activity
20192025
most citedStARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning

16 citations · 22 across the 13 of their papers we have counts for

collaborators

20 papers

cs.CV2025

OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory

Zhaochong An, Menglin Jia, Haonan Qiu +12

Storytelling in real-world videos often unfolds through multiple shots -- discontinuous yet semantically connected clips that together convey a coherent narrative. However, existin…

cs.CV2024

Adaptive Caching for Faster Video Generation with Diffusion Transformers

Kumara Kahatapitiya, Haozhe Liu, Sen He +5

Generating temporally-consistent high-fidelity videos can be computationally expensive, especially over longer temporal spans. More-recent Diffusion Transformers (DiTs) -- despite…

cs.CV2024★ 1 cited

MarDini: Masked Autoregressive Diffusion for Video Generation at Scale

Haozhe Liu, Shikun Liu, Zijian Zhou +12

We introduce MarDini, a new family of video diffusion models that integrate the advantages of masked auto-regression (MAR) into a unified diffusion model (DM) framework. Here, MAR…

cs.RO2024★ 2 cited

LLaRA: Supercharging Robot Learning Data for Vision-Language Policy

Xiang Li, Cristina Mata, Jongwoo Park +8

Vision Language Models (VLMs) have recently been leveraged to generate robotic actions, forming Vision-Language-Action (VLA) models. However, directly adapting a pretrained VLM for…

cs.CV2024

Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA

Jongwoo Park, Kanchana Ranasinghe, Kumara Kahatapitiya +3

Long-form videos that span across wide temporal intervals are highly information redundant and contain multiple distinct events or entities that are often loosely related. Therefor…

cs.CV2024

Understanding Long Videos with Multimodal Language Models

Kanchana Ranasinghe, Xiang Li, Kumara Kahatapitiya +1

Large Language Models (LLMs) have allowed recent LLM-based approaches to achieve excellent performance on long-video understanding benchmarks. We investigate how extensive world kn…