26 citations · 100 across the 23 of their papers we have counts for
23 papers
Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion
Ishaan Singh Rawal, Alexander Matyasko, Shantanu Jaiswal +2
While VideoQA Transformer models demonstrate competitive performance on standard benchmarks, the reasons behind their success are not fully understood. Do these models capture the…
Read My Mind: A Multi-Modal Dataset for Human Belief Prediction
Jiafei Duan, Samson Yu, Nicholas Tan +2
Understanding human intentions is key to enabling effective and efficient human-robot interaction (HRI) in collaborative settings. To enable developments and evaluation of the abil…
Robustness of Utilizing Feedback in Embodied Visual Navigation
Jenny Zhang, Samson Yu, Jiafei Duan +1
This paper presents a framework for training an agent to actively request help in object-goal navigation tasks, with feedback indicating the location of the target object in its fi…
Inferring Past Human Actions in Homes with Abductive Reasoning
Clement Tan, Chai Kiat Yeo, Cheston Tan +1
Abductive reasoning aims to make the most likely inference for a given set of incomplete observations. In this paper, we introduce "Abductive Past Action Inference", a novel resear…
BOSS: A Benchmark for Human Belief Prediction in Object-context Scenarios
Jiafei Duan, Samson Yu, Nicholas Tan +2
Humans with an average level of social cognition can infer the beliefs of others based solely on the nonverbal communication signals (e.g. gaze, gesture, pose and contextual inform…
ABCDE: An Agent-Based Cognitive Development Environment
Jieyi Ye, Jiafei Duan, Samson Yu +2
Children's cognitive abilities are sometimes cited as AI benchmarks. How can the most common 1,000 concepts (89\% of everyday use) be learnt in a naturalistic children's setting? C…
Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigation
Jenny Zhang, Samson Yu, Jiafei Duan +1
In reality, it is often more efficient to ask for help than to search the entire space to find an object with an unknown location. We present a learning framework that enables an a…
A Survey on Machine Learning Approaches for Modelling Intuitive Physics
Jiafei Duan, Arijit Dasgupta, Jason Fischer +1
Research in cognitive science has provided extensive evidence of human cognitive ability in performing physical reasoning of objects from noisy perceptual inputs. Such a cognitive…
A Benchmark for Modeling Violation-of-Expectation in Physical Reasoning Across Event Categories
Arijit Dasgupta, Jiafei Duan, Marcelo H. Ang +4
Recent work in computer vision and cognitive reasoning has given rise to an increasing adoption of the Violation-of-Expectation (VoE) paradigm in synthetic datasets. Inspired by in…
AVoE: A Synthetic 3D Dataset on Understanding Violation of Expectation for Artificial Cognition
Arijit Dasgupta, Jiafei Duan, Marcelo H. Ang +1
Recent work in cognitive reasoning and computer vision has engendered an increasing popularity for the Violation-of-Expectation (VoE) paradigm in synthetic datasets. Inspired by wo…
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs
Shantanu Jaiswal, Basura Fernando, Cheston Tan
Attention modules for Convolutional Neural Networks (CNNs) are an effective method to enhance performance on multiple computer-vision tasks. While existing methods appropriately mo…
PIP: Physical Interaction Prediction via Mental Simulation with Span Selection
Jiafei Duan, Samson Yu, Soujanya Poria +2
Accurate prediction of physical interaction outcomes is a crucial component of human intelligence and is important for safe and efficient deployments of robots in the real world. W…
SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments
Jiafei Duan, Samson Yu Bai Jian, Cheston Tan
Recent advancements in deep learning, computer vision, and embodied AI have given rise to synthetic causal reasoning video datasets. These datasets facilitate the development of AI…
A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation
M Ganesh Kumar, Cheston Tan, Camilo Libedinsky +2
Navigation to multiple cued reward locations has been increasingly used to study rodent learning. Though deep reinforcement learning agents have been shown to be able to learn the…
One-shot learning of paired association navigation with biologically plausible schemas
M Ganesh Kumar, Cheston Tan, Camilo Libedinsky +2
After learning a multiple paired-association (MPA) navigation task, rodents can rapidly learn new cue-location associations after a single exposure, a phenomenon proposed to be fac…
A Survey of Embodied AI: From Simulators to Research Tasks
Jiafei Duan, Samson Yu, Hui Li Tan +2
There has been an emerging paradigm shift from the era of "internet AI" to "embodied AI", where AI algorithms and agents no longer learn from datasets of images, videos or text cur…
Actionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment
Jiafei Duan, Samson Yu, Hui Li Tan +1
The problem of task planning for artificial agents remains largely unsolved. While there has been increasing interest in data-driven approaches for the study of task planning for a…
Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning
Tianying Wang, Hao Zhang, Wei Qi Toh +5
Learning-based methods have been used to pro-gram robotic tasks in recent years. However, extensive training is usually required not only for the initial task learning but also for…
6D Pose Estimation with Correlation Fusion
Yi Cheng, Hongyuan Zhu, Ying Sun +6
6D object pose estimation is widely applied in robotic tasks such as grasping and manipulation. Prior methods using RGB-only images are vulnerable to heavy occlusion and poor illum…
An End-to-End Network for Generating Social Relationship Graphs
Arushi Goel, Keng Teck Ma, Cheston Tan
Socially-intelligent agents are of growing interest in artificial intelligence. To this end, we need systems that can understand social relationships in diverse social contexts. In…
Neurogenesis and multiple plasticity mechanisms enhance associative memory retrieval in a spiking network model of the hippocampus
Yansong Chua, Cheston Tan
Hippocampal CA3 is crucial for the formation of long-term associative memory. It has a heavily recurrent connectivity, and memories are thought to be stored as memory engrams in th…
Deep Convolutional Networks are Hierarchical Kernel Machines
Fabio Anselmi, Lorenzo Rosasco, Cheston Tan +1
In i-theory a typical layer of a hierarchical architecture consists of HW modules pooling the dot products of the inputs to the layer with the transformations of a few templates un…
Neural tuning size is a key factor underlying holistic face processing
Cheston Tan, Tomaso Poggio
Faces are a class of visual stimuli with unique significance, for a variety of reasons. They are ubiquitous throughout the course of a person's life, and face recognition is crucia…