GMGaze achieves mean angular errors of 2.49°, 3.22°, 10.16°, and 1.44° on MPIIFaceGaze, EYEDIAP, Gaze360, and ETH-XGaze by early context-conditioned fusion and MoE scaling, outperforming baselines in within- and cross-domain settings.
Domain-adversarial training of neural networks.Journal of machine learning research, 17(59):1–35, 2016
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.
citing papers explorer
-
GMGaze: MoE-Based Context-Aware Gaze Estimation with CLIP and Multiscale Transformer
GMGaze achieves mean angular errors of 2.49°, 3.22°, 10.16°, and 1.44° on MPIIFaceGaze, EYEDIAP, Gaze360, and ETH-XGaze by early context-conditioned fusion and MoE scaling, outperforming baselines in within- and cross-domain settings.
-
A Ridge Too Far: Correcting Over-Shrinkage via Negative Regularization
Negative-capable ridge regression uses controlled negative regularization as anti-shrinkage to increase effective complexity along weak eigendirections and mitigate underfitting in small-data regression.