Search Results for author:
Found 8 papers, 4 papers with code
Compositional Physical Reasoning of Objects and Events from Videos
The model is evaluated based on its capability to unravel the compositional hidden properties, such as mass and charge, and use this knowledge to answer a set of questions.
M3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability Benchmark
As recent multi-modality large language models (MLLMs) have shown formidable proficiency on various complex tasks, there has been increasing attention on debating whether these models could eventually mirror human intelligence.
ComPhy: Compositional Physical Reasoning of Objects and Events from Videos
In this paper, we take an initial step to highlight the importance of inferring the hidden physical properties not directly observable from visual appearances, by introducing the Compositional Physical Reasoning (ComPhy) dataset.
Immersive Text Game and Personality Classification
We designed and built a game called \textit{Immersive Text Game}, which allows the player to choose a story and a character, and interact with other characters in the story in an immersive manner of dialogues.
Visual Grounding of Learned Physical Models
The abilities to perform physical reasoning and to adapt to new environments, while intrinsic to humans, remain challenging to state-of-the-art computational models.
CLEVRER: CoLlision Events for Video REpresentation and Reasoning
While these models thrive on the perception-based task (descriptive), they perform poorly on the causal tasks (explanatory, predictive and counterfactual), suggesting that a principled approach for causal reasoning should incorporate the capability of both perceiving complex visual and language inputs, and understanding the underlying dynamics and causal relations.
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
Second, the model is more data- and memory-efficient: it performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering.
Ranked #1 on
Visual Question Answering (VQA)
on CLEVR
Roll-back Hamiltonian Monte Carlo
We propose a new framework for Hamiltonian Monte Carlo (HMC) on truncated probability distributions with smooth underlying density functions.
