Search Results for author:
Found 4 papers, 3 papers with code
Homomorphism Counts for Graph Neural Networks: All About That Basis
A large body of work has investigated the properties of graph neural networks and identified several limitations, particularly pertaining to their expressive power.
Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI
We present Mini-BEHAVIOR, a novel benchmark for embodied AI that challenges agents to use reasoning and decision-making skills to solve complex activities that resemble everyday human challenges.
Modeling Dynamic Environments with Scene Graph Memory
We evaluate our method in the Dynamic House Simulator, a new benchmark that creates diverse dynamic graphs following the semantic patterns typically seen at homes, and show that NEP can be trained to predict the locations of objects in a variety of environments with diverse object movement dynamics, outperforming baselines both in terms of new scene adaptability and overall accuracy.
MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing
Video-language models (VLMs), large models pre-trained on numerous but noisy video-text pairs from the internet, have revolutionized activity recognition through their remarkable generalization and open-vocabulary capabilities.
Ranked #2 on
Few Shot Action Recognition
on MOMA-LRG
(using extra training data)
