6 papers · 1 filter
A Reconstruction-Based Framework for Caption Evaluation Beyond Reference Captions
Zhijiang Tang, Jiaxin Qi, Kaihua Tang +2
Image captioning is a primary task in vision--language research, yet assessing how faithfully a caption preserves image semantics without relying on reference captions remains unse…
Scaling Test-Time Robustness of Vision-Language Models via Self-Critical Inference Framework
Kaihua Tang, Jiaxin Qi, Jinli Ou +2
The emergence of Large Language Models (LLMs) has driven rapid progress in multi-modal learning, particularly in the development of Large Vision-Language Models (LVLMs). However, e…
LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs
Keda Tao, Yuhua Zheng, Jia Xu +13
Recent advancements in omnimodal large language models (OmniLLMs) have significantly improved the comprehension of audio and video inputs. However, current evaluations primarily fo…
Spatial Transcriptomics as Images for Large-Scale Pretraining
Yishun Zhu, Jiaxin Qi, Jian Wang +2
Spatial Transcriptomics (ST) profiles thousands of gene expression values at discrete spots with precise coordinates on tissue sections, preserving spatial context essential for cl…
Think-Before-Draw: Decomposing Emotion Semantics & Fine-Grained Controllable Expressive Talking Head Generation
Hanlei Shi, Leyuan Qu, Yu Liu +3
Emotional talking-head generation has emerged as a pivotal research area at the intersection of computer vision and multimodal artificial intelligence, with its core value lying in…
From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition
Yu Liu, Leyuan Qu, Hanlei Shi +3
Dynamic Facial Expression Recognition (DFER) aims to identify human emotions from temporally evolving facial movements and plays a critical role in affective computing. While recen…