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Preference detection of the humanoid robot face based on EEG and eye movement

Neural Computing and Applications 2024年第19期36卷 11603-11621页

作者： Wang, Pengchao Mu, Wei Zhan, Gege Wang, aiping Song, Zuoting Fang, Tao Zhang, Xueze Wang, Junkongshuai Niu, Lan Bin, Jianxiong Zhang, Lihua Jia, Jie Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI and Robotics Ministry of Education Shanghai Engineering Research Center of AI and Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Ji Hua Laboratory Guangdong Province Foshan528200 China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai200040 China Yiwu Research Institute of Fudan University Zhejiang Yiwu322000 China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou311121 China

The face of a humanoid robot can affect the user experience, and the detection of face preference is particularly important. Preference detection belongs to a branch of emotion recognition that has received much attention from researchers. Most of the previous preference detection studies have been conducted based on a single modality. In this paper, we detect face preferences of humanoid robots based on electroencephalogram (EEG) signals and eye movement signals for single modality, canonical correlation analysis fusion modality, and bimodal deep autoencoder (BDAE) fusion modality, respectively. We validated the theory of frontal asymmetry by analyzing the preference patterns of EEG and found that participants had higher alpha wave energy for preference faces. In addition, hidden preferences extracted by EEG signals were better classified than preferences from participants' subjective feedback, and also, the classification performance of eye movement data was improved. Finally, experimental results showed that BDAE multimodal fusion using frontal alpha and beta power spectral densities and eye movement information as features performed best, with the highest average accuracy of 83.13% for the SVM and 71.09% for the KNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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HubRouter: learning global routing via hub generation and pin-hub connection 23

HubRouter: learning global routing via hub generation and pi...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xingbo Du Chonghua Wang Ruizhe Zhong Junchi Yan Dept. of Computer Science and Engineering & MoE Key Lab of AI Shanghai Jiao Tong University

Global Routing (GR) is a core yet time-consuming task in VLSI systems. It recently attracted efforts from the machine learning community, especially generative models, but they suffer from the non-connectivity of generated routes. We argue that the inherent non-connectivity can harm the advantage of its one-shot generation and has to be post-processed by traditional approaches. Thus, we propose a novel definition, called hub, which represents the key point in the route. Equipped with hubs, global routing is transferred from a pin-pin connection problem to a hub-pin connection problem. Specifically, to generate definitely-connected routes, this paper proposes a two-phase learning scheme named HubRouter, which includes 1) hub-generation phase: A condition-guided hub generator using deep generative models; 2) pin-hub-connection phase: An RSMT construction module that connects the hubs and pins using an actor-critic model. In the first phase, we incorporate typical generative models into a multi-task learning framework to perform hub generation and address the impact of sensitive noise points with stripe mask learning. During the second phase, HubRouter employs an actor-critic model to finish the routing, which is efficient and has very slight errors. Experiments on simulated and real-world global routing benchmarks are performed to show our approach's efficiency, particularly HubRouter outperforms the state-of-the-art generative global routing methods in wirelength, overflow, and running time. Moreover, HubRouter also shows strength in other applications, such as RSMT construction and interactive path replanning.

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T2T: from distribution learning in training to gradient search in testing for combinatorial optimization 23

T2T: from distribution learning in training to gradient sear...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yang Li Jinpei Guo Runzhong Wang Junchi Yan Dept. of Computer Science and Engineering & MoE Key Lab of AI Shanghai Jiao Tong University

Extensive experiments have gradually revealed the potential performance bottleneck of modeling Combinatorial Optimization (CO) solving as neural solution prediction tasks. The neural networks, in their pursuit of minimizing the average objective score across the distribution of historical problem instances, diverge from the core target of CO of seeking optimal solutions for every test instance. This calls for an effective search on each problem instance, while the model should serve to provide supporting knowledge that benefits the search. To this end, we propose T2T (Training to Testing) framework that first leverages the generative modeling to estimate the high-quality solution distribution for each instance during training, and then conducts a gradient-based search within the solution space during testing. The proposed neural search paradigm consistently leverages generative modeling, specifically diffusion, for graduated solution improvement. It disrupts the local structure of the given solution by introducing noise and reconstructs a lower-cost solution guided by the optimization objective. Experimental results on Traveling Salesman Problem (TSP) and Maximal Indep.ndent Set (MIS) show the significant superiority of T2T, demonstrating an average performance gain of 49.15% for TSP solving and 17.27% for MIS solving compared to the previous state-of-the-art.

