Search Results for author: Cheston Tan

Found 30 papers, 13 papers with code

CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening

1 code implementation29 Mar 2024 Hei Yi Mak, Flint Xiaofeng Fan, Luca A. Lanzendörfer, Cheston Tan, Wei Tsang Ooi, Roger Wattenhofer

CAESAR is an aggregation strategy used by the server that combines convergence-aware sampling with a screening mechanism.

Can LLMs perform structured graph reasoning?

1 code implementation2 Feb 2024 Palaash Agrawal, Shavak Vasania, Cheston Tan

We highlight various limitations, biases and properties of LLMs through this benchmarking process, such as an inverse relation to the average degrees of freedom of traversal per node in graphs, the overall negative impact of k-shot prompting on graph reasoning tasks, and a positive response bias which prevents LLMs from identifying the absence of a valid solution.

Benchmarking Navigate +1

Advancing Perception in Artificial Intelligence through Principles of Cognitive Science

no code implementations13 Oct 2023 Palaash Agrawal, Cheston Tan, Heena Rathore

In this review paper, we focus on the cognitive functions of perception, which is the process of taking signals from one's surroundings as input, and processing them to understand the environment.

STUPD: A Synthetic Dataset for Spatial and Temporal Relation Reasoning

1 code implementation13 Sep 2023 Palaash Agrawal, Haidi Azaman, Cheston Tan

In addition to spatial relations, we also propose 50K visual depictions across 10 temporal relations, consisting of videos depicting event/time-point interactions.

Relation Relationship Detection +1

Compositional Learning of Visually-Grounded Concepts Using Reinforcement

1 code implementation8 Sep 2023 Zijun Lin, Haidi Azaman, M Ganesh Kumar, Cheston Tan

First, we explore if agents can perform compositional learning, and whether they can leverage on frozen text encoders (e. g.

Navigate reinforcement-learning +1

DetermiNet: A Large-Scale Diagnostic Dataset for Complex Visually-Grounded Referencing using Determiners

1 code implementation ICCV 2023 Clarence Lee, M Ganesh Kumar, Cheston Tan

We find that current state-of-the-art visual grounding models do not perform well on the dataset, highlighting the limitations of existing models on reference and quantification tasks.

Visual Grounding

Revealing the Illusion of Joint Multimodal Understanding in VideoQA Models

no code implementations15 Jun 2023 Ishaan Singh Rawal, Shantanu Jaiswal, Basura Fernando, Cheston Tan

We evaluate models on CLAVI and find that all models achieve high performance on multimodal shortcut instances, but most of them have poor performance on the counterfactual instances that necessitate joint multimodal understanding.

Benchmarking counterfactual

Robustness of Utilizing Feedback in Embodied Visual Navigation

no code implementations6 Mar 2023 Jenny Zhang, Samson Yu, Jiafei Duan, Cheston Tan

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 field of view.

Object Visual Navigation

FedHQL: Federated Heterogeneous Q-Learning

no code implementations26 Jan 2023 Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Cheston Tan, Bryan Kian Hsiang Low, Roger Wattenhofer

Federated Reinforcement Learning (FedRL) encourages distributed agents to learn collectively from each other's experience to improve their performance without exchanging their raw trajectories.

Q-Learning reinforcement-learning +1

Abductive Action Inference

no code implementations24 Oct 2022 Clement Tan, Chai Kiat Yeo, Cheston Tan, Basura Fernando

In this paper, we introduce a novel research task known as "abductive action inference" which addresses the question of which actions were executed by a human to reach a specific state shown in a single snapshot.

BOSS: A Benchmark for Human Belief Prediction in Object-context Scenarios

no code implementations21 Jun 2022 Jiafei Duan, Samson Yu, Nicholas Tan, Li Yi, Cheston Tan

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 information) exhibited during social interactions.


Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigation

1 code implementation20 Jun 2022 Jenny Zhang, Samson Yu, Jiafei Duan, Cheston Tan

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.

Visual Navigation

ABCDE: An Agent-Based Cognitive Development Environment

no code implementations10 Jun 2022 Jieyi Ye, Jiafei Duan, Samson Yu, Bihan Wen, Cheston Tan

How can the most common 1, 000 concepts (89\% of everyday use) be learnt in a naturalistic children's setting?

A Survey on Machine Learning Approaches for Modelling Intuitive Physics

no code implementations14 Feb 2022 Jiafei Duan, Arijit Dasgupta, Jason Fischer, Cheston Tan

Despite the wide range of work in physical reasoning for machine cognition, there is a scarcity of reviews that organize and group these deep learning approaches.

