13 papers · 1 filter
Hyperbolic Multimodal Continual Learning: A Closest-Admissible Solution
Jiahong Liu, Ming Shen, Xiaohao Liu +4
Existing continual-learning methods protect parameters, replayed examples, or Euclidean feature subspaces. When applied to hyperbolic multimodal models, they do not explicitly pres…
Walk the Talk: Bridging the Reasoning-Action Gap for Thinking with Images via Multimodal Agentic Policy Optimization
Wenhao Yang, Yu Xia, Jinlong Huang +10
Recent advancements in Multimodal Large Language Models (MLLMs) have incentivized models to ``think with images'' by actively invoking visual tools during multi-turn reasoning. The…
AUHead: Realistic Emotional Talking Head Generation via Action Units Control
Jiayi Lyu, Leigang Qu, Wenjing Zhang +6
Realistic talking-head video generation is critical for virtual avatars, film production, and interactive systems. Current methods struggle with nuanced emotional expressions due t…
DNA: Uncovering Universal Latent Forgery Knowledge
Jingtong Dou, Chuancheng Shi, Yemin Wang +6
As generative AI achieves hyper-realism, superficial artifact detection has become obsolete. While prevailing methods rely on resource-intensive fine-tuning of black-box backbones,…
Lingua-SafetyBench: A Benchmark for Safety Evaluation of Multilingual Vision-Language Models
Enyi Shi, Pengyang Shao, Yanxin Zhang +5
The robust safety of Vision-Language Large Models (VLLMs) against joint multilingual and multimodal threats remains severely underexplored. Current benchmarks typically isolate the…
ITS3D: Inference-Time Scaling for Text-Guided 3D Diffusion Models
Zhenglin Zhou, Fan Ma, Xiaobo Xia +3
We explore inference-time scaling in text-guided 3D diffusion models to enhance generative quality without additional training. To this end, we introduce ITS3D, a framework that fo…
AnchorFlow: Training-Free 3D Editing via Latent Anchor-Aligned Flows
Zhenglin Zhou, Fan Ma, Chengzhuo Gui +4
Training-free 3D editing aims to modify 3D shapes based on human instructions without model finetuning. It plays a crucial role in 3D content creation. However, existing approaches…
Calibrated Multimodal Representation Learning with Missing Modalities
Xiaohao Liu, Xiaobo Xia, Jiaheng Wei +4
Multimodal representation learning harmonizes distinct modalities by aligning them into a unified latent space. Recent research generalizes traditional cross-modal alignment to pro…
Logic Unseen: Revealing the Logical Blindspots of Vision-Language Models
Yuchen Zhou, Jiayu Tang, Shuo Yang +6
Vision-Language Models (VLMs), exemplified by CLIP, have emerged as foundational for multimodal intelligence. However, their capacity for logical understanding remains significantl…
Principled Multimodal Representation Learning
Xiaohao Liu, Xiaobo Xia, See-Kiong Ng +1
Multimodal representation learning seeks to create a unified representation space by integrating diverse data modalities to improve multimodal understanding. Traditional methods of…
Zero-1-to-A: Zero-Shot One Image to Animatable Head Avatars Using Video Diffusion
Zhenglin Zhou, Fan Ma, Hehe Fan +1
Animatable head avatar generation typically requires extensive data for training. To reduce the data requirements, a natural solution is to leverage existing data-free static avata…
Towards Modality Generalization: A Benchmark and Prospective Analysis
Xiaohao Liu, Xiaobo Xia, Zhuo Huang +2
Multi-modal learning has achieved remarkable success by integrating information from various modalities, achieving superior performance in tasks like recognition and retrieval comp…
ExpLLM: Towards Chain of Thought for Facial Expression Recognition
Xing Lan, Jian Xue, Ji Qi +3
Facial expression recognition (FER) is a critical task in multimedia with significant implications across various domains. However, analyzing the causes of facial expressions is es…