Search Results for author: Bin Cao

Found 14 papers, 2 papers with code

Robust Deep Joint Source-Channel Coding Enabled Distributed Image Transmission with Imperfect Channel State Information

no code implementations19 Nov 2024 Biao Dong, Bin Cao, Guan Gui, Qinyu Zhang

This work is concerned with robust distributed multi-view image transmission over a severe fading channel with imperfect channel state information (CSI), wherein the sources are slightly correlated.

Decoder

Fundamental Limits of Pulse Based UWB ISAC Systems: A Parameter Estimation Perspective

no code implementations17 Oct 2024 Fan Liu, Tingting Zhang, Zenan Zhang, Bin Cao, Yuan Shen, Qinyu Zhang

Impulse radio ultra-wideband (IR-UWB) signals stand out for their high temporal resolution, low cost, and large bandwidth, making them a highly promising option for integrated sensing and communication (ISAC) systems.

Quo Vadis, Motion Generation? From Large Language Models to Large Motion Models

no code implementations4 Oct 2024 Ye Wang, Sipeng Zheng, Bin Cao, Qianshan Wei, Qin Jin, Zongqing Lu

Inspired by the recent success of LLMs, the field of human motion understanding has increasingly shifted towards the development of large motion models.

Motion Generation

The Instance-centric Transformer for the RVOS Track of LSVOS Challenge: 3rd Place Solution

no code implementations20 Aug 2024 Bin Cao, Yisi Zhang, Hanyi Wang, Xingjian He, Jing Liu

Referring Video Object Segmentation is an emerging multi-modal task that aims to segment objects in the video given a natural language expression.

Referring Video Object Segmentation Retrieval +2

Decision Transformer for IRS-Assisted Systems with Diffusion-Driven Generative Channels

no code implementations28 Jun 2024 Jie Zhang, Jun Li, Zhe Wang, Yu Han, Long Shi, Bin Cao

In this paper, we propose a novel diffusion-decision transformer (D2T) architecture to optimize the beamforming strategies for intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) communication systems.

Reinforcement Learning (RL)

2nd Place Solution for MeViS Track in CVPR 2024 PVUW Workshop: Motion Expression guided Video Segmentation

no code implementations20 Jun 2024 Bin Cao, Yisi Zhang, Xuanxu Lin, Xingjian He, Bo Zhao, Jing Liu

Motion Expression guided Video Segmentation is a challenging task that aims at segmenting objects in the video based on natural language expressions with motion descriptions.

Instance Segmentation Referring Video Object Segmentation +5

SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Model

1 code implementation28 Feb 2024 Bin Cao, Jianhao Yuan, Yexin Liu, Jian Li, Shuyang Sun, Jing Liu, Bo Zhao

To alleviate artifacts and improve quality of synthetic images, we fine-tune Vision-Language Model (VLM) as artifact classifier to automatically identify and classify a wide range of artifacts and provide supervision for further optimizing generative models.

Image Generation Language Modeling +1

Secure and Efficient Federated Learning Through Layering and Sharding Blockchain

no code implementations27 Apr 2021 Shuo Yuan, Bin Cao, Yao Sun, Zhiguo Wan, Mugen Peng

Introducing blockchain into Federated Learning (FL) to build a trusted edge computing environment for transmission and learning has attracted widespread attention as a new decentralized learning pattern.

Edge-computing Federated Learning

Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain Approach

no code implementations27 Apr 2021 Mingrui Cao, Long Zhang, Bin Cao

Due to the distributed characteristics of Federated Learning (FL), the vulnerability of global model and coordination of devices are the main obstacle.

Anomaly Detection Federated Learning

Smarnet: Teaching Machines to Read and Comprehend Like Human

no code implementations8 Oct 2017 Zheqian Chen, Rongqin Yang, Bin Cao, Zhou Zhao, Deng Cai, Xiaofei He

Machine Comprehension (MC) is a challenging task in Natural Language Processing field, which aims to guide the machine to comprehend a passage and answer the given question.

Question Answering Reading Comprehension +1

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