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Found 9 papers, 4 papers with code
Robustness May be More Brittle than We Think under Different Degrees of Distribution Shifts
This indicates that while pre-trained representations may help improve downstream in-distribution performance, they could have minimal or even adverse effects on generalization in certain OOD scenarios of the downstream task if not used properly.
A Causal Framework to Unify Common Domain Generalization Approaches
Domain generalization (DG) is about learning models that generalize well to new domains that are related to, but different from, the training domain(s).
Contrastive Domain Generalization via Logit Attribution Matching
Domain Generalization (DG) is an important open problem in machine learning.
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention.
CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving
One main reason that impedes the development of truly reliably self-driving systems is the lack of public datasets for evaluating the performance of object detectors on corner cases.
MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition
MODNet is easy to be trained in an end-to-end manner.
Ranked #1 on
Image Matting
on PPM-100
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning
Although SSL methods have achieved impressive results in image classification, the performances of applying them to pixel-wise tasks are unsatisfactory due to their need for dense outputs.
Referring Image Segmentation via Recurrent Refinement Networks
We address the problem of image segmentation from natural language descriptions.
