5 citations · 12 across the 14 of their papers we have counts for
13 papers · 1 filter
WorldOlympiad: Can Your World Model Survive a Triathlon?
Yuke Zhao, Wangbo Zhao, Weijie Wang +8
We introduce WorldOlympiad, a benchmark for diagnosing video-based world models across physical faithfulness, geometric consistency, and interaction fidelity. While existing benchm…
ReCA: Multi-Shot Long Video Extrapolation via Recursive Context Allocation
Akide Liu, Jinbo Xing, Chaojie Mao +8
Minute-scale cinematic video generation is a central challenge for generative video models. Existing paradigms address only fragments of this challenge: single-shot extrapolation p…
CoV: Chain-of-View Prompting for Spatial Reasoning
Haoyu Zhao, Akide Liu, Zeyu Zhang +5
Embodied question answering (EQA) in 3D environments often requires collecting context that is distributed across multiple viewpoints and partially occluded. However, most recent v…
OmniSparse: Training-Aware Fine-Grained Sparse Attention for Long-Video MLLMs
Feng Chen, Yefei He, Shaoxuan He +9
Existing sparse attention methods primarily target inference-time acceleration by selecting critical tokens under predefined sparsity patterns. However, they often fail to bridge t…
An Empirical Study on How Video-LLMs Answer Video Questions
Chenhui Gou, Ziyu Ma, Zicheng Duan +6
Taking advantage of large-scale data and pretrained language models, Video Large Language Models (Video-LLMs) have shown strong capabilities in answering video questions. However,…
FPSAttention: Training-Aware FP8 and Sparsity Co-Design for Fast Video Diffusion
Akide Liu, Zeyu Zhang, Zhexin Li +12
Diffusion generative models have become the standard for producing high-quality, coherent video content, yet their slow inference speeds and high computational demands hinder pract…
ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS
Weijie Wang, Donny Y. Chen, Zeyu Zhang +3
Feed-forward 3D Gaussian Splatting (3DGS) models have recently emerged as a promising solution for novel view synthesis, enabling one-pass inference without the need for per-scene…
CIT: Rethinking Class-incremental Semantic Segmentation with a Class Independent Transformation
Jinchao Ge, Bowen Zhang, Akide Liu +4
Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by dist…
KMM: Key Frame Mask Mamba for Extended Motion Generation
Zeyu Zhang, Hang Gao, Akide Liu +9
Human motion generation is a cut-edge area of research in generative computer vision, with promising applications in video creation, game development, and robotic manipulation. The…
ESA: Annotation-Efficient Active Learning for Semantic Segmentation
Jinchao Ge, Zeyu Zhang, Minh Hieu Phan +4
Active learning enhances annotation efficiency by selecting the most revealing samples for labeling, thereby reducing reliance on extensive human input. Previous methods in semanti…
InfiniMotion: Mamba Boosts Memory in Transformer for Arbitrary Long Motion Generation
Zeyu Zhang, Akide Liu, Qi Chen +5
Text-to-motion generation holds potential for film, gaming, and robotics, yet current methods often prioritize short motion generation, making it challenging to produce long motion…
Motion Mamba: Efficient and Long Sequence Motion Generation
Zeyu Zhang, Akide Liu, Ian Reid +3
Human motion generation stands as a significant pursuit in generative computer vision, while achieving long-sequence and efficient motion generation remains challenging. Recent adv…
CV 3315 Is All You Need : Semantic Segmentation Competition
Akide Liu, Zihan Wang
This competition focus on Urban-Sense Segmentation based on the vehicle camera view. Class highly unbalanced Urban-Sense images dataset challenge the existing solutions and further…