52 citations · 93 across the 12 of their papers we have counts for
13 papers
AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation
Ziwei Zhou, Zeyuan Lai, Rui Wang +6
Text-to-Audio-Video (T2AV) generation is rapidly becoming a core interface for media creation, yet its evaluation remains fragmented. Existing benchmarks largely assess audio and v…
FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance
Quanhao Li, Zhen Xing, Rui Wang +4
Recent advances in trajectory-controllable video generation have achieved remarkable progress. Previous methods mainly use adapter-based architectures for precise motion control al…
Video-in-the-Loop: Span-Grounded Long Video QA with Interleaved Reasoning
Chendong Wang, Donglin Bai, Yifan Yang +11
We present \emph{Video-in-the-Loop} (ViTL), a two-stage long-video QA framework that preserves a fixed token budget by first \emph{localizing} question-relevant interval(s) with a…
ReasonGen-R1: CoT for Autoregressive Image generation models through SFT and RL
Yu Zhang, Yunqi Li, Yifan Yang +7
Although chain-of-thought reasoning and reinforcement learning (RL) have driven breakthroughs in NLP, their integration into generative vision models remains underexplored. We intr…
Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across Modalities
Ziwei Zhou, Rui Wang, Zuxuan Wu +1
Recent Multimodal Large Language Models (MLLMs) achieve promising performance on visual and audio benchmarks independently. However, the ability of these models to process cross-mo…
MagicMotion: Controllable Video Generation with Dense-to-Sparse Trajectory Guidance
Quanhao Li, Zhen Xing, Rui Wang +3
Recent advances in video generation have led to remarkable improvements in visual quality and temporal coherence. Upon this, trajectory-controllable video generation has emerged to…
SEED-Bench-2: Benchmarking Multimodal Large Language Models
Bohao Li, Yuying Ge, Yixiao Ge +4
Multimodal large language models (MLLMs), building upon the foundation of powerful large language models (LLMs), have recently demonstrated exceptional capabilities in generating n…
SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Bohao Li, Rui Wang, Guangzhi Wang +3
Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable…
Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning
Rui Wang, Dongdong Chen, Zuxuan Wu +5
Benefiting from masked visual modeling, self-supervised video representation learning has achieved remarkable progress. However, existing methods focus on learning representations…
Video Mobile-Former: Video Recognition with Efficient Global Spatial-temporal Modeling
Rui Wang, Zuxuan Wu, Dongdong Chen +6
Transformer-based models have achieved top performance on major video recognition benchmarks. Benefiting from the self-attention mechanism, these models show stronger ability of mo…
BEVT: BERT Pretraining of Video Transformers
Rui Wang, Dongdong Chen, Zuxuan Wu +6
This paper studies the BERT pretraining of video transformers. It is a straightforward but worth-studying extension given the recent success from BERT pretraining of image transfor…
A Multimodal Framework for Video Ads Understanding
Zejia Weng, Lingchen Meng, Rui Wang +2
There is a growing trend in placing video advertisements on social platforms for online marketing, which demands automatic approaches to understand the contents of advertisements e…
Cross-domain Contrastive Learning for Unsupervised Domain Adaptation
Rui Wang, Zuxuan Wu, Zejia Weng +3
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. Most existing UDA methods learn d…