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Found 4 papers, 1 papers with code
Augmentations vs Algorithms: What Works in Self-Supervised Learning
We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL).
VideoPrism: A Foundational Visual Encoder for Video Understanding
We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model.
VideoGLUE: Video General Understanding Evaluation of Foundation Models
We evaluate existing foundation models video understanding capabilities using a carefully designed experiment protocol consisting of three hallmark tasks (action recognition, temporal localization, and spatiotemporal localization), eight datasets well received by the community, and four adaptation methods tailoring a foundation model (FM) for a downstream task.
Leveraging Large Language Models in Conversational Recommender Systems
A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue.
