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IEEE International Conference on Robotics and Automation (ICRA)

作者： Biqi Yang Xiaojie Gao Xianzhi Li Yun-Hui Liu Chi-Wing Fu Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology

Instance segmentation is a long-standing task for supporting robotic bin picking. However, objects of diverse classes can be closely packed with occlusions in cluttered and chaotic scenes, hence, even recent methods could have difficulty in locating clear and precise boundaries to distinguish nearby objects. In this work, we aim to improve the boundary quality of the instance masks for robust and precise instance segmentation in these challenging scenarios. Technical-wise, we first formulate an IoU-based Boundary-aware Mask head (IBM head) for predicting the instance-level mask, boundary, and their corresponding IoU scores. With this core module, we then follow the coarse-to-fine strategy and design our pipeline with two stages: an 1IoUNet to learn localization-based objectness cue and a hierarchical mask refiner to produce sharper and cleaner boundaries. We deploy the IBM head throughout the framework. Extensive experimental results on three grasping benchmarks manifest that our method attains the best instance segmentation performance, compared with the state-of-the-art approaches. Practically, we conduct real-world picking tests to show that with the objectness and boundary IoU scores as guidance, we are able to filter invalid (occluded) instances and select high-fidelity (exposed) instances for grasping.

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Implementation of a Hip Fracture Classification Network Based on CT Tomography Scans

Implementation of a Hip Fracture Classification Network Base...

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International Conference on computer and Communications (ICCC)

作者： Kui Zhao Meng Yang Ti Zhang Die Hu Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Science University of Chinese Academy of Science Shenyang China Department of Orthopedics Peking University Third Hospital Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Hip fractures is the most common hip clinical trauma. The majority of elderly patients experience a decrease in bone density due to osteoporosis, making them highly prone to various types of hip fractures. In this context, accurate diagnosis and classification of hip fractures are crucial for guiding clinical decision-making and ensuring the best possible patient outcomes. The BV C- N et proposed in this study is a two-stage model for the identification of hip fracture types, which is based on the integration of the V-Net and ConvNeXt architectures. First, deep learning segmentation model is trained to generate candidate fracture regions, and then, a deep learning classification model is trained in the second stage to classify the segmented local fracture regions according to the candidate fracture regions generated in the first stage to ascertain the type of fracture. The results show that the two-stage deep learning model proposed in this study enhances the accuracy of hip fracture type recognition and reduces the time required for manual classification, which can better assist doctors in fracture region recognition.

关键词： Deep learning Image segmentation Adaptation models Osteoporosis Accuracy Computed tomography Transfer learning Predictive models Older adults Hip

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Query Rewriting for Retrieval-Augmented Large Language Models

arXiv

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arXiv 2023年

作者： Ma, Xinbei Gong, Yeyun He, Pengcheng Zhao, Hai Duan, Nan 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 Microsoft Research Asia China Microsoft Azure AI

Large Language Models (LLMs) play powerful, black-box readers in the retrieve-then-read pipeline, making remarkable progress in knowledge-intensive tasks. This work introduces a new framework, Rewrite-Retrieve-Read instead of the previous retrieve-then-read for the retrieval-augmented LLMs from the perspective of the query rewriting. Unlike prior studies focusing on adapting either the retriever or the reader, our approach pays attention to the adaptation of the search query itself, for there is inevitably a gap between the input text and the needed knowledge in retrieval. We first prompt an LLM to generate the query, then use a web search engine to retrieve contexts. Furthermore, to better align the query to the frozen modules, we propose a trainable scheme for our pipeline. A small language model is adopted as a trainable rewriter to cater to the black-box LLM reader. The rewriter is trained using the feedback of the LLM reader by reinforcement learning. Evaluation is conducted on downstream tasks, open-domain QA and multiple-choice QA. Experiments results show consistent performance improvement, indicating that our framework is proven effective and scalable, and brings a new framework for retrieval-augmented LLM. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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Towards expert gaze modeling and recognition of a user's attention in realtime 24

Towards expert gaze modeling and recognition of a user's att...

