Arrival times of seismic phases contribute substantially to the study of the inner working of
the Earth. Despite great advances in seismic data collection, the usage of seismic arrival …

Salient object segmentation, edge detection, and skeleton extraction are three contrasting
low-level pixel-wise vision problems, where existing works mostly focused on designing …

Many convolutional neural networks (CNNs) for single image deblurring employ a U-Net
structure to estimate latent sharp images. Having long been proven to be effective in image …

Features from multiple scales can greatly benefit the semantic edge detection task if they are
well fused. However, the prevalent semantic edge detection methods apply a fixed weight …

Panoptic segmentation combines instance and semantic predictions, allowing the detection
of countable objects and different backgrounds simultaneously. Effectively approaching …

Aiming at the real-time detection of multiple objects and micro-objects in large-scene remote
sensing images, a cascaded convolutional neural network real-time object-detection …

Computing object skeletons in natural images is challenging, owing to large variations in
object appearance and scale, and the complexity of handling background clutter. Many …

We present a learning method for predicting animation skeletons for input 3D models of
articulated characters. In contrast to previous approaches that fit pre-defined skeleton …

Conventional networks for object skeleton detection are usually hand-crafted. Despite the
effectiveness, hand-crafted network architectures lack the theoretical basis and require …

Detecting object skeletons in natural images presents challenges due to varied object
scales and complex backgrounds. The skeleton is a highly compressing shape …