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Found 10 papers, 6 papers with code
Does Spatial Cognition Emerge in Frontier Models?
Results suggest that contemporary frontier models fall short of the spatial intelligence of animals, performing near chance level on a number of classic tests of animal cognition.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
SpotEM: Efficient Video Search for Episodic Memory
The goal in episodic memory (EM) is to search a long egocentric video to answer a natural language query (e. g., "where did I leave my purse?").
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
NaQ: Leveraging Narrations as Queries to Supervise Episodic Memory
Searching long egocentric videos with natural language queries (NLQ) has compelling applications in augmented reality and robotics, where a fluid index into everything that a person (agent) has seen before could augment human memory and surface relevant information on demand.
Habitat-Matterport 3D Semantics Dataset
The scale, quality, and diversity of object annotations far exceed those of prior datasets.
PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning
We propose Potential functions for ObjectGoal Navigation with Interaction-free learning (PONI), a modular approach that disentangles the skills of `where to look?'
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
Environment Predictive Coding for Visual Navigation
We introduce environment predictive coding, a self-supervised approach to learn environment-level representations for embodied agents.
Learning to Set Waypoints for Audio-Visual Navigation
In audio-visual navigation, an agent intelligently travels through a complex, unmapped 3D environment using both sights and sounds to find a sound source (e. g., a phone ringing in another room).
