Jongwoo Park

CS Ph.D. candidate @SBU | Working on VLM, VLA, 3D Det. | Prev. Career: Intern @AWS x 2 @Samsung, Startup Co-founder | Prev. Education @ UPenn, UC Bekeley

[Jongwoo Park @jongwoopark7978]

Excited to share our poster schedule for 🤖IVRA at ICRA 2026 in Vienna

📍 Thu, June 4, 15:00-16:30, Hall C, ThI2I.318

A lightweight, training-free method that improves 🧠📈 spatial understanding in VLA with affinity hint

🎥 More demo: https://t.co/WPyPIuDvcK

More details ⬇️

[Jongwoo Park @jongwoopark7978]

Excited to share our poster schedule for 🎞️LVNet at EACL 2026 in Morocco

📍 March 27, 9:00-10:30 AM, Poster Hall, Session 10

LVNet is a Training-free keyframe selector for long-video QA

Paper: https://t.co/wvrtJ2jmSc

Demo: LVNet (top) vs VideoTree (bottom)

[Jongwoo Park @jongwoopark7978]

🎉Congrats to the team: @kahnchana @jjh6297 Cristina Mata, Yoo Sung Jang, @ryoo_michael

[Jongwoo Park @jongwoopark7978]

IVRA is accepted to #ICRA2026 🎉

A lightweight, training-free method that improves 🧠📈 spatial understanding in VLA models using affinity hints already inside the vision encoder—no external encoder.

🎥 More demo: https://t.co/WPyPIuDvcK

More details ⬇️

[Kanchana Ranasinghe @kahnchana]

[CVPR 2026] FOFPred has been accepted to #CVPR2026 (Findings)!

We build a diffusion-based model that predicts Future Optical Flow from a single image guided by natural language instructions.

Checkout code, model ckpt, & live demo at:
https://t.co/tCFSYWlSNr

[Jongwoo Park @jongwoopark7978]

Code + more demos: https://t.co/SMSd6ZuucH

Congrats to the team!: @kahnchana , @kkahatapitiy , Wonjeong Ryu, Donghyun Kim, @ryoo_michael

[Jongwoo Park @jongwoopark7978]

LVNet accepted to #EACL26! Training-free keyframe selector for long-video QA: 🎯High accuracy low caption,⚡up to 3.4x speed, ⚙️filters 1,800 to 24 keyframes on 1 GPU,💸10x cheaper LLM cost.

Paper: https://t.co/wvrtJ2jmSc

More details in the thread. ⬇️

Demo: LVNet (top)

[Xiang Li @XiangLi54505720]

(1/5)
Excited to present our #ICLR2025 paper, LLaRA, at NYC CV Day!
LLaRA efficiently transforms a pretrained Vision-Language Model (VLM) into a robot Vision-Language-Action (VLA) policy, even with a limited amount of training data.
More details are in the thread. ⬇️

[TwelveLabs (twelvelabs.io) @twelve_labs]

The webinar recording of this session with @jongwoopark7978, @kkahatapitiy, and @kahnchana is up!

Watch here: https://t.co/X4wmcgai6W 📺

All three talks focused on efficient multimodal LLMs for long videos: Visual keyframes, Effective captions, and Fast inference. Enjoy!

[Jongwoo Park @jongwoopark7978]

@XiangLi54505720 Thanks for all your effort Xiang!

[Jongwoo Park @jongwoopark7978]

🚀 Check out our new arXiv release!

We've demonstrated the effectiveness of the Hierarchical Keyframe Selector for very long-form VQA. The model processes video in three stages.

Explore our work and code here: https://t.co/SMSd6Zv22f

[Xiang Li @XiangLi54505720]

(6/N) We ran multiple types of real-world robot experiments and found that our method, trained on just 8k simulated data, performs strongly in unseen real-world settings. With minimal in-domain fine-tuning, the model achieves a 91.6% average success rate!

[AK @_akhaliq]

Token Turing Machines
abs: https://t.co/BhQJhDQBmB

[AK @_akhaliq]

Grafting Vision Transformers
abs: https://t.co/Kt8hYrKZ7D