Search Results for author: Jaewon Chu

Found 3 papers, 3 papers with code

vid-TLDR: Training Free Token merging for Light-weight Video Transformer

1 code implementation20 Mar 2024 Joonmyung Choi, Sanghyeok Lee, Jaewon Chu, Minhyuk Choi, Hyunwoo J. Kim

To tackle these issues, we propose training free token merging for lightweight video Transformer (vid-TLDR) that aims to enhance the efficiency of video Transformers by merging the background tokens without additional training.

Ranked #2 on Video Retrieval on SSv2-template retrieval (using extra training data)

Action Recognition Computational Efficiency +5

NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA

1 code implementation NeurIPS 2023 Hyeong Kyu Choi, Seunghun Lee, Jaewon Chu, Hyunwoo J. Kim

Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves retrieving nodes from a knowledge graph (KG) to answer natural language questions.

Graph Question Answering Proper Noun +1

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