mlsanityData to 7 Oct 2026

SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation

arXiv 2210.12417 ↗First posted 22 Oct 2022cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Recent mainstream weakly-supervised semantic segmentation (WSSS) approaches mainly relies on image-level classification learning, which has limited representation capacity. In this paper, we propose a novel semantic learning based framework, named SLAMs (Semantic Learning based Activation Map), for WSSS.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 29 references that are in the map. First posted on arXiv 22 Oct 2022.

Builds on

25 of 29 references.

References · 25

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  4. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  5. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
  6. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  7. 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
  8. 2018BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentationcited by 645
  9. 2014Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFscited by 552
  10. 2016ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentationcited by 330
  11. 2018Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentationcited by 321
  12. 2020Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentationcited by 299
13 more
  1. 2015What's the Point: Semantic Segmentation with Point Supervisioncited by 297
  2. 2016Simple Does It: Weakly Supervised Instance and Semantic Segmentationcited by 226
  3. 2017Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approachcited by 201
  4. 2016Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentationcited by 198
  5. 2018Tell Me Where to Look: Guided Attention Inference Networkcited by 168
  6. 2018Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentationcited by 145
  7. 2022Multi-class Token Transformer for Weakly Supervised Semantic Segmentationcited by 138
  8. 2018Learning random-walk label propagation for weakly-supervised semantic segmentationcited by 101
  9. 2019Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentationcited by 97
  10. 2018CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentationcited by 80
  11. 2021Complementary Patch for Weakly Supervised Semantic Segmentationcited by 75
  12. 2019Self-Supervised Difference Detection for Weakly-Supervised Semantic Segmentationcited by 58
  13. 2022Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholdscited by 37

Built on by

Not cited yet.