Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users' virtual representati...
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The standard class-incremental continual learning setting assumes a set of tasks seen one after the other in a fixed and pre-defined order. This is not very realistic in federated learning environments where each clie...
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Although massive pre-trained vision-language models like CLIP show impressive generalization capabilities for many tasks, still it often remains necessary to fine-tune them for improved performance on specific dataset...
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Facial Action Units detection (FAUs) represents a fine-grained classification problem that involves identifying different units on the human face, as defined by the Facial Action Coding System. In this paper, we prese...
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Image anomaly detection aims to detect out-of-distribution instances. Most existing methods treat anomaly detection as an unsupervised task because anomalous training data and labels are usually scarce or unavailable....
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Task-free continual learning is the subfield of machine learning that focuses on learning online from a stream whose distribution changes continuously over time. In contrast, previous works evaluate task-free continua...
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Semi-Supervised Domain Adaptation (SSDA) aims to develop domain invariant models from scarcely labeled target domain in addition to the fully labeled source domain. Current SSDA works are often applied in conjunction ...
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Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more ...
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Despite the recent development of learning-based gaze estimation methods, most methods require one or more eye or face region crops as inputs and produce a gaze direction vector as output. Cropping results in a higher...
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We introduce Temporal consistency for Test-time adaptation (TempT), a novel method for test-time adaptation on videos through the use of temporal coherence of predictions across sequential frames as a self-supervision...
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