1 code implementation • 7 Feb 2024 • Hailiang Li, Yan Huo, Yan Wang, Xu Yang, Miaohui Hao, Xiao Wang
As the modern CPU, GPU, and NPU chip design complexity and transistor counts keep increasing, and with the relentless shrinking of semiconductor technology nodes to nearly 1 nanometer, the placement and routing have gradually become the two most pivotal processes in modern very-large-scale-integrated (VLSI) circuit back-end design.
no code implementations • 30 Oct 2019 • Hailiang Li, Adele Y. C. Wang, Yang Liu, Du Tang, Zhibin Lei, Wenye Li
The Transformer based neural networks have been showing significant advantages on most evaluations of various natural language processing and other sequence-to-sequence tasks due to its inherent architecture based superiorities.
1 code implementation • 14 Dec 2017 • Hailiang Li, Kin-Man Lam, Miaohui Wang
In this paper, we present a novel feature-augmented random forest (FARF) for image super-resolution, where the conventional gradient-based features are augmented with gradient magnitudes and different feature recipes are formulated on different stages in an RF.
Ranked #48 on Image Super-Resolution on BSD100 - 4x upscaling
1 code implementation • 30 Aug 2017 • Hailiang Li, Kin-Man Lam, Dong Li
In the JMPF scheme, the original feature space is transformed into a compactly pre-clustered feature space, via a trained rotation matrix.
Ranked #50 on Image Super-Resolution on BSD100 - 4x upscaling
no code implementations • 30 Nov 2016 • Hailiang Li, Kin-Man Lam, Man-Yau Chiu, Kangheng Wu, Zhibin Lei
The constrained local model (CLM) proposes a paradigm that the locations of a set of local landmark detectors are constrained to lie in a subspace, spanned by a shape point distribution model (PDM).
no code implementations • 21 Nov 2016 • Hailiang Li, Kin-Man Lam, Edmond M. Y. Chiu, Kangheng Wu, Zhibin Lei
In this paper, we present a random-forest based fast cascaded regression model for face alignment, via a novel local feature.