18 citations · 49 across the 15 of their papers we have counts for
9 papers · 1 filter
DensityKV: Density-Guided KV Cache Compression for Long Video Generation
Wenqu Zhao, Xuemin Chi, Xin Zhang +6
Autoregressive video diffusion models enable streaming generation through sliding-window attention, but each generated block is conditioned on previously generated content, causing…
TetherCache: Stabilizing Autoregressive Long-Form Video Generation with Gated Recall and Trusted Alignment
Yu Meng, Xiangyang Luo, Letian Li +5
Autoregressive video diffusion models provide a natural formulation for streaming and variable-length video generation by conditioning newly generated frames on previously generate…
UniVLR: Unifying Text and Vision in Visual Latent Reasoning for Multimodal LLMs
Houcheng Jiang, Jiajun Fu, Junfeng Fang +4
Multimodal large language models are increasingly expected to perform thinking with images, yet existing visual latent reasoning methods still rely on explicit textual chain-of-tho…
HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer
Qi Cai, Jingwen Chen, Chengmin Gao +22
The evolution of visual generative models has long been constrained by fragmented architectures relying on disjoint text encoders and external VAEs. In this report, we present HiDr…
RoboScape: Physics-informed Embodied World Model
Yu Shang, Xin Zhang, Yinzhou Tang +4
World models have become indispensable tools for embodied intelligence, serving as powerful simulators capable of generating realistic robotic videos while addressing critical data…
Balanced Token Pruning: Accelerating Vision Language Models Beyond Local Optimization
Kaiyuan Li, Xiaoyue Chen, Chen Gao +2
Large Vision-Language Models (LVLMs) have shown impressive performance across multi-modal tasks by encoding images into thousands of tokens. However, the large number of image toke…
PLPHP: Per-Layer Per-Head Vision Token Pruning for Efficient Large Vision-Language Models
Yu Meng, Kaiyuan Li, Chenran Huang +4
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a range of multimodal tasks. However, their inference efficiency is constrained by the large n…
Defining and Evaluating Visual Language Models' Basic Spatial Abilities: A Perspective from Psychometrics
Wenrui Xu, Dalin Lyu, Weihang Wang +3
The Theory of Multiple Intelligences underscores the hierarchical nature of cognitive capabilities. To advance Spatial Artificial Intelligence, we pioneer a psychometric framework…
Understanding and Evaluating Hallucinations in 3D Visual Language Models
Ruiying Peng, Kaiyuan Li, Weichen Zhang +3
Recently, 3D-LLMs, which combine point-cloud encoders with large models, have been proposed to tackle complex tasks in embodied intelligence and scene understanding. In addition to…