Task and Environment-Aware Virtual Scene Rearrangement for Enhanced Safety in Virtual Reality.

Emerging VR applications have revolutionized user experiences by immersing individuals in digitally crafted environments. However, fully immersive experiences introduce new challenges, notably the risk of physical hazards when users are unaware of their surroundings. Existing solutions, including guardian spaces and locomotion systems, present trade-offs that either disrupt the immersive experience or risk inducing motion sickness.

Prior Guided Transformer for Accurate Radiology Reports Generation.

In this paper, we propose a prior guided transformer for accurate radiology reports generation. In the encoder part, a radiograph is firstly represented by a set of patch features, which is obtained through a convolutional neural network and a traditional transformer encoder. Then an Additive Gaussian model is applied to represent the prior knowledge based on unsupervised clustering and sparse attention.

Robust discriminative tracking via landmark-based label propagation.

The appearance of an object could be continuously changing during tracking, thereby being not independent identically distributed. A good discriminative tracker often needs a large number of training samples to fit the underlying data distribution, which is impractical for visual tracking. In this paper, we present a new discriminative tracker via landmark-based label propagation (LLP) that is nonparametric and makes no specific assumption about the sample distribution.