Department Publications

Test-time prompt tuning (TPT) has emerged as a promising technique for adapting large vision-language models (VLMs) to unseen tasks without relying on labeled data. However, the lack of dispersion between textual features can hurt calibration performance, which raises concerns about VLMs' reliability, trustworthiness, and safety. Current TPT approaches primarily focus on improving prompt calibration by either maximizing average textual feature dispersion or enforcing orthogonality constraints to encourage angular separation. However, these methods may not always have optimal angular separation between class-wise textual features, which implies overlooking the critical role of angular diversity. To address this, we propose A-TPT, a novel TPT framework that introduces angular diversity to encourage uniformity in the distribution of normalized textual features induced by corresponding learnable prompts …

The rapid evolution of Artificial Intelligence (AI) has revolutionized multimedia creation, particularly through Text-to-Image (T2I) diffusion models, which synthesize images from textual descriptions with impressive capabilities. Building on this, enhancing video GIF generation has become a promising frontier. However, the field of Text-to-Video (T2V) GIFs remains underexplored, with limited research addressing this specific application. This synthesis method expands creative expression and finds utility in education, marketing, and entertainment. The lack of focused work highlights both novelty and opportunity for impactful contributions. This study focuses on optimizing T2V GIF generation in terms of computational and parameter efficiency. The resulting model maintains a parameter count below 1 billion, enabling faster training, reduced inference time, lower memory usage, and compatibility with low-end hardware. Inspired …