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Relative entropic optimal transport: a (prior-aware) matching perspective to (unbalanced) classification 23

Relative entropic optimal transport: a (prior-aware) matchin...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Liangliang Shi Haoyu Zhen Gu Zhang Junchi Yan Dept. of Computer Science and Engineering & MoE Key Lab of AI Shanghai Jiao Tong University

Classification is a fundamental problem in machine learning, and considerable efforts have been recently devoted to the demanding long-tailed setting due to its prevalence in nature. dep.rture from the Bayesian framework, this paper rethinks classification from a matching perspective by studying the matching probability between samples and labels with optimal transport (OT) formulation. Specifically, we first propose a new variant of optimal transport, called Relative Entropic Optimal Transport (RE-OT), which guides the coupling solution to a known prior information matrix. We gives some theoretical results and their proof for RE-OT and surprisingly find RE-OT can help to deblur for barycenter images. Then we adopt inverse RE-OT for training long-tailed data and find that the loss derived from RE-OT has a similar form to Softmax-based cross-entropy loss, indicating a close connection between optimal transport and classification and the potential for transferring concepts between these two academic fields, such as barycentric projection in OT, which can map the labels back to the feature space. We further derive an epoch-varying RE-OT loss, and do the experiments on unbalanced image classification, molecule classification, instance segmentation and representation learning. Experimental results show its effectiveness.

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BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning 29

BiBL: AMR Parsing and Generation with Bidirectional Bayesian...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Cheng, Ziming Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

Meaning Representation (AMR) offers a unified semantic representation for natural language sentences. Thus transformation between AMR and text yields two transition tasks in opposite directions, i.e., Text-to-AMR parsing and AMR-to-Text generation. Existing AMR studies only focus on one-side improvements despite the duality of the two tasks, and their improvements are greatly attributed to the inclusion of large extra training data or complex structure modifications which harm the inference speed. Instead, we propose data-efficient Bidirectional Bayesian learning (BiBL) to facilitate bidirectional information transition by adopting a single-stage multitasking strategy so that the resulting model may enjoy much lighter training at the same time. Evaluation on benchmark datasets shows that our proposed BiBL outperforms strong previous seq2seq refinements without the help of extra data which is indispensable in existing counterpart models. We release the codes of BiBL at: https://***/KHAKhazeus/BiBL. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Leveraging hallucinations to reduce manual prompt dep.ndency in promptable segmentation 24

Leveraging hallucinations to reduce manual prompt dependency...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jian Hu Jiayi Lin Junchi Yan Shaogang Gong School of Electronic Engineering and Computer Science Queen Mary University of London Dept. of CSE & School of AI & Moe Key Lab of AI Shanghai Jiao Tong University

ISBN: (纸本)9798331314385

Promptable segmentation typically requires instance-specific manual prompts to guide the segmentation of each desired object. To minimize such a need, task-generic promptable segmentation has been introduced, which employs a single task-generic prompt to segment various images of different objects in the same task. Current methods use Multimodal Large Language Models (MLLMs) to rea son detailed instance-specific prompts from a task-generic prompt for improving segmentation accuracy. The effectiveness of this segmentation heavily dep.nds on the precision of these derived prompts. However, MLLMs often suffer hallucinations during reasoning, resulting in inaccurate prompting. While existing methods focus on eliminating hallucinations to improve a model, we argue that MLLM hallucinations can reveal valuable contextual insights when leveraged correctly, as they represent pre-trained large-scale knowledge beyond individual images. In this paper, we utilize hallucinations to mine task-related information from images and verify its accuracy for enhancing precision of the generated prompts. Specifically, we introduce an iterative Prompt-Mask Cycle generation framework (ProMaC) with a prompt generator and a mask generator. The prompt generator uses a multi-scale chain of thought prompting, initially exploring hallucinations for extracting extended contextual knowledge on a test image. These hallucinations are then reduced to formulate precise instance-specific prompts, directing the mask generator to produce masks that are consistent with task semantics by mask semantic alignment. The generated masks iteratively induce the prompt generator to focus more on task-relevant image areas and reduce irrelevant hallucinations, resulting jointly in better prompts and masks. Experiments on 5 benchmarks demonstrate the effectiveness of ProMaC. Code given in https://***/ProMaC/.

