8 papers
Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution
Rui Cai, Jun Guo, Xinze He +20
In this report, we introduce Xiaomi-Robotics-0, an advanced vision-language-action (VLA) model optimized for high performance and fast and smooth real-time execution. The key to ou…
T2SGrid: Temporal-to-Spatial Gridification for Video Temporal Grounding
Chaohong Guo, Yihan He, Yongwei Nie +3
Video Temporal Grounding (VTG) aims to localize the video segment that corresponds to a natural language query, which requires a comprehensive understanding of complex temporal dyn…
Nüwa: Mending the Spatial Integrity Torn by VLM Token Pruning
Yihong Huang, Fei Ma, Yihua Shao +4
Vision token pruning has proven to be an effective acceleration technique for the efficient Vision Language Model (VLM). However, existing pruning methods demonstrate excellent per…
E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis
Fei Ma, Han Lin, Yifan Xie +4
Emotion recognition from electroencephalography (EEG) signals remains challenging due to high inter-subject variability, limited labeled data, and the lack of interpretable reasoni…
Enhancing Long Video Question Answering with Scene-Localized Frame Grouping
Xuyi Yang, Wenhao Zhang, Hongbo Jin +5
Current Multimodal Large Language Models (MLLMs) often perform poorly in long video understanding, primarily due to resource limitations that prevent them from processing all video…
TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding
Chaohong Guo, Xun Mo, Yongwei Nie +3
Video Temporal Grounding (VTG) aims to localize specific video segments corresponding to natural language queries. While recent Large Vision-Language Models (LVLMs) employ Reinforc…
Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks Preserving Action Understanding Ability
Zhaoyu Chen, Hongnan Lin, Yongwei Nie +4
Temporal Video Grounding (TVG) aims to localize video segments corresponding to a given textual query, which often describes human actions. However, we observe that current methods…
MotionBank: A Large-scale Video Motion Benchmark with Disentangled Rule-based Annotations
Liang Xu, Shaoyang Hua, Zili Lin +6
In this paper, we tackle the problem of how to build and benchmark a large motion model (LMM). The ultimate goal of LMM is to serve as a foundation model for versatile motion-relat…