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Castner, Nora Geßler, Lea Geisler, David Hüttig, Fabian Kasneci, Enkelejda Human-Computer Interaction University of Tübingen Germany Institute of Computer Science University of Tübingen Germany Department of Prosthodontics University Hospital Tübingen Germany

One of the appealing areas of expertise research is devoted to measuring the effectiveness of training programs for novices. With recent progress in eye tracking, gaze-based interaction systems recognize a user's attention and can direct it accordingly. Moreover, dynamic visualization of an expert gaze model facilitates novice training by guiding the gaze to relevant areas. In addition, the system should be aware of realtime attention to remove an overlay that could occlude relevant information. We use an implementation of subtle gaze direction (SGD) and the simplified scanpath of a dentist to train naive participants in finding anomalies in dental radiographs. We were able to effectively direct user gaze to relevant image features without occluding the area when attention was recognized. Additionally, participants reported that the intervention was helpful for image inspection. The results of the model intervention show minimal improvements in anomaly detection, which is expected of naive subjects. We advocate that the system has the potential to be highly effective for advanced students and trainees with a certain foundation of conceptual knowledge. © 2020 The Authors. Published by Elsevier B.V.

关键词： Eye tracking

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Saliency Region Detection in Complex Scenes Based on Multi-scale Cascaded Attention

Saliency Region Detection in Complex Scenes Based on Multi-s...

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IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Yifeng Wang Zhenfeng Huo Aoli Liu Lin Zhao Di Wang The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shanxi Province Xidian University China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781665441506

This paper proposes a saliency detection method based on multi-scale cascade attention mechanism. It utilizes both channel and spatial weight attention mechanism to effectively learn the salient regions. By generating multi-scale intermediate feature maps, the shallow features are divided into categories of foreground and background. Then, the channel weights are calculated by using the foreground and background feature distribution, and the spatial weights are computed by using the predicted feature map, so that the network is more focused on salient regions and suppresses the interference of background regions. Experimental results show that the model can reliably and accurately detect salient targets and delivers better performance.

关键词： Training Cloud computing Conferences Computational modeling Interference Reliability Convolutional neural networks

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

arXiv

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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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Arbitrary Motion Style Transfer with Multi-Condition Motion Latent Diffusion Model

Arbitrary Motion Style Transfer with Multi-Condition Motion ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Wenfeng Song Xingliang Jin Shuai Li Chenglizhao Chen Aimin Hao Xia Hou Ning Li Hong Qin Beijing Information Science and Technology University China Zhongguancun Laboratory China State Key Laboratory of Virtual Reality Technology and Systems Beihang University College of Computer Science and Technology China University of Petroleum (East China) Research Unit of Virtual Human and Virtual Surgery (2019RU004) Chinese Academy of Medical Sciences Department of Computer Science Stony Brook University (SUNY at Stony Brook) Stony Brook New York USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

computer animation's quest to bridge content and style has historically been a challenging venture, with previous efforts often leaning toward one at the expense of the other. This paper tackles the inherent challenge of content-style duality, ensuring a harmonious fusion where the core narrative of the content is both preserved and elevated through stylistic enhancements. We propose a novel Multi-condition Motion Latent Diffusion Model (MCM-LDM) for Arbitrary Motion Style Transfer (AMST). Our MCM-LDM significantly emphasizes preserving trajectories, recognizing their fundamental role in defining the essence and fluidity of motion content. Our MCM-LDM's cornerstone lies in its ability first to disentangle and then intricately weave together motion's tripartite components: motion trajectory, motion content, and motion style. The critical insight of MCM-LDM is to embed multiple conditions with distinct priorities. The content channel serves as the primary flow, guiding the overall structure and movement, while the trajectory and style channels act as auxiliary components and synchronize with the primary one dynamically. This mechanism ensures that multi-conditions can seamlessly integrate into the main flow, enhancing the overall animation without overshadowing the core content. Empirical evaluations underscore the model's proficiency in achieving fluid and authentic motion style transfers, setting a new benchmark in the realm of computer animation. The source code and model are available at https://***/XingliangJin/***.

