Object detection plays a critical role in various computer vision applications, encompassingdomains like autonomous vehicles, object tracking, and scene understanding. These applica-tions rely on detectors that generate bounding boxes around known object c ...
As an 'early alerting' sense, one of the primary tasks for the human visual system is to recognize distant objects. In the specific context of facial identification, this ecologically important task has received surprisingly little attention. Most studies ...
There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...
CEBRA is a machine-learning method that can be used to compress time series in a way that reveals otherwise hidden structures in the variability of the data. It excels at processing behavioural and neural data recorded simultaneously, and it can decode act ...
State-of-the-art face recognition systems require vast amounts of labeled training data. Given the priority of privacy in face recognition applications, the data is limited to celebrity web crawls, which have issues such as limited numbers of identities. O ...
In the past years, deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in both verification and identification scenarios. Despite the high accuracy, they are often criticized for lacking explainability. The ...
Despite the significant progress in recent years, deep face recognition is often treated as a "black box" and has been criticized for lacking explainability. It becomes increasingly important to understand the characteristics and decisions of deep face rec ...
Deepfakes - synthetic videos generated by machine learning models - are becoming increasingly sophisticated. While they have several positive use cases, their potential for harm is also high. Deepfake production involves input from multiple engineers, maki ...
In this paper, we propose and compare personalized models for Productive Engagement (PE) recognition. PE is defined as the level of engagement that maximizes learning. Previously, in the context of robot-mediated collaborative learning, a framework of prod ...
Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsup ...
NATURE PORTFOLIO2022
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.