Gedas Bertasius

This account is verified. Learn more

@gberta227

Assistant Professor at UNC, previously a postdoc at Meta AI, PhD from UPenn, video understanding, multimodal AI, a basketball enthusiast.

Chapel Hill, NC gedasbertasius.com Joined June 2020

1.5K Following 1.7K Followers 604 Posts

Latest posts 5

Pinned post

Gedas Bertasius @gberta227 Jun 17

Sharing my CVPR 2026 talk from the Vision for Intelligent Task Assistants workshop: "From Perception to Agency: The Cognitive Stack for Video Task Assistants."

It covers our SVI-Bench project (svi-bench.github.io) plus a video+robotics project we'll release soon.

w/ @YuluPan_00 @mmiemon @Han_Yi_724 @mars_su0311 @baiqil0203

gedasbertasius.com/talks

23:15

Gedas Bertasius @gberta227 Sep 4

Video models have become good at recognizing actions but still struggle to infer what people are trying to accomplish, why they chose a particular action, and what they will do next. We built SVI-Bench to measure this gap. We'll present it at #ECCV2026 next week. The video below gives a short overview of the work. I also wrote a short post on the broader research direction: gedasbertasius.com/post/from-reco…

4:54

Gedas Bertasius @gberta227 Aug 18

@eccvconf when will the conference schedule be posted?

Gedas Bertasius @gberta227 Jul 23

Our proposed VideoTreeSearch achieves state-of-the-art results across all grounded long-video QA benchmarks while also being more efficient than prior agentic video methods. We also show that we can achieve strong temporal grounding results even if we don't use any manually labeled long video data.

Check out Ce's thread below for more details.

Work with @cezhhh @YuluPan_00 @ZiyangW00 @mohitban47

Ce Zhang Verified account @cezhhh Jul 23

Excited to share our new work, “Searching Videos as Trees: Self-Correcting Agents for Grounded Long Video QA.”

Grounded LVQA requires a model to answer a question about a long video and localize the short interval supporting its answer. We introduce VideoTreeSearch (VTS), which organizes the video as an adaptive temporal tree and lets an agent search, backtrack, and revise its search path.

VTS brings strong gains on three grounded LVQA benchmarks and also transfers to general long-video QA.

(1/9)
View quoted post on X

Gedas Bertasius @gberta227 Jul 1

ICML 2026 is coming up, which means it's been 5 years since I presented TimeSformer at ICML 2021 (arxiv.org/pdf/2102.05095). It turned out to be the most cited paper I've published, and it's still the only ICML paper I have. A bit of the story behind it. 🧵👇