BIG-bench Machine Learning

TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs

1 code implementation26 Nov 2021 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.

feature selection Image Classification +3

A Benchmark for Modeling Violation-of-Expectation in Physical Reasoning Across Event Categories

no code implementations16 Nov 2021 Arijit Dasgupta, Jiafei Duan, Marcelo H. Ang Jr, Yi Lin, Su-hua Wang, Renée Baillargeon, Cheston Tan

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.

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee

2 code implementations NeurIPS 2021 Flint Xiaofeng Fan, Yining Ma, Zhongxiang Dai, Wei Jing, Cheston Tan, Bryan Kian Hsiang Low

The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better decision-making policy without sharing raw trajectories.

Decision Making Federated Learning +2

AVoE: A Synthetic 3D Dataset on Understanding Violation of Expectation for Artificial Cognition

1 code implementation12 Oct 2021 Arijit Dasgupta, Jiafei Duan, Marcelo H. Ang Jr, Cheston Tan

Recent work in cognitive reasoning and computer vision has engendered an increasing popularity for the Violation-of-Expectation (VoE) paradigm in synthetic datasets.

PIP: Physical Interaction Prediction via Mental Simulation with Span Selection

no code implementations10 Sep 2021 Jiafei Duan, Samson Yu, Soujanya Poria, Bihan Wen, Cheston Tan

However, there is a lack of intuitive physics models that are tested on long physical interaction sequences with multiple interactions among different objects.

Friction Semantic Object Interaction Classification

SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments

2 code implementations13 Aug 2021 Jiafei Duan, Samson Yu Bai Jian, Cheston Tan

We then further evaluate it with a state-of-the-art physics-based deep model and show that the SPACE dataset improves the learning of intuitive physics with an approach inspired by curriculum learning.

A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation

1 code implementation25 Jun 2021 M Ganesh Kumar, Cheston Tan, Camilo Libedinsky, Shih-Cheng Yen, Andrew Yong-Yi Tan

Biologically plausible classic actor-critic agents have been shown to learn to navigate to single reward locations, but which biologically plausible agents are able to learn multiple cue-reward location tasks has remained unclear.


One-shot learning of paired association navigation with biologically plausible schemas

2 code implementations7 Jun 2021 M Ganesh Kumar, Cheston Tan, Camilo Libedinsky, Shih-Cheng Yen, Andrew Yong-Yi Tan

But how schemas, conceptualized at Marr's computational level, correspond with neural implementations remains poorly understood, and a biologically plausible computational model of the rodent learning has not been demonstrated.

One-Shot Learning

A Survey of Embodied AI: From Simulators to Research Tasks

no code implementations8 Mar 2021 Jiafei Duan, Samson Yu, Hui Li Tan, Hongyuan Zhu, Cheston Tan

This paper aims to provide an encyclopedic survey for the field of embodied AI, from its simulators to its research.

Embodied Question Answering Question Answering +1

Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning

no code implementations11 Dec 2019 Tianying Wang, Hao Zhang, Wei Qi Toh, Hongyuan Zhu, Cheston Tan, Yan Wu, Yong liu, Wei Jing

The proposed method is able to efficiently generalize the previously learned task by model fusion to solve the environment adaptation problem.

reinforcement-learning Reinforcement Learning (RL)

6D Pose Estimation with Correlation Fusion

no code implementations24 Sep 2019 Yi Cheng, Hongyuan Zhu, Ying Sun, Cihan Acar, Wei Jing, Yan Wu, Liyuan Li, Cheston Tan, Joo-Hwee Lim

To our best knowledge, this is the first work to explore effective intra- and inter-modality fusion in 6D pose estimation.

6D Pose Estimation 6D Pose Estimation using RGB

An End-to-End Network for Generating Social Relationship Graphs

no code implementations CVPR 2019 Arushi Goel, Keng Teck Ma, Cheston Tan

Inferring the social context in a given visual scene not only involves recognizing objects, but also demands a more in-depth understanding of the relationships and attributes of the people involved.

Attribute Graph Generation +1

Deep Convolutional Networks are Hierarchical Kernel Machines

no code implementations5 Aug 2015 Fabio Anselmi, Lorenzo Rosasco, Cheston Tan, Tomaso Poggio

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 under a group.

Neural tuning size is a key factor underlying holistic face processing

no code implementations15 Jun 2014 Cheston Tan, Tomaso Poggio

The main aim of this work is to further the fundamental understanding of what causes the visual processing of faces to be different from that of objects.

Face Recognition

Neural representation of action sequences: how far can a simple snippet-matching model take us?

no code implementations NeurIPS 2013 Cheston Tan, Jedediah M. Singer, Thomas Serre, David Sheinberg, Tomaso Poggio

The macaque Superior Temporal Sulcus (STS) is a brain area that receives and integrates inputs from both the ventral and dorsal visual processing streams (thought to specialize in form and motion processing respectively).


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