Fine-tuning visual models has been widely shown promising performance on many
downstream visual tasks. With the surprising development of pre-trained visual foundation …

Text-to-motion generation is an emerging and challenging problem, which aims to
synthesize motion with the same semantics as the input text. However, due to the lack of …

Open-set fine-grained retrieval is an emerging challenge that requires an extra capability to
retrieve unknown subcategories during evaluation. However, current works are rooted in the …

Pre-trained vision transformers have strong representations benefit to various downstream
tasks. Recently many parameter-efficient fine-tuning (PEFT) methods have been proposed …

Open-set fine-grained retrieval is an emerging challenging task that allows to retrieve
unknown categories beyond the training set. The best solution for handling unknown …

Next-generation edge intelligence is anticipated to bring huge benefits to various
applications, eg, offloading systems. However, traditional deep offloading architectures face …

Recent advancements in remote sensing foundation models have unveiled their
tremendous potential in addressing Earth observation tasks. Presently, when large-scale …

Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable
generalization capabilities to downstream tasks. However, existing prompt tuning based …

Classifying fine-grained lesions is challenging due to minor and subtle differences in
medical images. This is because learning features of fine-grained lesions with highly minor …

Parameter-Efficient Transfer Learning (PETL) aims at efficiently adapting large models pre-
trained on massive data to downstream tasks with limited task-specific data. In view of the …