关键词： Bridges computer vision Fluids Computational modeling Source coding Animation Diffusion models

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A Novel Lightweight Convolutional Neural Network for EEG-Based Miniature-Event-Related Potentials

A Novel Lightweight Convolutional Neural Network for EEG-Bas...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Chang Liu Wenxiao Zhong Yang Di Liang Zhao Xingwei An Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin P. R. China Tianjin Key Laboratory of Brain Science and Neuroengineering academy of medical engineering and translation of medicine Tianjin University Tianjin Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration State Key Laboratory of Advanced Medical Materials and Devices Tianjin University Tianjin Tianjin P. R. China

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

Miniature asymmetric visual evoked potential (aVEP) induced by very small lateral visual stimuli is a comfortable stimulation paradigm. It could only induce a miniature potential about 0.5 μV in amplitude, which could be challenging to detect. Most decoding methods based on miniature aVEP are machine learning approaches, and deep learning methods have not yet been introduced. Therefore, this paper introduces deep learning techniques for the first time and designs a lightweight convolutional neural network. Specifically, depthwise convolution is employed to reduce the number of network parameters and extract temporal and spatial features from EEG signals. The model achieves an average classification accuracy of 78.48% on the miniature aVEP dataset, which is 1.14% higher than the previous state-of-the-art model, DCPM. Through comparison with other deep learning models in terms of parameter count, computational complexity, and classification accuracy, it is evident that the proposed model surpasses them in all these aspects The results suggest that the CNN model proposed in this study achieves high classification accuracy while demanding fewer computational resources. Although the network model we proposed is very simple, it can achieve good results in processing miniature aVEP. In future research, we will continue to improve this lightweight network to enhance its decoding performance for miniature aVEP.

关键词： Deep learning Visualization Accuracy Computational modeling Neural networks Virtual environments Brain modeling

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A Generalized Outage Prediction Model for Various Types of Extreme Climate Events in Texas

A Generalized Outage Prediction Model for Various Types of E...

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IEEE International Conference on Big Data

作者： Jangjae Lee Sangkeun Matthew Lee Stephanie German Paal Supriya Chinthavali Zachry Department of Civil and Environmental Engineering Texas A&M University College Station TX USA Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA Geospatial Science and Human Security Division Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This study proposes a generalized model for predicting power outages for various types of extreme weather events. To accomplish the objective of this research, diverse features (e.g., weather data, geographical features, socio-demographic data, and infrastructure information) were leveraged as independent features, while the target variable was the number of customers who had problems with their electricity at the county level. Using the time of occurrence of extreme weather as defined by the National Weather Service, the top ten influential events were selected using the impact index based on cumulative power outages due to each type of extreme weather event. Additionally, a generalized model was created to predict power outages using weather data from one hour before the outage, outage data from one hour prior, as well as socio-demographic, geographic, and infrastructure information and this model was evaluated. The model was developed in two ways: first, as an Ensemble model trained using individual extreme weather events, and second, as a Unified model using all types of extreme weather conditions. As a result of evaluating the model using mean directional accuracy (MDA), one of the evaluation metrics, the ensemble model showed an accuracy of over 0.4 for weather event types such as Winter Storms, Cold/Wind Chill, Frost/Freeze, and Ice Storms. Although this study focused on creating a model specific to Texas, it is possible to expand the data to develop a nationwide model.

关键词： Measurement Accuracy Storms Electricity Predictive models Data models Ice Power system reliability Indexes Optimization

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Event-Guided Attention Network for Low Light Image Enhancement

Event-Guided Attention Network for Low Light Image Enhanceme...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qiaobin Wang Haiyan Jin Haonan Su Zhaolin Xiao Department of Computer Science and Engineering Engineering Research Center Shaanxi Key Laboratory for Network Computing and Security Technology Human-Machine Integration Intelligent Robot Shaanxi University Xi'an University of Technology

In the low-light conditions, images are corrupted by low contrast and severe noise, but event cameras can capture event streams with clear edge structures. Therefore, we propose an Event-Guided Attention Network for Low-light Image Enhancement (EGAN) using a dual branch Network and recover clear structure with the guide of events. To overcome the lack of paired training datasets, we first synthesize the dataset containing low-light event streams, low-light images, and the ground truth (GT) normal-light images. Then, we develop an end-to-end dual branch network consisting of a Image Enhancement Branch (IEB) and a Gradient Reconstruction Branch (GRB). The GRB branch reconstructs image gradients using events, and the IEB enhances low-light images using reconstructed gradients. Moreover, we develops the Attention based Event-Image Feature Fusion Module (AEIFFM) which selectively fuses the event and low-light image features using the spatial and channel attention mechanism, and the fused features are concatenated into the IEB and GRB, which respectively generate the enhanced images with clear structure and more accurate gradient images. Extensive experiments on synthetic and real datasets demonstrate that the proposed EGAN produces visually more appealing enhancement images, and achieves a good performance in structure preservation and denoising over state-of-the-arts.

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