Search Results for author:
Found 9 papers, 8 papers with code
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
To this end, we scale up the dataset by designing a data engine to collect and automatically annotate large-scale unlabeled data (~62M), which significantly enlarges the data coverage and thus is able to reduce the generalization error.
Ranked #3 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
A Lightweight Clustering Framework for Unsupervised Semantic Segmentation
We thus propose a lightweight clustering framework for unsupervised semantic segmentation.
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
Then, we investigate the role of synthetic images by joint training with real images, or pre-training for real images.
Diverse Cotraining Makes Strong Semi-Supervised Segmentor
Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it.
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to discard uncertain samples.
Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation
Differently, in this work, we follow a standard teacher-student framework and propose AugSeg, a simple and clean approach that focuses mainly on data perturbations to boost the SSS performance.
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version.
Ranked #1 on
Semi-supervised Change Detection
on WHU - 10% labeled data
(IoU metric)
Semi-supervised Change Detection
Semi-supervised Medical Image Segmentation
+1
ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
In this work, we first construct a strong baseline of self-training (namely ST) for semi-supervised semantic segmentation via injecting strong data augmentations (SDA) on unlabeled images to alleviate overfitting noisy labels as well as decouple similar predictions between the teacher and student.
Mining Latent Classes for Few-shot Segmentation
Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes.
Ranked #41 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
