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Found 19 papers, 0 papers with code
Computer Vision for Primate Behavior Analysis in the Wild
With this perspective paper, we want to contribute towards closing this gap, by guiding behavioral scientists in what can be expected from current methods and steering computer vision researchers towards problems that are relevant to advance research in animal behavior.
Multi Sentence Description of Complex Manipulation Action Videos
Automatic video description requires the generation of natural language statements about the actions, events, and objects in the video.
A Hierarchical Graph-based Approach for Recognition and Description Generation of Bimanual Actions in Videos
To achieve this, we encode, first, the spatio-temporal inter dependencies between objects and actions with scene graphs and we combine this, in a second step, with a novel 3-level architecture creating a hierarchical attention mechanism using Graph Attention Networks (GATs).
Infant movement classification through pressure distribution analysis
Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we proposed an innovative non-intrusive approach using a pressure sensing device to classify infant general movements (GMs).
Open video data sharing in developmental and behavioural science
Video data has rarely been shared due to ethical concerns of confidentiality, although the need of shared large-scaled datasets remains increasing.
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects
We propose a method for 3D object reconstruction and 6D-pose estimation from 2D images that uses knowledge about object shape as the primary key.
Combining Optimal Path Search With Task-Dependent Learning in a Neural Network
The neural network has the same algorithmic complexity as Bellman-Ford and, in addition, we can show that network learning mechanisms (such as Hebbian learning) can adapt the weights in the network augmenting the resulting paths according to some task at hand.
Bootstrapping Concept Formation in Small Neural Networks
We argue that, first, Concepts are formed as closed representations, which are then consolidated by relating them to each other.
Human and Machine Action Prediction Independent of Object Information
In spite of these constraints, our results show the subjects were able to predict actions in, on average, less than 64% of the action's duration.
Semantic Image Search for Robotic Applications
Generalization in robotics is one of the most important problems.
One-shot path planning for multi-agent systems using fully convolutional neural network
In contrast to that, we propose a novel method by utilising fully convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i. e., with a single prediction step.
Generation of Paths in a Maze using a Deep Network without Learning
Trajectory- or path-planning is a fundamental issue in a wide variety of applications.
Attention on Abstract Visual Reasoning
Our proposed hybrid model, represents an alternative on learning abstract relations using self-attention and demonstrates that the Transformer network is also well suited for abstract visual reasoning.
Action Prediction in Humans and Robots
To achieve prediction, actions can be encoded by a series of events, where every event corresponds to a change in a (static or dynamic) relation between some of the objects in a scene.
Symbol Emergence in Cognitive Developmental Systems: a Survey
However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered.
Learning to Label Affordances from Simulated and Real Data
An autonomous robot should be able to evaluate the affordances that are offered by a given situation.
Transfer entropy-based feedback improves performance in artificial neural networks
The structure of the majority of modern deep neural networks is characterized by uni- directional feed-forward connectivity across a very large number of layers.
Distributed Recurrent Neural Forward Models with Synaptic Adaptation for Complex Behaviors of Walking Robots
The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc.
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
To address this problem, we extend the single chaotic CPG to multiple CPGs with learning.
