5 papers
Enhancing Descriptive Captions with Visual Attributes for Multimodal Perception
Yanpeng Sun, Jing Hao, Ke Zhu +6
Training Large Multimodality Models (LMMs) relies on descriptive image caption that connects image and language. Existing methods for generating such captions often rely on distill…
Interpretable Face Anti-Spoofing: Enhancing Generalization with Multimodal Large Language Models
Guosheng Zhang, Keyao Wang, Haixiao Yue +5
Face Anti-Spoofing (FAS) is essential for ensuring the security and reliability of facial recognition systems. Most existing FAS methods are formulated as binary classification tas…
Revisiting MLLMs: An In-Depth Analysis of Image Classification Abilities
Huan Liu, Lingyu Xiao, Jiangjiang Liu +4
With the rapid advancement of Multimodal Large Language Models (MLLMs), a variety of benchmarks have been introduced to evaluate their capabilities. While most evaluations have foc…
ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts
Sinan Du, Guosheng Zhang, Keyao Wang +7
Parameter-efficient transfer learning (PETL) has become a promising paradigm for adapting large-scale vision foundation models to downstream tasks. Typical methods primarily levera…
Continual SFT Matches Multimodal RLHF with Negative Supervision
Ke Zhu, Yu Wang, Yanpeng Sun +4
Multimodal RLHF usually happens after supervised finetuning (SFT) stage to continually improve vision-language models' (VLMs) comprehension. Conventional wisdom holds its superiori…