relations can effectively deal with the realistic problem of incomplete data. However, the space and time complexity of computing the positive region using TRS increases dramatically. Simultaneously, redundant features are allowed to enter the feature subset, resulting in poor classification accuracy, because the positive region-based significance measure neglects uncertain information. To overcome these drawbacks, this paper introduces a subrelation-based tolerance class (STC) approach. STC transforms the TRS positive region calculation into a two-stage certainty measurement of objects, and thus divides the entire universe into two regions of certainty and uncertainty at each stage, by decomposing the tolerance relation into two subrelations. The computational space is reduced by progressively filtering out of the regions of certainty. Using STC, a new incremental mechanism is proposed. Furthermore, we propose a new significance measure using both certain and uncertain information, which effectively reduces the number of redundant features and subsequently improves computational efficiency and classification accuracy. Experiments on real data sets and comparisons with existing algorithms have been conducted to verify the superiority of the method. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization

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arXiv 2025年

作者： Wu, Weiqi Huang, Shen Jiang, Yong Xie, Pengjun Huang, Fei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongyi Lab Alibaba Group China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

In the fast-changing realm of information, the capacity to construct coherent timelines from extensive event-related content has become increasingly significant and challenging. The complexity arises in aggregating related documents to build a meaningful event graph around a central topic. This paper proposes CHRONOS - Causal Headline Retrieval for Open-domain News Timeline SummarizatiOn via Iterative Self-Questioning, which offers a fresh perspective on the integration of Large Language Models (LLMs) to tackle the task of Timeline Summarization (TLS). By iteratively reflecting on how events are linked and posing new questions regarding a specific news topic to gather information online or from an offline knowledge base, LLMs produce and refresh chronological summaries based on documents retrieved in each round. Furthermore, we curate Open-TLS, a novel dataset of timelines on recent news topics authored by professional journalists to evaluate open-domain TLS where information overload makes it impossible to find comprehensive relevant documents from the web. Our experiments indicate that CHRONOS is not only adept at open-domain timeline summarization, but it also rivals the performance of existing state-of-the-art systems designed for closed-domain applications, where a related news corpus is provided for summarization. Copyright © 2025, The Authors. All rights reserved.

关键词： Modeling languages

Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy

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arXiv 2023年

作者： Marinov, Zdravko Jäger, Paul F. Egger, Jan Kleesiek, Jens Stiefelhagen, Rainer The Computer Vision for Human-Computer Interaction Lab Department of Informatics Karlsruhe Institute of Technology Adenauerring 10 Karlsruhe76131 Germany Girardetstraße 2 Essen45131 Germany Heidelberg Interactive Machine Learning Group Im Neuenheimer Feld 223 Heidelberg69120 Germany The Helmholtz Imaging DKFZ Im Neuenheimer Feld 223 Heidelberg69120 Germany

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks. Appendixes are here. © 2023, CC BY.

关键词： Medical imaging