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Published on Aug 25 · Submitted by Muhammad Asad Ali on Aug 26 Abstract MoTE replaces dense decoder feed-forward networks with task-specific experts routed by sample-level task labels, improving multi-task video-language accuracy with sparse, interpretable computation. Procedural video-language models must solve heterogeneous tasks from the same visual evidence, including action recognition, forecasting, and procedure prediction.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