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Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding

Fast and High-Quality Auto-Regressive Speech Synthesis via S...

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

作者： Bohan Li Hankun Wang Situo Zhang Yiwei Guo Kai Yu Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The auto-regressive (AR) architecture, exemplified by models such as GPT, is extensively utilized in modern Text-to-Speech (TTS) systems. However, it often leads to considerable inference delays, primarily due to the challenges associated with next-token prediction in long speech sequences. In this work, we introduce VADUSA, one of the first approaches to accelerate AR-based TTS through speculative decoding. Our findings demonstrate that VADUSA not only delivers a significant reduction in inference time but also enhances TTS quality by employing draft heads to predict future speech tokens in an auto-regressive manner. Additionally, the incorporation of a tolerance mechanism during the sampling process further boosts performance, yielding approximately a 3× speedup in AR TTS. Moreover, our approach exhibits strong generalization across diverse datasets and various speech token types.

关键词： Speech enhancement Signal processing Acoustics Decoding Text to speech Delays

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Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing 29

Nested Named Entity Recognition as Corpus Aware Holistic Str...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Yang, Yifei Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

As a fundamental natural language processing task and one of core knowledge extraction techniques, named entity recognition (NER) is widely used to extract information from texts for downstream tasks. Nested NER is a branch of NER in which the named entities (NEs) are nested with each other. However, most of the previous studies on nested NER usually apply linear structure to model the nested NEs which are actually accommodated in a hierarchical structure. Thus in order to address this mismatch, this work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all. Besides, there is no research on applying corpus-level information to NER currently. To make up for the loss of this information, we introduce Point-wise Mutual Information (PMI) and other frequency features from corpus-aware statistics for even better performance by holistic modeling from sentence-level to corpus-level. Experiments show that our model yields promising results on widely-used benchmarks which approach or even achieve state-of-the-art. Further empirical studies show that our proposed corpus-aware features can substantially improve NER domain adaptation, which demonstrates the surprising advantage of our proposed corpus-level holistic structure modeling. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Syntactics

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Aspect-based Sentiment Analysis as Machine Reading Comprehension 29

Aspect-based Sentiment Analysis as Machine Reading Comprehen...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Yang, Yifei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

Existing studies typically handle aspect-based sentiment analysis by stacking multiple neural modules, which inevitably result in severe error propagation. Instead, we propose a novel end-to-end framework, MRCOOL: MRC-PrOmpt mOdeL framework, where numerous sentiment aspects are elicited by a machine reading comprehension (MRC) model and their corresponding sentiment polarities are classified in a prompt learning way. Experiments show that our end-to-end framework consistently yields promising results on widely-used benchmark datasets which significantly outperform existing state-of-the-art models or achieve comparable performance. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Sentiment analysis

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Smooth-Foley: Creating Continuous Sound for Video-to-Audio Generation Under Semantic Guidance

Smooth-Foley: Creating Continuous Sound for Video-to-Audio G...

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

作者： Yaoyun Zhang Xuenan Xu Mengyue Wu Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The video-to-audio (V2A) generation task has drawn attention in the field of multimedia due to the practicality in producing Foley sound. Semantic and temporal conditions are fed to the generation model to indicate sound events and temporal occurrence. Recent studies on synthesizing immersive and synchronized audio are faced with challenges on videos with moving visual presence. The temporal condition is not accurate enough while low-resolution semantic condition exacerbates the problem. To tackle these challenges, we propose Smooth-Foley, a V2A generative model taking semantic guidance from the textual label across the generation to enhance both semantic and temporal alignment in audio. Two adapters are trained to leverage pre-trained text-to-audio generation models. A frame adapter integrates high-resolution frame-wise video features while a temporal adapter integrates temporal conditions obtained from similarities of visual frames and textual labels. The incorporation of semantic guidance from textual labels achieves precise audio-video alignment. We conduct extensive quantitative and qualitative experiments. Results show that Smooth-Foley performs better than existing models on both continuous sound scenarios and general scenarios. With semantic guidance, the audio generated by Smooth-Foley exhibits higher quality and better adherence to physical laws. Homepage: https://***/***/

关键词： Adaptation models Visualization Accuracy Semantics Streaming media Signal processing Synchronization Sustainable development Speech processing Videos

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