MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation

• URL: http://arxiv.org/abs/2411.19067v1
• Date: Thu, 28 Nov 2024 11:27:56 GMT
• Title: MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation
• Authors: Minhyun Lee, Seungho Lee, Song Park, Dongyoon Han, Byeongho Heo, Hyunjung Shim,
• Abstract summary: Referring Image Code (RIS) is an advanced vision-aware task that involves identifying and segmenting objects within an image.<n>We propose a novel training framework called Masked Referring Image Code (MaskRIS)<n>MaskRIS uses both image and text masking, followed by Contextual Learning to fully exploit the benefits of the masking strategy.
• Score: 38.3201448852059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring Image Segmentation (RIS) is an advanced vision-language task that involves identifying and segmenting objects within an image as described by free-form text descriptions. While previous studies focused on aligning visual and language features, exploring training techniques, such as data augmentation, remains underexplored. In this work, we explore effective data augmentation for RIS and propose a novel training framework called Masked Referring Image Segmentation (MaskRIS). We observe that the conventional image augmentations fall short of RIS, leading to performance degradation, while simple random masking significantly enhances the performance of RIS. MaskRIS uses both image and text masking, followed by Distortion-aware Contextual Learning (DCL) to fully exploit the benefits of the masking strategy. This approach can improve the model's robustness to occlusions, incomplete information, and various linguistic complexities, resulting in a significant performance improvement. Experiments demonstrate that MaskRIS can easily be applied to various RIS models, outperforming existing methods in both fully supervised and weakly supervised settings. Finally, MaskRIS achieves new state-of-the-art performance on RefCOCO, RefCOCO+, and RefCOCOg datasets. Code is available at https://github.com/naver-ai/maskris.

Related papers

• High-Quality Mask Tuning Matters for Open-Vocabulary Segmentation [109.19165503929992]
  We present MaskCLIP++, which uses ground-truth masks instead of generated masks to enhance the mask classification capability of CLIP. After low-cost fine-tuning, MaskCLIP++ significantly improves the mask classification performance on multi-domain datasets. We achieve performance improvements of +1.7, +2.3, +2.1, +3.1, and +0.3 mIoU on the A-847, PC-459, A-150, PC-59, and PAS-20 datasets.
  arXiv  Detail & Related papers  (2024-12-16T05:44:45Z)
• Semantic Refocused Tuning for Open-Vocabulary Panoptic Segmentation [42.020470627552136]
  Open-vocabulary panoptic segmentation is an emerging task aiming to accurately segment the image into semantically meaningful masks. mask classification is the main performance bottleneck for open-vocab panoptic segmentation. We propose Semantic Refocused Tuning, a novel framework that greatly enhances open-vocab panoptic segmentation.
  arXiv  Detail & Related papers  (2024-09-24T17:50:28Z)
• ColorMAE: Exploring data-independent masking strategies in Masked AutoEncoders [53.3185750528969]
  Masked AutoEncoders (MAE) have emerged as a robust self-supervised framework. We introduce a data-independent method, termed ColorMAE, which generates different binary mask patterns by filtering random noise. We demonstrate our strategy's superiority in downstream tasks compared to random masking.
  arXiv  Detail & Related papers  (2024-07-17T22:04:00Z)
• Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation [13.924553294859315]
  Point PrompTing (PPT) is a point generator that harnesses CLIP's text-image alignment capability and SAM's powerful mask generation ability. PPT significantly and consistently outperforms prior weakly supervised techniques on mIoU.
  arXiv  Detail & Related papers  (2024-04-18T08:46:12Z)
• CL-MAE: Curriculum-Learned Masked Autoencoders [49.24994655813455]
  We propose a curriculum learning approach that updates the masking strategy to continually increase the complexity of the self-supervised reconstruction task. We train our Curriculum-Learned Masked Autoencoder (CL-MAE) on ImageNet and show that it exhibits superior representation learning capabilities compared to MAE.
  arXiv  Detail & Related papers  (2023-08-31T09:13:30Z)
• Mask to reconstruct: Cooperative Semantics Completion for Video-text Retrieval [19.61947785487129]
  Mask for Semantics Completion (MASCOT) based on semantic-based masked modeling. Our MASCOT performs state-of-the-art performance on four major text-video retrieval benchmarks.
  arXiv  Detail & Related papers  (2023-05-13T12:31:37Z)
• Improving Masked Autoencoders by Learning Where to Mask [65.89510231743692]
  Masked image modeling is a promising self-supervised learning method for visual data. We present AutoMAE, a framework that uses Gumbel-Softmax to interlink an adversarially-trained mask generator and a mask-guided image modeling process. In our experiments, AutoMAE is shown to provide effective pretraining models on standard self-supervised benchmarks and downstream tasks.
  arXiv  Detail & Related papers  (2023-03-12T05:28:55Z)
• Improving self-supervised representation learning via sequential adversarial masking [12.176299580413097]
  Masking-based pretext tasks extend beyond NLP, serving as useful pretraining objectives in computer vision. We propose a new framework that generates masks in a sequential fashion with different constraints on the adversary.
  arXiv  Detail & Related papers  (2022-12-16T04:25:43Z)
• MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining [138.86293836634323]
  MaskCLIP incorporates a newly proposed masked self-distillation into contrastive language-image pretraining. MaskCLIP achieves superior results in linear probing, finetuning, and zero-shot performance with the guidance of the language encoder.
  arXiv  Detail & Related papers  (2022-08-25T17:59:58Z)
• Masked Autoencoders are Robust Data Augmentors [90.34825840657774]
  Regularization techniques like image augmentation are necessary for deep neural networks to generalize well. We propose a novel perspective of augmentation to regularize the training process. We show that utilizing such model-based nonlinear transformation as data augmentation can improve high-level recognition tasks.
  arXiv  Detail & Related papers  (2022-06-10T02:41:48Z)
• Adversarial Masking for Self-Supervised Learning [81.25999058340997]
  Masked image model (MIM) framework for self-supervised learning, ADIOS, is proposed. It simultaneously learns a masking function and an image encoder using an adversarial objective. It consistently improves on state-of-the-art self-supervised learning (SSL) methods on a variety of tasks and datasets.
  arXiv  Detail & Related papers  (2022-01-31T10:23:23Z)

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.