Applying ViT in Generalized Few-shot Semantic Segmentation

• URL: http://arxiv.org/abs/2408.14957v1
• Date: Tue, 27 Aug 2024 11:04:53 GMT
• Title: Applying ViT in Generalized Few-shot Semantic Segmentation
• Authors: Liyuan Geng, Jinhong Xia, Yuanhe Guo,
• Abstract summary: This paper explores the capability of ViT-based models under the generalized few-shot semantic segmentation (GFSS) framework. We conduct experiments with various combinations of backbone models, including ResNets and pretrained Vision Transformer (ViT)-based models. We demonstrate the great potential of large pretrained ViT-based model on GFSS task, and expect further improvement on testing benchmarks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores the capability of ViT-based models under the generalized few-shot semantic segmentation (GFSS) framework. We conduct experiments with various combinations of backbone models, including ResNets and pretrained Vision Transformer (ViT)-based models, along with decoders featuring a linear classifier, UPerNet, and Mask Transformer. The structure made of DINOv2 and linear classifier takes the lead on popular few-shot segmentation bench mark PASCAL-$5^i$, substantially outperforming the best of ResNet structure by 116% in one-shot scenario. We demonstrate the great potential of large pretrained ViT-based model on GFSS task, and expect further improvement on testing benchmarks. However, a potential caveat is that when applying pure ViT-based model and large scale ViT decoder, the model is easy to overfit.

Related papers

• Minimalist and High-Performance Semantic Segmentation with Plain Vision Transformers [10.72362704573323]
  We introduce the PlainSeg, a model comprising only three 3$times$3 convolutions in addition to the transformer layers. We also present the PlainSeg-Hier, which allows for the utilization of hierarchical features.
  arXiv  Detail & Related papers  (2023-10-19T14:01:40Z)
• Semantic Segmentation using Vision Transformers: A survey [0.0]
  Convolutional neural networks (CNN) and Vision Transformers (ViTs) provide the architecture models for semantic segmentation. ViTs have proven success in image classification, they cannot be directly applied to dense prediction tasks such as image segmentation and object detection. This survey aims to review and compare the performances of ViT architectures designed for semantic segmentation using benchmarking datasets.
  arXiv  Detail & Related papers  (2023-05-05T04:11:00Z)
• GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer [76.2625311630021]
  Vision transformers (ViTs) have shown very impressive empirical performance in various computer vision tasks. To mitigate this challenging problem, structured pruning is a promising solution to compress model size and enable practical efficiency. We propose GOHSP, a unified framework of Graph and Optimization-based Structured Pruning for ViT models.
  arXiv  Detail & Related papers  (2023-01-13T00:40:24Z)
• Exploring Efficient Few-shot Adaptation for Vision Transformers [70.91692521825405]
  We propose a novel efficient Transformer Tuning (eTT) method that facilitates finetuning ViTs in the Few-shot Learning tasks. Key novelties come from the newly presented Attentive Prefix Tuning (APT) and Domain Residual Adapter (DRA) We conduct extensive experiments to show the efficacy of our model.
  arXiv  Detail & Related papers  (2023-01-06T08:42:05Z)
• ViTPose++: Vision Transformer for Generic Body Pose Estimation [70.86760562151163]
  We show the surprisingly good properties of plain vision transformers for body pose estimation from various aspects. ViTPose employs the plain and non-hierarchical vision transformer as an encoder to encode features and a lightweight decoder to decode body keypoints. We empirically demonstrate that the knowledge of large ViTPose models can be easily transferred to small ones via a simple knowledge token.
  arXiv  Detail & Related papers  (2022-12-07T12:33:28Z)
• Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection [39.37861288287621]
  A MIM pre-trained vanilla ViT can work surprisingly well in the challenging object-level recognition scenario. A random compact convolutional stem supplants the pre-trained large kernel patchify stem. The proposed detector, named MIMDet, enables a MIM pre-trained vanilla ViT to outperform hierarchical Swin Transformer by 2.3 box AP and 2.5 mask AP on.
  arXiv  Detail & Related papers  (2022-04-06T17:59:04Z)
• Improving Vision Transformers by Revisiting High-frequency Components [106.7140968644414]
  We show that Vision Transformer (ViT) models are less effective in capturing the high-frequency components of images than CNN models. To compensate, we propose HAT, which directly augments high-frequency components of images via adversarial training. We show that HAT can consistently boost the performance of various ViT models.
  arXiv  Detail & Related papers  (2022-04-03T05:16:51Z)
• Global Vision Transformer Pruning with Hessian-Aware Saliency [93.33895899995224]
  This work challenges the common design philosophy of the Vision Transformer (ViT) model with uniform dimension across all the stacked blocks in a model stage. We derive a novel Hessian-based structural pruning criteria comparable across all layers and structures, with latency-aware regularization for direct latency reduction. Performing iterative pruning on the DeiT-Base model leads to a new architecture family called NViT (Novel ViT), with a novel parameter that utilizes parameters more efficiently.
  arXiv  Detail & Related papers  (2021-10-10T18:04:59Z)
• Vision Transformers are Robust Learners [65.91359312429147]
  We study the robustness of the Vision Transformer (ViT) against common corruptions and perturbations, distribution shifts, and natural adversarial examples. We present analyses that provide both quantitative and qualitative indications to explain why ViTs are indeed more robust learners.
  arXiv  Detail & Related papers  (2021-05-17T02:39:22Z)

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.