5 citations · 7 across the 15 of their papers we have counts for
8 papers · 1 filter
FineXtrol: Controllable Motion Generation via Fine-Grained Text
Keming Shen, Bizhu Wu, Junliang Chen +2
Recent works have sought to enhance the controllability and precision of text-driven motion generation. Some approaches leverage large language models (LLMs) to produce more detail…
EyePCR: A Comprehensive Benchmark for Fine-Grained Perception, Knowledge Comprehension and Clinical Reasoning in Ophthalmic Surgery
Gui Wang, Yang Wennuo, Xusen Ma +7
MLLMs (Multimodal Large Language Models) have showcased remarkable capabilities, but their performance in high-stakes, domain-specific scenarios like surgical settings, remains lar…
RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Media
Yudong Li, Yufei Sun, Peiru Yang +7
We introduce RedNote-Vibe, a dataset spanning five years (pre-LLM to July 2025) sourced from lifestyle platform RedNote (Xiaohongshu), capturing the temporal dynamics of content cr…
DisFaceRep: Representation Disentanglement for Co-occurring Facial Components in Weakly Supervised Face Parsing
Xiaoqin Wang, Xianxu Hou, Meidan Ding +4
Face parsing aims to segment facial images into key components such as eyes, lips, and eyebrows. While existing methods rely on dense pixel-level annotations, such annotations are…
Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications
Zizhan Ma, Wenxuan Wang, Meidan Ding +7
The proliferation of Large Language Models (LLMs) in medicine has enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, a…
A Survey of Multimodal Ophthalmic Diagnostics: From Task-Specific Approaches to Foundational Models
Xiaoling Luo, Ruli Zheng, Qiaojian Zheng +6
Visual impairment represents a major global health challenge, with multimodal imaging providing complementary information that is essential for accurate ophthalmic diagnosis. This…
FineMotion: A Dataset and Benchmark with both Spatial and Temporal Annotation for Fine-grained Motion Generation and Editing
Bizhu Wu, Jinheng Xie, Meidan Ding +5
Generating realistic human motions from textual descriptions has undergone significant advancements. However, existing methods often overlook specific body part movements and their…
MG-MotionLLM: A Unified Framework for Motion Comprehension and Generation across Multiple Granularities
Bizhu Wu, Jinheng Xie, Keming Shen +5
Recent motion-aware large language models have demonstrated promising potential in unifying motion comprehension and generation. However, existing approaches primarily focus on coa…