4 accepted papers
Achieving robust performance in vision-language tasks requires strong multimodal alignment, where textual and visual data interact seamlessly. Existing frameworks often combine contrastive learning with image captioning to unify visual and textual representations. However, reliance on global represe…
"Catastrophic overfitting (CO) poses a significant challenge to fast adversarial training (FastAT), particularly at large perturbation scales, leading to dramatic reductions in adversarial test accuracy. Our analysis of existing FastAT methods shows that CO is accompanied by abrupt and irregular flu…
The dot product self-attention (DPSA) is a fundamental component of transformers. However, scaling them to long sequences, like documents or high-resolution images, becomes prohibitively expensive due to the quadratic time and memory complexities arising from the softmax operation. Kernel methods ar…
Given the recent advances with image-generating algorithms, deep image completion methods have made significant progress. However, state-of-art methods typically provide poor cross-scene generalization, and generated masked areas often contain blurry artifacts. Predictive filtering is a method for r…