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20242026
most citedLook Every Frame All at Once: Video-Mamba for Efficient Long-form Video Understanding with Multi-Axis Gradient Checkpointing

1 citations · 1 across the 3 of their papers we have counts for

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cs.CV2026

Generating Humanless Environment Walkthroughs from Egocentric Walking Tour Videos

Yujin Ham, Junho Kim, Vivek Boominathan +1

Egocentric "walking tour" videos provide a rich source of image data to develop rich and diverse visual models of environments around the world. However, the significant presence o…

cs.CV2026

STRIDE: When to Speak Meets Sequence Denoising for Streaming Video Understanding

Junho Kim, Hosu Lee, James M. Rehg +2

Recent progress in video large language models (Video-LLMs) has enabled strong offline reasoning over long and complex videos. However, real-world deployments increasingly require…

cs.CV2025

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…

cs.CV20241 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…