Panoptic SwiftNet: Pyramidal Fusion for Real-time Panoptic Segmentation

• URL: http://arxiv.org/abs/2203.07908v2
• Date: Tue, 18 Apr 2023 14:46:07 GMT
• Title: Panoptic SwiftNet: Pyramidal Fusion for Real-time Panoptic Segmentation
• Authors: Josip \v{S}ari\'c, Marin Or\v{s}i\'c, Sini\v{s}a \v{S}egvi\'c
• Abstract summary: Many applications require fast inference over large input resolutions on affordable or even embedded hardware. We propose to achieve this goal by trading off backbone capacity for multi-scale feature extraction. We present panoptic experiments on Cityscapes, Vistas, COCO and the BSB-Aerial dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dense panoptic prediction is a key ingredient in many existing applications such as autonomous driving, automated warehouses or remote sensing. Many of these applications require fast inference over large input resolutions on affordable or even embedded hardware. We propose to achieve this goal by trading off backbone capacity for multi-scale feature extraction. In comparison with contemporaneous approaches to panoptic segmentation, the main novelties of our method are efficient scale-equivariant feature extraction, cross-scale upsampling through pyramidal fusion and boundary-aware learning of pixel-to-instance assignment. The proposed method is very well suited for remote sensing imagery due to the huge number of pixels in typical city-wide and region-wide datasets. We present panoptic experiments on Cityscapes, Vistas, COCO and the BSB-Aerial dataset. Our models outperform the state of the art on the BSB-Aerial dataset while being able to process more than a hundred 1MPx images per second on a RTX3090 GPU with FP16 precision and TensorRT optimization.

Related papers

• Multi-Head Attention Residual Unfolded Network for Model-Based Pansharpening [2.874893537471256]
  Unfolding fusion methods integrate the powerful representation capabilities of deep learning with the robustness of model-based approaches. In this paper, we propose a model-based deep unfolded method for satellite image fusion. Experimental results on PRISMA, Quickbird, and WorldView2 datasets demonstrate the superior performance of our method.
  arXiv  Detail & Related papers  (2024-09-04T13:05:00Z)
• SaccadeDet: A Novel Dual-Stage Architecture for Rapid and Accurate Detection in Gigapixel Images [50.742420049839474]
  'SaccadeDet' is an innovative architecture for gigapixel-level object detection, inspired by the human eye saccadic movement. Our approach, evaluated on the PANDA dataset, achieves an 8x speed increase over the state-of-the-art methods. It also demonstrates significant potential in gigapixel-level pathology analysis through its application to Whole Slide Imaging.
  arXiv  Detail & Related papers  (2024-07-25T11:22:54Z)
• Exploring Multi-view Pixel Contrast for General and Robust Image Forgery Localization [4.8454936010479335]
  We propose a Multi-view Pixel-wise Contrastive algorithm (MPC) for image forgery localization. Specifically, we first pre-train the backbone network with the supervised contrastive loss. Then the localization head is fine-tuned using the cross-entropy loss, resulting in a better pixel localizer.
  arXiv  Detail & Related papers  (2024-06-19T13:51:52Z)
• Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers [0.11219061154635457]
  Whole-Slide Imaging allows for the capturing and digitization of high-resolution images of histological specimen. transformer architecture has been proposed as a possible candidate for effectively leveraging the high-resolution information. We propose a novel cascaded cross-attention network (CCAN) based on the cross-attention mechanism that scales linearly with the number of extracted patches.
  arXiv  Detail & Related papers  (2023-05-11T16:42:24Z)
• Efficient Context Integration through Factorized Pyramidal Learning for Ultra-Lightweight Semantic Segmentation [1.0499611180329804]
  We propose a novel Factorized Pyramidal Learning (FPL) module to aggregate rich contextual information in an efficient manner. We decompose the spatial pyramid into two stages which enables a simple and efficient feature fusion within the module to solve the notorious checkerboard effect. Based on the FPL module and FIR unit, we propose an ultra-lightweight real-time network, called FPLNet, which achieves state-of-the-art accuracy-efficiency trade-off.
  arXiv  Detail & Related papers  (2023-02-23T05:34:51Z)
• Probabilistic Deep Metric Learning for Hyperspectral Image Classification [91.5747859691553]
  This paper proposes a probabilistic deep metric learning framework for hyperspectral image classification. It aims to predict the category of each pixel for an image captured by hyperspectral sensors. Our framework can be readily applied to existing hyperspectral image classification methods.
  arXiv  Detail & Related papers  (2022-11-15T17:57:12Z)
• GoToNet: Fast Monocular Scene Exposure and Exploration [0.6204265638103346]
  We present a novel method for real-time environment exploration. Our method requires only one look (image) to make a good tactical decision. Two direction predictions, characterized by pixels dubbed the Goto and Lookat pixels, comprise the core of our method.
  arXiv  Detail & Related papers  (2022-06-13T08:28:31Z)
• FOVEA: Foveated Image Magnification for Autonomous Navigation [53.69803081925454]
  We propose an attentional approach that elastically magnifies certain regions while maintaining a small input canvas. Our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning. On the autonomous driving datasets Argoverse-HD and BDD100K, we show our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning.
  arXiv  Detail & Related papers  (2021-08-27T03:07:55Z)
• Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers [115.90778814368703]
  Our objective is language-based search of large-scale image and video datasets. For this task, the approach that consists of independently mapping text and vision to a joint embedding space, a.k.a. dual encoders, is attractive as retrieval scales. An alternative approach of using vision-text transformers with cross-attention gives considerable improvements in accuracy over the joint embeddings.
  arXiv  Detail & Related papers  (2021-03-30T17:57:08Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)
• Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes [98.65457534223539]
  We propose a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes. The proposed method achieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU) with the inference speed of 51.0 fps and 39.3 fps.
  arXiv  Detail & Related papers  (2020-03-11T08:45:53Z)

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.