2 citations · 4 across the 10 of their papers we have counts for
10 papers
CS-CLIP: Compositional Scene Graph-guided CLIP for Robust Compositional Reasoning
SeongJun Jeong, Minjoon Jung, Woo Suk Choi +2
Vision-language models (VLMs) demonstrate strong performance across compositional reasoning benchmarks, which require reasoning over semantic perturbations of objects, attributes,…
EvoGround: Self-Evolving Video Agents for Video Temporal Grounding
Minjoon Jung, Byoung-Tak Zhang, Lorenzo Torresani
Video temporal grounding (VTG) takes an untrimmed video and a natural-language query as input and localizes the temporal moment that best matches the query. Existing methods rely o…
EgoExo-Con: Exploring View-Invariant Video Temporal Understanding
Minjoon Jung, Junbin Xiao, Junghyun Kim +2
Do Video-LLMs have consistent temporal understanding when videos capture the same event from different viewpoints? To study this question, we introduce EgoExo-Con(sistency), a benc…
Confidence-guided Refinement Reasoning for Zero-shot Question Answering
Youwon Jang, Woo Suk Choi, Minjoon Jung +2
We propose Confidence-guided Refinement Reasoning (C2R), a novel training-free framework applicable to question-answering (QA) tasks across text, image, and video domains. C2R stra…
Exploring Ordinal Bias in Action Recognition for Instructional Videos
Joochan Kim, Minjoon Jung, Byoung-Tak Zhang
Action recognition models have achieved promising results in understanding instructional videos. However, they often rely on dominant, dataset-specific action sequences rather than…
On the Consistency of Video Large Language Models in Temporal Comprehension
Minjoon Jung, Junbin Xiao, Byoung-Tak Zhang +1
Video large language models (Video-LLMs) can temporally ground language queries and retrieve video moments. Yet, such temporal comprehension capabilities are neither well-studied n…
PGA: Personalizing Grasping Agents with Single Human-Robot Interaction
Junghyun Kim, Gi-Cheon Kang, Jaein Kim +3
Language-Conditioned Robotic Grasping (LCRG) aims to develop robots that comprehend and grasp objects based on natural language instructions. While the ability to understand person…
Background-aware Moment Detection for Video Moment Retrieval
Minjoon Jung, Youwon Jang, Seongho Choi +3
Video moment retrieval (VMR) identifies a specific moment in an untrimmed video for a given natural language query. This task is prone to suffer the weak alignment problem innate i…
Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval
Minjoon Jung, Seongho Choi, Joochan Kim +2
Video corpus moment retrieval (VCMR) is the task to retrieve the most relevant video moment from a large video corpus using a natural language query. For narrative videos, e.g., dr…
Toward a Human-Level Video Understanding Intelligence
Yu-Jung Heo, Minsu Lee, Seongho Choi +5
We aim to develop an AI agent that can watch video clips and have a conversation with human about the video story. Developing video understanding intelligence is a significantly ch…