activity
20172024
most citedDual Encoding for Video Retrieval by Text

206 citations · 273 across the 8 of their papers we have counts for

collaborators

8 papers

cs.SI2024

Information Dissemination Model Based on User Attitude and Public Opinion Environment

Xinyu Li, Jinyang Huang, Xiang Zhang +5

Modeling the information dissemination process in social networks is a challenging problem. Despite numerous attempts to address this issue, existing studies often assume that user…

cs.CV2023

Multimodal Feature Extraction and Fusion for Emotional Reaction Intensity Estimation and Expression Classification in Videos with Transformers

Jia Li, Yin Chen, Xuesong Zhang +6

In this paper, we present our advanced solutions to the two sub-challenges of Affective Behavior Analysis in the wild (ABAW) 2023: the Emotional Reaction Intensity (ERI) Estimation…

cs.CL2023★ 1 cited

MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning

Zheng Lian, Haiyang Sun, Licai Sun +15

The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia. The challenge focuses on system robustness and consists of three distinct tra…

cs.CV2020★ 206 cited

Dual Encoding for Video Retrieval by Text

Jianfeng Dong, Xirong Li, Chaoxi Xu +4

This paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described exclusive…

cs.CV2020★ 4 cited

Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition

Zhao Zhang, Zemin Tang, Yang Wang +4

Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Networks (DenseNet), have achieved great success for image representation by discovering deep hierarchical in…

cs.CV2020★ 21 cited

Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network

Yanyan Wei, Zhao Zhang, Yang Wang +4

Removing the rain streaks from single image is still a challenging task, since the shapes and directions of rain streaks in the synthetic datasets are very different from real imag…