8 citations · 11 across the 9 of their papers we have counts for
9 papers
ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations
Junke Wang, Xiao Wang, Jiacheng Pan +16
This paper introduces ARM, a discrete representation-based AutoRegressive Model that unifies image understanding, generation, and editing within a next-token prediction framework.…
BubbleSpec: Turning Long-Tail Bubbles into Speculative Rollout Drafts for Synchronous Reinforcement Learning
Yuhang Xu, Kaibin Tian, Yang Tian +6
Reinforcement Learning (RL) has become a cornerstone for improving the performance of Large Language Models (LLMs). However, its rollout phase constitutes a significant efficiency…
Towards Efficient and Effective Text-to-Video Retrieval with Coarse-to-Fine Visual Representation Learning
Kaibin Tian, Yanhua Cheng, Yi Liu +3
In recent years, text-to-video retrieval methods based on CLIP have experienced rapid development. The primary direction of evolution is to exploit the much wider gamut of visual a…
TeachCLIP: Multi-Grained Teaching for Efficient Text-to-Video Retrieval
Kaibin Tian, Ruixiang Zhao, Hu Hu +4
For text-to-video retrieval (T2VR), which aims to retrieve unlabeled videos by ad-hoc textual queries, CLIP-based methods are dominating. Compared to CLIP4Clip which is efficient a…
ChinaOpen: A Dataset for Open-world Multimodal Learning
Aozhu Chen, Ziyuan Wang, Chengbo Dong +5
This paper introduces ChinaOpen, a dataset sourced from Bilibili, a popular Chinese video-sharing website, for open-world multimodal learning. While the state-of-the-art multimodal…
Renmin University of China at TRECVID 2022: Improving Video Search by Feature Fusion and Negation Understanding
Xirong Li, Aozhu Chen, Ziyue Wang +4
We summarize our TRECVID 2022 Ad-hoc Video Search (AVS) experiments. Our solution is built with two new techniques, namely Lightweight Attentional Feature Fusion (LAFF) for combini…
MVP: Robust Multi-View Practice for Driving Action Localization
Jingjie Shang, Kunchang Li, Kaibin Tian +2
Distracted driving causes thousands of deaths per year, and how to apply deep-learning methods to prevent these tragedies has become a crucial problem. In Track3 of the 6th AI City…
Co-Teaching for Unsupervised Domain Adaptation and Expansion
Hailan Lin, Qijie Wei, Kaibin Tian +2
Unsupervised Domain Adaptation (UDA) essentially trades a model's performance on a source domain for improving its performance on a target domain. To overcome this, Unsupervised Do…
Unsupervised Domain Expansion for Visual Categorization
Jie Wang, Kaibin Tian, Dayong Ding +2
Expanding visual categorization into a novel domain without the need of extra annotation has been a long-term interest for multimedia intelligence. Previously, this challenge has b…