2 citations · 2 across the 8 of their papers we have counts for
9 papers
When Does an Image Determine the Answer? Benchmarking Visual Answerability across Charts and Scenes
Sungguk Cha, Mintae Kim, Youngsub Han +2
Reliable visual question answering requires correct answers when evidence is sufficient and abstention when it is not. We introduce a benchmark that connects complete-question eval…
Do Current Retrievers Cover All the Evidence? A Controlled Study of Conjunctive Cross-Page Retrieval
Sungguk Cha, DongWook Kim, Mintae Kim +3
Finding a long document relevant to a multi-part request is not the same as establishing that it contains every requested piece of evidence. We study this gap for conjunctive docum…
Frame Entrepreneurs in an AI Agent Community: Concentrated Identity-Claim Production on Moltbook
Sungguk Cha, DongWook Kim
Frame-alignment and collective-identity theories explain how external events become public claims about a group's standing, vulnerability, rights, or obligations. Whether such mech…
ReinPool: Reinforcement Learning Pooling Multi-Vector Embeddings for Retrieval System
Sungguk Cha, DongWook Kim, Mintae Kim +3
Multi-vector embedding models have emerged as a powerful paradigm for document retrieval, preserving fine-grained visual and textual details through token-level representations. Ho…
Annotation-Free Reinforcement Learning Query Rewriting via Verifiable Search Reward
Sungguk Cha, DongWook Kim, Taeseung Hahn +3
Optimizing queries for Retrieval-Augmented Generation (RAG) systems poses a significant challenge, particularly across diverse modal indices. We introduce RL-QR, a novel annotation…
Visually Dehallucinative Instruction Generation: Know What You Don't Know
Sungguk Cha, Jusung Lee, Younghyun Lee +1
"When did the emperor Napoleon invented iPhone?" Such hallucination-inducing question is well known challenge in generative language modeling. In this study, we present an innovati…
Visual Question Answering Instruction: Unlocking Multimodal Large Language Model To Domain-Specific Visual Multitasks
Jusung Lee, Sungguk Cha, Younghyun Lee +1
Having revolutionized natural language processing (NLP) applications, large language models (LLMs) are expanding into the realm of multimodal inputs. Owing to their ability to inte…
Visually Dehallucinative Instruction Generation
Sungguk Cha, Jusung Lee, Younghyun Lee +1
In recent years, synthetic visual instructions by generative language model have demonstrated plausible text generation performance on the visual question-answering tasks. However,…
Zero-Shot Semantic Segmentation via Spatial and Multi-Scale Aware Visual Class Embedding
Sungguk Cha, Yooseung Wang
Fully supervised semantic segmentation technologies bring a paradigm shift in scene understanding. However, the burden of expensive labeling cost remains as a challenge. To solve t…