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Found 13 papers, 4 papers with code
What does CLIP know about peeling a banana?
Humans show an innate capability to identify tools to support specific actions.
The revenge of BiSeNet: Efficient Multi-Task Image Segmentation
Recent advancements in image segmentation have focused on enhancing the efficiency of the models to meet the demands of real-time applications, especially on edge devices.
A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives
Human comprehension of a video stream is naturally broad: in a few instants, we are able to understand what is happening, the relevance and relationship of objects, and forecast what will follow in the near future, everything all at once.
PEM: Prototype-based Efficient MaskFormer for Image Segmentation
To fill this gap, we propose Prototype-based Efficient MaskFormer (PEM), an efficient transformer-based architecture that can operate in multiple segmentation tasks.
Cross-Domain Transfer Learning with CoRTe: Consistent and Reliable Transfer from Black-Box to Lightweight Segmentation Model
Many practical applications require training of semantic segmentation models on unlabelled datasets and their execution on low-resource hardware.
Entropic Score metric: Decoupling Topology and Size in Training-free NAS
Neural Networks design is a complex and often daunting task, particularly for resource-constrained scenarios typical of mobile-sized models.
Graph learning in robotics: a survey
Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applications.
Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots
Soft robots are gaining popularity thanks to their intrinsic safety to contacts and adaptability.
Bringing Online Egocentric Action Recognition into the wild
To enable a safe and effective human-robot cooperation, it is crucial to develop models for the identification of human activities.
PoliTO-IIT-CINI Submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition
In this report, we describe the technical details of our submission to the EPIC-Kitchens-100 Unsupervised Domain Adaptation (UDA) Challenge in Action Recognition.
Online vs. Offline Adaptive Domain Randomization Benchmark
However, transferring the acquired knowledge to the real world can be challenging due to the reality gap.
FreeREA: Training-Free Evolution-based Architecture Search
In the last decade, most research in Machine Learning contributed to the improvement of existing models, with the aim of increasing the performance of neural networks for the solution of a variety of different tasks.
Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead
Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving.
