Search Results for author:
Found 6 papers, 3 papers with code
U-VAP: User-specified Visual Appearance Personalization via Decoupled Self Augmentation
Extensive experiments on various kinds of visual attributes with SOTA personalization methods show the ability of the proposed method to mimic target visual appearance in novel contexts, thus improving the controllability and flexibility of personalization.
CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models
Multimodal large language models (MLLMs) have demonstrated promising results in a variety of tasks that combine vision and language.
Adversarial Robust Memory-Based Continual Learner
Despite the remarkable advances that have been made in continual learning, the adversarial vulnerability of such methods has not been fully discussed.
Topology-Preserving Adversarial Training
Despite the effectiveness in improving the robustness of neural networks, adversarial training has suffered from the natural accuracy degradation problem, i. e., accuracy on natural samples has reduced significantly.
Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Models
However, existing visual instruction tuning methods only utilize image-language instruction data to align the language and image modalities, lacking a more fine-grained cross-modal alignment.
Progressive Domain Expansion Network for Single Domain Generalization
A series of strategies are introduced to guarantee the safety and effectiveness of the expanded domains.
