Comprehensive Generative Replay for Task-Incremental Segmentation with Concurrent Appearance and Semantic Forgetting

• URL: http://arxiv.org/abs/2406.19796v1
• Date: Fri, 28 Jun 2024 10:05:58 GMT
• Title: Comprehensive Generative Replay for Task-Incremental Segmentation with Concurrent Appearance and Semantic Forgetting
• Authors: Wei Li, Jingyang Zhang, Pheng-Ann Heng, Lixu Gu,
• Abstract summary: Generalist segmentation models are increasingly favored for diverse tasks involving various objects from different image sources. We propose a Comprehensive Generative (CGR) framework that restores appearance and semantic knowledge by synthesizing image-mask pairs. Experiments on incremental tasks (cardiac, fundus and prostate segmentation) show its clear advantage for alleviating concurrent appearance and semantic forgetting.
• Score: 49.87694319431288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalist segmentation models are increasingly favored for diverse tasks involving various objects from different image sources. Task-Incremental Learning (TIL) offers a privacy-preserving training paradigm using tasks arriving sequentially, instead of gathering them due to strict data sharing policies. However, the task evolution can span a wide scope that involves shifts in both image appearance and segmentation semantics with intricate correlation, causing concurrent appearance and semantic forgetting. To solve this issue, we propose a Comprehensive Generative Replay (CGR) framework that restores appearance and semantic knowledge by synthesizing image-mask pairs to mimic past task data, which focuses on two aspects: modeling image-mask correspondence and promoting scalability for diverse tasks. Specifically, we introduce a novel Bayesian Joint Diffusion (BJD) model for high-quality synthesis of image-mask pairs with their correspondence explicitly preserved by conditional denoising. Furthermore, we develop a Task-Oriented Adapter (TOA) that recalibrates prompt embeddings to modulate the diffusion model, making the data synthesis compatible with different tasks. Experiments on incremental tasks (cardiac, fundus and prostate segmentation) show its clear advantage for alleviating concurrent appearance and semantic forgetting. Code is available at https://github.com/jingyzhang/CGR.

Related papers

• Unlocking Pre-trained Image Backbones for Semantic Image Synthesis [29.688029979801577]
  We propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images. Our model, which we dub DP-SIMS, achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K, COCO-Stuff, and Cityscapes.
  arXiv  Detail & Related papers  (2023-12-20T09:39:19Z)
• ComPtr: Towards Diverse Bi-source Dense Prediction Tasks via A Simple yet General Complementary Transformer [91.43066633305662]
  We propose a novel underlineComPlementary underlinetransformer, textbfComPtr, for diverse bi-source dense prediction tasks. ComPtr treats different inputs equally and builds an efficient dense interaction model in the form of sequence-to-sequence on top of the transformer.
  arXiv  Detail & Related papers  (2023-07-23T15:17:45Z)
• Source Identification: A Self-Supervision Task for Dense Prediction [8.744460886823322]
  We propose a new self-supervision task called source identification (SI) Synthetic images are generated by fusing multiple source images and the network's task is to reconstruct the original images, given the fused images. We validate our method on two medical image segmentation tasks: brain tumor segmentation and white matter hyperintensities segmentation.
  arXiv  Detail & Related papers  (2023-07-05T12:27:58Z)
• Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models [80.23791222509644]
  Inconsistent AI models are considered brittle and untrustworthy by human users. We find that state-of-the-art vision-language models suffer from a surprisingly high degree of inconsistent behavior across tasks. We propose a rank correlation-based auxiliary training objective, computed over large automatically created cross-task contrast sets.
  arXiv  Detail & Related papers  (2023-03-28T16:57:12Z)
• Semantics-Depth-Symbiosis: Deeply Coupled Semi-Supervised Learning of Semantics and Depth [83.94528876742096]
  We tackle the MTL problem of two dense tasks, ie, semantic segmentation and depth estimation, and present a novel attention module called Cross-Channel Attention Module (CCAM) In a true symbiotic spirit, we then formulate a novel data augmentation for the semantic segmentation task using predicted depth called AffineMix, and a simple depth augmentation using predicted semantics called ColorAug. Finally, we validate the performance gain of the proposed method on the Cityscapes dataset, which helps us achieve state-of-the-art results for a semi-supervised joint model based on depth and semantic
  arXiv  Detail & Related papers  (2022-06-21T17:40:55Z)
• Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism [120.1998866178014]
  We present a flexible framework for continual object detection via pRotOtypical taSk corrElaTion guided gaTingAnism (ROSETTA) Concretely, a unified framework is shared by all tasks while task-aware gates are introduced to automatically select sub-models for specific tasks. Experiments on COCO-VOC, KITTI-Kitchen, class-incremental detection on VOC and sequential learning of four tasks show that ROSETTA yields state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-05-06T07:31:28Z)
• Decoupled Multi-task Learning with Cyclical Self-Regulation for Face Parsing [71.19528222206088]
  We propose a novel Decoupled Multi-task Learning with Cyclical Self-Regulation for face parsing. Specifically, DML-CSR designs a multi-task model which comprises face parsing, binary edge, and category edge detection. Our method achieves the new state-of-the-art performance on the Helen, CelebA-HQ, and LapaMask datasets.
  arXiv  Detail & Related papers  (2022-03-28T02:12:30Z)
• Dependent Multi-Task Learning with Causal Intervention for Image Captioning [10.6405791176668]
  In this paper, we propose a dependent multi-task learning framework with the causal intervention (DMTCI) Firstly, we involve an intermediate task, bag-of-categories generation, before the final task, image captioning. Secondly, we apply Pearl's do-calculus on the model, cutting off the link between the visual features and possible confounders. Finally, we use a multi-agent reinforcement learning strategy to enable end-to-end training and reduce the inter-task error accumulations.
  arXiv  Detail & Related papers  (2021-05-18T14:57:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.