3 accepted papers
Large language models (LLMs) have enabled the creation of multi-modal LLMs that exhibit strong comprehension of visual data such as images and videos. However, these models usually rely on extensive visual tokens from visual encoders, leading to high computational demands, which limits their applica…
We present PAVE, a framework for adapting pre-trained video large language models (Video-LLMs) to downstream tasks that incorporate side-channel signals, such as audio, camera pose, or high frame rate videos. PAVE introduces a lightweight adaptation strategy called "patching", which adds a small num…
The existing active learning methods select the samples by evaluating the sample's uncertainty or its effect on the diversity of labeled datasets based on different task-specific or model-specific criteria. In this paper, we propose the Influence Selection for Active Learning(ISAL) which selects the…