Categories: FAANG

MM-Ego: Towards Building Egocentric Multimodal LLMs

This research aims to comprehensively explore building a multimodal foundation model for egocentric video understanding. To achieve this goal, we work on three fronts. First, as there is a lack of QA data for egocentric video understanding, we automatically generate 7M high-quality QA samples for egocentric videos ranging from 30 seconds to one hour long in Ego4D based on human-annotated data. This is one of the largest egocentric QA datasets. Second, we contribute a challenging egocentric QA benchmark with 629 videos and 7,026 questions to evaluate the models’ ability in recognizing and…
AI Generated Robotic Content

Recent Posts

3 Nuclear Startups Hit a Big Milestone. Why It Matters—and Why It Doesn’t

The companies’ Fourth of July plans include celebrating new reactor designs coming online. But there’s…

17 hours ago

Context vs. Memory Engineering in Agentic AI Systems

Compression on Arrival Tool outputs should be compressed after a call returns, not after the…

2 days ago

Why I disappeared for 3 Months & What’s Next

I’ve been quiet since November because I’ve been building.Over the past few months, AI has…

2 days ago

Multi-Agent Teams Hold Experts Back

Multi-agent LLM systems are increasingly deployed as autonomous collaborators, where agents interact freely rather than…

2 days ago

Managing Elasticsearch Reindex at Scale: Performance, Reliability, and Observability

Editor’s Note: This is the fourth post in a series exploring how Palantir customizes infrastructure…

2 days ago

GenPage: Towards End-to-End Generative Homepage Construction at Netflix

Authors: Lequn Wang, Jiangwei Pan, and Linas BaltrunasFigure 1. Autoregressive homepage generation. GenPage builds a…

2 days ago