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A curated collection of research papers on Large Language Models (LLMs), transformers, prompting, fine-tuning, and multi-modal systems. Maintained by Sean Moran.

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Embodied Question Answering

Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, Dhruv Batra . 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2018 – 371 citations

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Agentic CVPR Evaluation Reinforcement Learning

We present a new AI task – Embodied Question Answering (EmbodiedQA) – where an agent is spawned at a random location in a 3D environment and asked a question (“What color is the car?”). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question (“orange”). This challenging task requires a range of AI skills – active perception, language understanding, goal-driven navigation, commonsense reasoning, and grounding of language into actions. In this work, we develop the environments, end-to-end-trained reinforcement learning agents, and evaluation protocols for EmbodiedQA.

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