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Found 6 papers, 5 papers with code
Weakly supervised training of universal visual concepts for multi-domain semantic segmentation
For instance, pickups are labeled as trucks in VIPER, cars in Vistas, and vans in ADE20k.
IRB-NLP at SemEval-2022 Task 1: Exploring the Relationship Between Words and Their Semantic Representations
We give a detailed overview of the systems that we designed for Definition Modeling and Reverse Dictionary tasks, and that achieved top scores on SemEval-2022 CODWOE challenge in several subtasks.
Multi-domain semantic segmentation with overlapping labels
Deep supervised models have an unprecedented capacity to absorb large quantities of training data.
Revisiting consistency for semi-supervised semantic segmentation
Our experiments show clear advantages of (1) one-way consistency, (2) perturbing only the student branch, and (3) strong photometric and geometric perturbations.
Densely connected normalizing flows
Normalizing flows are bijective mappings between inputs and latent representations with a fully factorized distribution.
Ranked #2 on
Image Generation
on ImageNet 32x32
(bpd metric)
Multi-domain semantic segmentation with pyramidal fusion
We present our submission to the semantic segmentation contest of the Robust Vision Challenge held at ECCV 2020.
