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20232026
most citedCODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models

2 citations · 4 across the 10 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CV2024★ 1 cited

Look Every Frame All at Once: Video-Mamba for Efficient Long-form Video Understanding with Multi-Axis Gradient Checkpointing

Hosu Lee, Junho Kim, Hyunjun Kim +1

With the growing scale and complexity of video data, efficiently processing long video sequences poses significant challenges due to the quadratic increase in memory and computatio…

cs.CV2024

SALOVA: Segment-Augmented Long Video Assistant for Targeted Retrieval and Routing in Long-Form Video Analysis

Junho Kim, Hyunjun Kim, Hosu Lee +1

Despite advances in Large Multi-modal Models, applying them to long and untrimmed video content remains challenging due to limitations in context length and substantial memory over…

cs.CV2024

Personalized Lip Reading: Adapting to Your Unique Lip Movements with Vision and Language

Jeong Hun Yeo, Chae Won Kim, Hyunjun Kim +4

Lip reading aims to predict spoken language by analyzing lip movements. Despite advancements in lip reading technologies, performance degrades when models are applied to unseen spe…

cs.CV2024★ 2 cited

CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models

Junho Kim, Hyunjun Kim, Yeonju Kim +1

Large Multi-modal Models (LMMs) have recently demonstrated remarkable abilities in visual context understanding and coherent response generation. However, alongside these advanceme…

cs.CV2024

Robust Pedestrian Detection via Constructing Versatile Pedestrian Knowledge Bank

Sungjune Park, Hyunjun Kim, Yong Man Ro

Pedestrian detection is a crucial field of computer vision research which can be adopted in various real-world applications (e.g., self-driving systems). However, despite noticeabl…