Search Results for author: Liang Song

Found 20 papers, 6 papers with code

Extracting and Transferring Abilities For Building Multi-lingual Ability-enhanced Large Language Models

no code implementations10 Oct 2024 Zhipeng Chen, Liang Song, Kun Zhou, Wayne Xin Zhao, Bingning Wang, WeiPeng Chen, Ji-Rong Wen

In the extraction stage, we firstly locate key neurons that are highly related to specific abilities, and then employ them to extract the transferable ability-specific weights.

End-Cloud Collaboration Framework for Advanced AI Customer Service in E-commerce

no code implementations20 Sep 2024 Liangyu Teng, Yang Liu, Jing Liu, Liang Song

Specifically, the large cloud model acts as a teacher, guiding and promoting the learning of the end model, which significantly reduces the end model's reliance on large-scale, high-quality data and thereby addresses the data bottleneck in traditional end model training, offering a new paradigm for the rapid deployment of industry applications.

Explain EEG-based End-to-end Deep Learning Models in the Frequency Domain

no code implementations25 Jul 2024 Hanqi Wang, Kun Yang, Jingyu Zhang, Tao Chen, Liang Song

The recent rise of EEG-based end-to-end deep learning models presents a significant challenge in elucidating how these models process raw EEG signals and generate predictions in the frequency domain.

Deep Learning EEG +1

MetaGPT: Merging Large Language Models Using Model Exclusive Task Arithmetic

no code implementations17 Jun 2024 Yuyan Zhou, Liang Song, Bingning Wang, WeiPeng Chen

The advent of large language models (LLMs) like GPT-4 has catalyzed the exploration of multi-task learning (MTL), in which a single model demonstrates proficiency across diverse tasks.

Computational Efficiency Task Arithmetic

Networking Systems for Video Anomaly Detection: A Tutorial and Survey

1 code implementation16 May 2024 Jing Liu, Yang Liu, Jieyu Lin, Jielin Li, Peng Sun, Bo Hu, Liang Song, Azzedine Boukerche, Victor C. M. Leung

With the advancements in deep learning and edge computing, VAD has made significant progress and advances synergized with emerging applications in smart cities and video internet, which has moved beyond the conventional research scope of algorithm engineering to deployable Networking Systems for VAD (NSVAD), a practical hotspot for intersection exploration in the AI, IoVT, and computing fields.

Anomaly Detection Edge-computing +2

Cascaded Self-supervised Learning for Subject-independent EEG-based Emotion Recognition

no code implementations6 Mar 2024 Hanqi Wang, Tao Chen, Liang Song

Inspired by recent efforts in combining low-level and high-level tasks in deep learning, we propose a cascaded self-supervised architecture for EEG emotion recognition.

Contrastive Learning Deep Learning +3

SC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views

no code implementations10 Sep 2023 Liang Song, Guangming Wang, Jiuming Liu, Zhenyang Fu, Yanzi Miao, Hesheng

By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results.

Novel View Synthesis SSIM

Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception

1 code implementation ICCV 2023 Kun Yang, Dingkang Yang, Jingyu Zhang, Mingcheng Li, Yang Liu, Jing Liu, Hanqi Wang, Peng Sun, Liang Song

In this paper, we propose SCOPE, a novel collaborative perception framework that aggregates the spatio-temporal awareness characteristics across on-road agents in an end-to-end manner.

3D Object Detection Autonomous Vehicles +1

A novel efficient Multi-view traffic-related object detection framework

no code implementations23 Feb 2023 Kun Yang, Jing Liu, Dingkang Yang, Hanqi Wang, Peng Sun, Yanni Zhang, Yan Liu, Liang Song

With the rapid development of intelligent transportation system applications, a tremendous amount of multi-view video data has emerged to enhance vehicle perception.

Model Selection object-detection +1

Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

1 code implementation10 Feb 2023 Yang Liu, Dingkang Yang, Yan Wang, Jing Liu, Jun Liu, Azzedine Boukerche, Peng Sun, Liang Song

Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within videos.

Anomaly Detection Event Detection +2

Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification

no code implementations11 Mar 2022 Jianzhang Zheng, Fan Yang, Hao Shen, Xuan Tang, Mingsong Chen, Liang Song, Xian Wei

We propose an algorithmic framework that leverages the advantages of the DNNs for data self-expression and task-specific predictions, to improve image classification.

Classification Image Classification

Towards Understanding the Cause of Error in Few-Shot Learning

no code implementations1 Jan 2021 Liang Song, Jinlu Liu, Yongqiang Qin

We first introduce and derive a theoretical upper bound of error rate which is constrained to 1) linear separability in the learned embedding space and 2) discrepancy of task-specific and task-independent classifier.

Few-Shot Learning

BiOpt: Bi-Level Optimization for Few-Shot Segmentation

no code implementations23 Nov 2020 Jinlu Liu, Liang Song, Yongqiang Qin

On each task, the inner loop aims to learn optimized prototypes from the query images.

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