3 accepted papers
Current vision-language models (VLMs) understand complex vision-text tasks by extracting overall semantic information from large-scale cross-modal associations. However, extracting from large-scale cross-modal associations often smooths out semantic details and requires large computations, limiting…
Low-rank adaptation (LoRA) efficiently adapts LLMs to downstream tasks by decomposing LLMs’ weight update into trainable low-rank matrices for fine-tuning. However, the random low-rank matrices may introduce massive task-irrelevant information, while their recomposed form suffer from limited represe
Face aging is a typical ill-posed problem influenced by various factors such as environment and genetics, leading to highly diverse outcomes. However, existing methods primarily rely on numerical age representations, making it difficult to accurately capture individual or group-level aging patterns.…