Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Yang, Lihe"'
Semi-supervised semantic segmentation (SSS) aims at learning rich visual knowledge from cheap unlabeled images to enhance semantic segmentation capability. Among recent works, UniMatch improves its precedents tremendously by amplifying the practice o
Externí odkaz:
http://arxiv.org/abs/2410.10777
Autor:
Yang, Lihe, Kang, Bingyi, Huang, Zilong, Zhao, Zhen, Xu, Xiaogang, Feng, Jiashi, Zhao, Hengshuang
This work presents Depth Anything V2. Without pursuing fancy techniques, we aim to reveal crucial findings to pave the way towards building a powerful monocular depth estimation model. Notably, compared with V1, this version produces much finer and m
Externí odkaz:
http://arxiv.org/abs/2406.09414
This work presents Depth Anything, a highly practical solution for robust monocular depth estimation. Without pursuing novel technical modules, we aim to build a simple yet powerful foundation model dealing with any images under any circumstances. To
Externí odkaz:
http://arxiv.org/abs/2401.10891
Unsupervised semantic segmentation aims to categorize each pixel in an image into a corresponding class without the use of annotated data. It is a widely researched area as obtaining labeled datasets is expensive. While previous works in the field ha
Externí odkaz:
http://arxiv.org/abs/2311.18628
Semantic segmentation has witnessed tremendous progress due to the proposal of various advanced network architectures. However, they are extremely hungry for delicate annotations to train, and the acquisition is laborious and unaffordable. Therefore,
Externí odkaz:
http://arxiv.org/abs/2310.15160
Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it. In this work, we revisit the core assumption that supports co-training: multiple comp
Externí odkaz:
http://arxiv.org/abs/2308.09281
Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to discard uncertain samples. This p
Externí odkaz:
http://arxiv.org/abs/2308.06777
Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress. Despite their promising performance, current state-of-the-art methods tend to increasingly complex designs at the cost of introducing more network components and a
Externí odkaz:
http://arxiv.org/abs/2212.04976
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. Intriguingly, w
Externí odkaz:
http://arxiv.org/abs/2208.09910
Self-training via pseudo labeling is a conventional, simple, and popular pipeline to leverage unlabeled data. In this work, we first construct a strong baseline of self-training (namely ST) for semi-supervised semantic segmentation via injecting stro
Externí odkaz:
http://arxiv.org/abs/2106.05095