Current state-of-the-art trackers rely only on a target appearance model in order to localize
the object in each frame. Such approaches are however prone to fail in case of eg fast …

Domain adaptation assumes that samples from source and target domains are freely
accessible during a training phase. However, such an assumption is rarely plausible in the …

This article tackles Partial Domain Adaptation (PDA) where the target label set is a subset of
the source label set. A key challenging issue in PDA is to prevent negative transfer by …

With the rapid development of Siamese network based trackers, a set of related methods
have produced considerable performance improvement. However, the tracking results are …

Filtering noisy labels is crucial for robust training of deep neural networks. To train networks
with noisy labels, sampling methods have been introduced, which sample the reliable …

Label assignment refers to determining positive/negative labels for each sample to
supervise the training process. Existing Siamese-based trackers primarily use fixed label …

Network quantization has been widely studied to compress the deep neural network in
mobile devices. Conventional methods quantize the network parameters of all layers with …

Automatic machine anomaly sound detection is important for machine maintenance in
display manufacturing factory. Recently, unsupervised anomaly detection approach based …