Search Results for author:
Found 20 papers, 7 papers with code
Towards Socially Intelligent Agents with Mental State Transition and Human Value
One of which is to track the agent’s mental state transition and teach the agent to make decisions guided by its value like a human.
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes
To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes.
OpenD: A Benchmark for Language-Driven Door and Drawer Opening
We introduce OPEND, a benchmark for learning how to use a hand to open cabinet doors or drawers in a photo-realistic and physics-reliable simulation environment driven by language instruction.
Alignment-guided Temporal Attention for Video Action Recognition
Temporal modeling is crucial for various video learning tasks.
VRKitchen2.0-IndoorKit: A Tutorial for Augmented Indoor Scene Building in Omniverse
With the recent progress of simulations by 3D modeling software and game engines, many researchers have focused on Embodied AI tasks in the virtual environment.
Triangular Character Animation Sampling with Motion, Emotion, and Relation
We propose a Spatial-Temporal And-Or graph (ST-AOG), a stochastic grammar model, to encode the contextual relationship between motion, emotion, and relation, forming a triangle in a conditional random field.
Learning to Act with Affordance-Aware Multimodal Neural SLAM
With the proposed Affordance-aware Multimodal Neural SLAM (AMSLAM) approach, we obtain more than 40% improvement over prior published work on the ALFRED benchmark and set a new state-of-the-art generalization performance at a success rate of 23. 48% on the test unseen scenes.
ValueNet: A New Dataset for Human Value Driven Dialogue System
To the best of our knowledge, ValueNet is the first large-scale text dataset for human value modeling, and we are the first one trying to incorporate a value model into emotionally intelligent dialogue systems.
Learning from the Tangram to Solve Mini Visual Tasks
Current pre-training methods in computer vision focus on natural images in the daily-life context.
LUMINOUS: Indoor Scene Generation for Embodied AI Challenges
However, current simulators for Embodied AI (EAI) challenges only provide simulated indoor scenes with a limited number of layouts.
IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning
Also, we develop a strong IconQA baseline Patch-TRM that applies a pyramid cross-modal Transformer with input diagram embeddings pre-trained on the icon dataset.
Ranked #1 on
Visual Question Answering (VQA)
on IconQA
STAR: Sparse Transformer-based Action Recognition
The cognitive system for human action and behavior has evolved into a deep learning regime, and especially the advent of Graph Convolution Networks has transformed the field in recent years.
SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues
Inferring social relations from dialogues is vital for building emotionally intelligent robots to interpret human language better and act accordingly.
Ranked #5 on
Dialog Relation Extraction
on DialogRE
Towards Socially Intelligent Agents with Mental State Transition and Human Utility
One of which is to track the agent's mental state transition and teach the agent to make decisions guided by its value like a human.
Information Theoretic Secure Aggregation with User Dropouts
The identity of the dropped users is not known a priori and the server needs to securely recover the sum of the remaining surviving users.
Weighted Entropy Modification for Soft Actor-Critic
We generalize the existing principle of the maximum Shannon entropy in reinforcement learning (RL) to weighted entropy by characterizing the state-action pairs with some qualitative weights, which can be connected with prior knowledge, experience replay, and evolution process of the policy.
Structured Attention for Unsupervised Dialogue Structure Induction
Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics.
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state.
Bayesian Inference
Explainable Artificial Intelligence (XAI)
+1
TWIN GRAPH CONVOLUTIONAL NETWORKS: GCN WITH DUAL GRAPH SUPPORT FOR SEMI-SUPERVISED LEARNING
Graph Neural Networks as a combination of Graph Signal Processing and Deep Convolutional Networks shows great power in pattern recognition in non-Euclidean domains.
