Review the state-of-the-art technologies of semantic segmentation based on deep learning
The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …
information and predict the semantic category of each pixel from a given label set. With the …
A brief survey on semantic segmentation with deep learning
S Hao, Y Zhou, Y Guo - Neurocomputing, 2020 - Elsevier
Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …
performance of semantic segmentation has been greatly improved by using deep learning …
Learning affinity from attention: End-to-end weakly-supervised semantic segmentation with transformers
Weakly-supervised semantic segmentation (WSSS) with image-level labels is an important
and challenging task. Due to the high training efficiency, end-to-end solutions for WSSS …
and challenging task. Due to the high training efficiency, end-to-end solutions for WSSS …
Class re-activation maps for weakly-supervised semantic segmentation
Extracting class activation maps (CAM) is arguably the most standard step of generating
pseudo masks for weakly-supervised semantic segmentation (WSSS). Yet, we find that the …
pseudo masks for weakly-supervised semantic segmentation (WSSS). Yet, we find that the …
Regional semantic contrast and aggregation for weakly supervised semantic segmentation
Learning semantic segmentation from weakly-labeled (eg, image tags only) data is
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …
challenging since it is hard to infer dense object regions from sparse semantic tags. Despite …
L2g: A simple local-to-global knowledge transfer framework for weakly supervised semantic segmentation
Mining precise class-aware attention maps, aka, class activation maps, is essential for
weakly supervised semantic segmentation. In this paper, we present L2G, a simple online …
weakly supervised semantic segmentation. In this paper, we present L2G, a simple online …
Clip is also an efficient segmenter: A text-driven approach for weakly supervised semantic segmentation
Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging
task. Mainstream approaches follow a multi-stage framework and suffer from high training …
task. Mainstream approaches follow a multi-stage framework and suffer from high training …
Causal intervention for weakly-supervised semantic segmentation
We present a causal inference framework to improve Weakly-Supervised Semantic
Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by …
Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by …
Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation
Image-level weakly supervised semantic segmentation is a challenging problem that has
been deeply studied in recent years. Most of advanced solutions exploit class activation map …
been deeply studied in recent years. Most of advanced solutions exploit class activation map …
Clims: Cross language image matching for weakly supervised semantic segmentation
It has been widely known that CAM (Class Activation Map) usually only activates
discriminative object regions and falsely includes lots of object-related backgrounds. As only …
discriminative object regions and falsely includes lots of object-related backgrounds. As only …