6 accepted papers
Tackling multi-object tracking and segmentation (MOTS) can be attributed to a multi-task learning task, i.e., performing Segmentation and Identity Embedding jointly (SIEJ). Unfortunately, achieving optimal SIEJ is non-trivial, as it relies on different spatiotemporal features of objects. Besides, th…
Multi-layer networks (MLN) effectively model interactions across layers, and the network disintegration (ND) problem yields significant importance in the analysis of MLN. Unfortunately, previous advances in ND for single-layer networks exhibits inefficiency and lack of scalability when extended to M…
In Electronic Support Measure (ESM) systems, the recognition of radar emitters stands as a pivotal yet intricate task. The complex electromagnetic environments, however, often hinders the collection of clean radar signal data, and results in data with different noise levels. Consequently, formulatin…
The black-box characteristic of deep reinforcement learning restricts the safe and scalable application of decision models in practical deployment. Existing interpretability methods for deep reinforcement learning models are often inadequate in providing comprehensive insights and generating logical…
Dubins coverage has been extensively researched to address the coverage path planning (CPP) problem of a known environment for the curvature-constrained robot. However, its fixed-speed assumption prevents the robot from accelerating to reduce the time and limits its flexibility to avoid obstacles. T…
Stereo matching algorithms usually consist of four steps, including matching cost calculation, matching cost aggregation, disparity calculation, and disparity refinement. Existing CNN-based methods only adopt CNN to solve parts of the four steps, or use different networks to deal with different step…