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LNSMM: Eye Gaze Estimation With Local Network Share Multiview Multitask
Eye gaze estimation has become increasingly significant in computer vision. In this paper, we systematically study the mainstream of eye gaze estimation methods, propose a novel methodology to estimate eye gaze points and eye gaze directions simultaneously. First, we construct a local sharing network for feature extraction of gaze points and gaze directions estimation, which can reduce network computational parameters and converge quickly;Second, we propose a Multiview Multitask Learning (MTL) framework, for gaze directions, a coplanar constraint is proposed for the left and right eyes, for gaze points, three views data input indirectly introduces eye position information, a cross-view pooling module is designed, propose joint loss which handle both gaze points and gaze directions estimation. Eventually, we collect a dataset to use of gaze points, which have three views to exist public dataset. The experiment show our method is state-of-the-art the current mainstream methods on two indicators of gaze points and gaze directions.
Machine Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO
Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA).
