Joochan Kim

I'm a research intern at KIST in Seoul, mentored by Hwasup Lim and Taekgeun You, where I am working on Embodied AI, mostly focusing on Vision-Language-Action models.

I finished my master's degree at SNU, where I was advised by Byoung-Tak Zhang. During my master course, I did internship at A*STAR mentored by Haiyue Zhu. Also, I did my bachelor's degree at Yonsei University.

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Research

I'm interested in multimodal AI, generative AI, and data-center AI. Most of my research is about understanding video and images with language guidance, thereby developing embodied AI from internet AI. Some papers with highlight are papers with main contributions.

Ordinal bias leads action recognition models to over-rely on dominant action pairs, inflating performance and lacking true video comprehension even when challenged by action masking and sequence shuffling.

We propose Background-aware Moment Detection TRansformer (BM-DETR), which carefully adopts a contrastive approach for robust prediction. BM-DETR achieves state-of-the-art performance on various benchmarks while being highly efficient.

We propose the zero-shot Vision-and-Language Navigation with Collision Mitigation (VLN-CM), which takes low-level actions as an output while considering possible collisions.

We propose Continual Vision-and-Language Navigation (CVLN) paradigm along with two methods for CVLN: Perplexity Replay (PerpR) and Episodic Self-Replay (ESR).

We propose a self-supervised learning framework: Modal-specific Pseudo Query Generation Network (MPGN). First, MPGN selects candidate temporal moments via subtitle-based moment sampling.Then, it generates pseudo queries exploiting both visual and textual information from the selected temporal moments.

Miscellanea

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