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Found 30 papers, 13 papers with code
Neural representation of action sequences: how far can a simple snippet-matching model take us?
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).
Neural tuning size is a key factor underlying holistic face processing
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.
Deep Convolutional Networks are Hierarchical Kernel Machines
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.
An End-to-End Network for Generating Social Relationship Graphs
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.
6D Pose Estimation with Correlation Fusion
To our best knowledge, this is the first work to explore effective intra- and inter-modality fusion in 6D pose estimation.
Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning
The proposed method is able to efficiently generalize the previously learned task by model fusion to solve the environment adaptation problem.
Actionet: An Interactive End-To-End Platform For Task-Based Data Collection And Augmentation In 3D Environment
The problem of task planning for artificial agents remains largely unsolved.
A Survey of Embodied AI: From Simulators to Research Tasks
This paper aims to provide an encyclopedic survey for the field of embodied AI, from its simulators to its research.
One-shot learning of paired association navigation with biologically plausible schemas
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.
A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation
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.
SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments
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.
PIP: Physical Interaction Prediction via Mental Simulation with Span Selection
However, there is a lack of intuitive physics models that are tested on long physical interaction sequences with multiple interactions among different objects.
Ranked #1 on
Semantic Object Interaction Classification
on SPACE
AVoE: A Synthetic 3D Dataset on Understanding Violation of Expectation for Artificial Cognition
Recent work in cognitive reasoning and computer vision has engendered an increasing popularity for the Violation-of-Expectation (VoE) paradigm in synthetic datasets.
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
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.
A Benchmark for Modeling Violation-of-Expectation in Physical Reasoning Across Event Categories
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.
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs
Attention modules for Convolutional Neural Networks (CNNs) are an effective method to enhance performance on multiple computer-vision tasks.
A Survey on Machine Learning Approaches for Modelling Intuitive Physics
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.
ABCDE: An Agent-Based Cognitive Development Environment
How can the most common 1, 000 concepts (89\% of everyday use) be learnt in a naturalistic children's setting?
Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigation
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.
BOSS: A Benchmark for Human Belief Prediction in Object-context Scenarios
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.
Abductive Action Inference
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.
FedHQL: Federated Heterogeneous Q-Learning
Federated Reinforcement Learning (FedRL) encourages distributed agents to learn collectively from each other's experience to improve their performance without exchanging their raw trajectories.
Robustness of Utilizing Feedback in Embodied Visual Navigation
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.
Revealing the Illusion of Joint Multimodal Understanding in VideoQA Models
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.
DetermiNet: A Large-Scale Diagnostic Dataset for Complex Visually-Grounded Referencing using Determiners
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.
Compositional Learning of Visually-Grounded Concepts Using Reinforcement
First, we explore if agents can perform compositional learning, and whether they can leverage on frozen text encoders (e. g.
STUPD: A Synthetic Dataset for Spatial and Temporal Relation Reasoning
In addition to spatial relations, we also propose 50K visual depictions across 10 temporal relations, consisting of videos depicting event/time-point interactions.
Advancing Perception in Artificial Intelligence through Principles of Cognitive Science
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.
Can LLMs perform structured graph reasoning?
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.
CAESAR: Enhancing Federated RL in Heterogeneous MDPs through Convergence-Aware Sampling with Screening
CAESAR is an aggregation strategy used by the server that combines convergence-aware sampling with a screening mechanism.
