What is the impact of multimodal pre-training (e.g., using audio-visual data) on the downstream task performan

Compressed videos offer a compelling alternative to raw videos, showing the possibility to significantly reduce the on-line computational and storage cost. However, current approaches to compressed video processing generally follow the resource-consuming pre-training and fine-tuning paradigm, which does not fully take advantage of such properties, making them not favorable enough for widespread applications. Inspired by recent successes of prompt tuning techniques in computer vision, this paper presents the first attempt to build a prompt based representation learning framework, which enables

Research goal: What is the impact of multimodal pre-training (e.g., using audio-visual data) on the downstream task performance of CLAM models compared to unimodal pre-training, as measured by success rates on the BridgeData V2 benchmark?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.8/10.