84 citations · 304 across the 36 of their papers we have counts for
7 papers · 1 filter
Semi-supervised Multi-modal Emotion Recognition with Cross-Modal Distribution Matching
Jingjun Liang, Ruichen Li, Qin Jin
Automatic emotion recognition is an active research topic with wide range of applications. Due to the high manual annotation cost and inevitable label ambiguity, the development of…
The End-of-End-to-End: A Video Understanding Pentathlon Challenge (2020)
Samuel Albanie, Yang Liu, Arsha Nagrani +18
We present a new video understanding pentathlon challenge, an open competition held in conjunction with the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.…
Team RUC_AIM3 Technical Report at Activitynet 2020 Task 2: Exploring Sequential Events Detection for Dense Video Captioning
Yuqing Song, Shizhe Chen, Yida Zhao +1
Detecting meaningful events in an untrimmed video is essential for dense video captioning. In this work, we propose a novel and simple model for event sequence generation and explo…
YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos
Shizhe Chen, Weiying Wang, Ludan Ruan +2
The goal of the YouMakeup VQA Challenge 2020 is to provide a common benchmark for fine-grained action understanding in domain-specific videos e.g. makeup instructional videos. We p…
Better Captioning with Sequence-Level Exploration
Jia Chen, Qin Jin
Sequence-level learning objective has been widely used in captioning tasks to achieve the state-of-the-art performance for many models. In this objective, the model is trained by t…
Fine-grained Video-Text Retrieval with Hierarchical Graph Reasoning
Shizhe Chen, Yida Zhao, Qin Jin +1
Cross-modal retrieval between videos and texts has attracted growing attentions due to the rapid emergence of videos on the web. The current dominant approach for this problem is t…
Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs
Shizhe Chen, Qin Jin, Peng Wang +1
Humans are able to describe image contents with coarse to fine details as they wish. However, most image captioning models are intention-agnostic which can not generate diverse des…