5 accepted papers
Multi-task test-time adaptation (MT-TTA) aims to adapt pre-trained models to dynamic environments during multi-task inference by leveraging unlabeled test data. This task is particularly challenging as different tasks respond divergently to distribution shifts, and mixed input streams containing bot
Recent advancements in text-guided image editing have achieved notable success by leveraging natural language prompts for fine-grained semantic control. However, certain editing semantics are challenging to specify precisely using textual descriptions alone. A practical alternative involves learning…
External knowledge provides critical clues for knowledge-based visual question answering (KB-VQA), while the implicit knowledge in images is difficult to capture in order to construct effective queries for knowledge bases. To this end, we propose a visual entity-centric prompting for knowledge retri…
Continuum robots and soft robots have shown great potential in industrial and medical applications. Sensing the shapes of continuum robots is a challenging but significant problem for enhancing their performance during various tasks. In this letter, we present a novel method to estimate the shapes o
Transparent objects with unique visual properties often make depth cameras fail to scan their reflective and refractive surfaces. Recent studies on depth completion of transparent objects have leveraged a linear system based on the geometric constraints to predict the missing depth, which is hard to…