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Found 12 papers, 2 papers with code
FollowMe: a Robust Person Following Framework Based on Re-Identification and Gestures
Human-robot interaction (HRI) has become a crucial enabler in houses and industries for facilitating operational flexibility.
CARPE-ID: Continuously Adaptable Re-identification for Personalized Robot Assistance
In today's Human-Robot Interaction (HRI) scenarios, a prevailing tendency exists to assume that the robot shall cooperate with the closest individual or that the scene involves merely a singular human actor.
Artifacts Mapping: Multi-Modal Semantic Mapping for Object Detection and 3D Localization
To further explore this direction, we propose a framework that can autonomously detect and localize predefined objects in a known environment using a multi-modal sensor fusion approach (combining RGB and depth data from an RGB-D camera and a lidar).
Compact CNN Structure Learning by Knowledge Distillation
The concept of compressing deep Convolutional Neural Networks (CNNs) is essential to use limited computation, power, and memory resources on embedded devices.
Explainable Deep Classification Models for Domain Generalization
Explanations are defined as regions of visual evidence upon which a deep classification network makes a decision.
Guided Zoom: Questioning Network Evidence for Fine-grained Classification
We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions.
Excitation Dropout: Encouraging Plasticity in Deep Neural Networks
In this work, we utilize the evidence at each neuron to determine the probability of dropout, rather than dropping out neurons uniformly at random as in standard dropout.
Excitation Backprop for RNNs
Models are trained to caption or classify activity in videos, but little is known about the evidence used to make such decisions.
What Will I Do Next? The Intention from Motion Experiment
In this paper, we bridge cognitive and computer vision studies, by demonstrating the effectiveness of video-based approaches for the prediction of human intentions.
Predicting Human Intentions from Motion Only: A 2D+3D Fusion Approach
In this paper, we address the new problem of the prediction of human intents.
Revisiting Human Action Recognition: Personalization vs. Generalization
In particular, considering that each human action in the datasets is performed several times by different subjects, we were able to precisely quantify the effect of inter- and intra-subject variability, so as to figure out the impact of several learning approaches in terms of classification performance.
Kernelized Covariance for Action Recognition
In this paper we aim at increasing the descriptive power of the covariance matrix, limited in capturing linear mutual dependencies between variables only.
