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Found 11 papers, 6 papers with code
DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation
Moreover, recognizing the variability across target domains, an Adaptive Refine Self-Matching (ARSM) method is also proposed to adjust the matching threshold and dynamically refine the prediction result with the self-matching method, enhancing accuracy.
An annotated grain kernel image database for visual quality inspection
We present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels.
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection
Cereal grain plays a crucial role in the human diet as a major source of essential nutrients.
Online Learning and Planning in Cognitive Hierarchies
Complex robot behaviour typically requires the integration of multiple robotic and Artificial Intelligence (AI) techniques and components.
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition
In this work, we propose to automatically design efficient 3D CNN architectures via a novel training-free neural architecture search approach tailored for 3D CNNs considering the model complexity.
Ranked #84 on
Action Recognition
on Something-Something V2
Multimodal Trajectory Prediction: A Survey
Trajectory prediction is an important task to support safe and intelligent behaviours in autonomous systems.
Graph-based Spatial Transformer with Memory Replay for Multi-future Pedestrian Trajectory Prediction
Pedestrian trajectory prediction is an essential and challenging task for a variety of real-life applications such as autonomous driving and robotic motion planning.
Explainability in Graph Neural Networks: An Experimental Survey
Graph neural networks (GNNs) have been extensively developed for graph representation learning in various application domains.
GrainSpace: A Large-scale Dataset for Fine-grained and Domain-adaptive Recognition of Cereal Grains
To the best of our knowledge, GrainSpace is the first publicly released dataset for cereal grain inspection.
Perceptual Context in Cognitive Hierarchies
Cognition does not only depend on bottom-up sensor feature abstraction, but also relies on contextual information being passed top-down.
