activity
20182025
most citedsDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG20251 cited

sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging

Jingyuan Chen, Yuan Yao, Mie Anderson +5

Automatic sleep staging based on electroencephalography (EEG) and electromyography (EMG) signals is an important aspect of sleep-related research. Current sleep staging methods suf…

cs.CV2025

Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion

Jingyuan Chen, Fuchen Long, Jie An +4

The first-in-first-out (FIFO) video diffusion, built on a pre-trained text-to-video model, has recently emerged as an effective approach for tuning-free long video generation. This…

cs.CV2021

The Blessings of Unlabeled Background in Untrimmed Videos

Yuan Liu, Jingyuan Chen, Zhenfang Chen +3

Weakly-supervised Temporal Action Localization (WTAL) aims to detect the action segments with only video-level action labels in training. The key challenge is how to distinguish th…

cs.CV2020

Learning to Segment the Tail

Xinting Hu, Yi Jiang, Kaihua Tang +3

Real-world visual recognition requires handling the extreme sample imbalance in large-scale long-tailed data. We propose a "divide&conquer" strategy for the challenging LVIS task:…

cs.CV2018

Real-Time Referring Expression Comprehension by Single-Stage Grounding Network

Xinpeng Chen, Lin Ma, Jingyuan Chen +3

In this paper, we propose a novel end-to-end model, namely Single-Stage Grounding network (SSG), to localize the referent given a referring expression within an image. Different fr…

cs.CV2018

Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset

Xinpeng Chen, Jingyuan Chen, Lin Ma +4

Nowadays, billions of videos are online ready to be viewed and shared. Among an enormous volume of videos, some popular ones are widely viewed by online users while the majority at…