4 papers
Do LLMs Truly Benefit from Longer Context in Automatic Post-Editing?
Ahrii Kim, Seong-heum Kim
Automatic post-editing (APE) aims to refine machine translations by correcting residual errors. Although recent large language models (LLMs) demonstrate strong translation capabili…
DIP-R1: Deep Inspection and Perception with RL Looking Through and Understanding Complex Scenes
Sungjune Park, Hyunjun Kim, Junho Kim +2
MLLMs have demonstrated significant visual understanding capabilities, yet their fine-grained visual perception in complex real-world scenarios, such as densely crowded public area…
GTA-Crime: A Synthetic Dataset and Generation Framework for Fatal Violence Detection with Adversarial Snippet-Level Domain Adaptation
Seongho Kim, Sejong Ryu, Hyoukjun You +1
Recent advancements in video anomaly detection (VAD) have enabled identification of various criminal activities in surveillance videos, but detecting fatal incidents such as shooti…
Survey and Evaluation of Converging Architecture in LLMs based on Footsteps of Operations
Seongho Kim, Jihyun Moon, Juntaek Oh +2
The advent of the Attention mechanism and Transformer architecture enables contextually natural text generation and compresses the burden of processing entire source information in…