Papers by Junsung Park
Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach (2024.acl-long)
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| Challenge: | primarily addressed in text-to-image retrieval task using dialogue-form context query . conventionally, text-based retrieval methods rely on initial text descriptions . |
| Approach: | They propose a plug-based retrieval method that uses large language models as questioners to generate non-redundant questions about the attributes of the target image. |
| Outcome: | The proposed method performs better than zero-shot and fine-tuned baselines in benchmarks. |
Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context (2025.findings-naacl)
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| Challenge: | Multi-hop reasoning requires multi-step reasoning based on supporting documents within a given context. |
| Approach: | They propose a method that prompts the model by repeatedly presenting the context. |
| Outcome: | The proposed method improves the F1 score by 30%p on multi-hop QA tasks and increases accuracy by 70%p on a synthetic task. |
3D-Aware Vision-Language Models Fine-Tuning with Geometric Distillation (2025.findings-emnlp)
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| Challenge: | Vision-Language Models (VLMs) have shown remarkable performance on diverse visual and linguistic tasks, yet they remain limited in their understanding of 3D spatial structures. |
| Approach: | They propose a framework that injects human-inspired geometric cues into pretrained VLMs . they use sparse correspondences, relative depth relations and dense cost volumes . |
| Outcome: | The proposed framework outperforms existing methods on vision-language reasoning and 3D perception benchmarks. |