6 accepted papers
While adversarial attacks can effectively deceive deep neural networks, their real-world applicability is often limited by complex and conspicuous patterns that reveal their attack intent to human observers. To overcome this limitation, we propose UYE, a novel camouflage framework designed to simult
Adversarial attacks pose a significant challenge in deep learning, as carefully crafted perturbations can severely degrade even the most advanced models. In real-world scenarios, where the target models are often unknown, previous works often focus on creating adversarial patterns for specific known…
Existing fine-tuning paradigms are predominantly characterized by Full Parameter Tuning (FPT) and Parameter-Efficient Tuning (PET). FPT fine-tunes all parameters of a pre-trained model on downstream tasks, whereas PET freezes the pre-trained model and employs only a minimal number of learnable param…
Understanding student behavior in educational settings is critical in improving both the quality of pedagogy and the level of student engagement. While various AI-based models exist for classroom analysis, they tend to specialize in limited tasks and lack generalizability across diverse educational…
With the success of pre-trained visual-language (VL) models such as CLIP in visual representation tasks, transferring pre-trained models to downstream tasks has become a crucial paradigm. Recently, the prompt tuning paradigm, which draws inspiration from natural language processing (NLP), has made s…
Scene editing has attracted increasing research interests owing to its valuable applications in the field of photography and entertainment. With style-based GAN being proposed, images can be reasonably edited on specific semantics by manipulating in latent space of the generator. However, existing d…