Exploring Driving-aware Salient Object Detection via Knowledge Transfer

• URL: http://arxiv.org/abs/2105.08286v1
• Date: Tue, 18 May 2021 05:24:21 GMT
• Title: Exploring Driving-aware Salient Object Detection via Knowledge Transfer
• Authors: Jinming Su, Changqun Xia, and Jia Li
• Abstract summary: We construct a driving task-oriented dataset where pixel-level masks of salient objects have been annotated. Cross-domain knowledge difference and task-specific scene gap are two main challenges to focus the salient objects when driving. Inspired by these findings, we proposed a baseline model for the driving task-aware SOD via a knowledge transfer convolutional neural network.
• Score: 16.105754712355274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, general salient object detection (SOD) has made great progress with the rapid development of deep neural networks. However, task-aware SOD has hardly been studied due to the lack of task-specific datasets. In this paper, we construct a driving task-oriented dataset where pixel-level masks of salient objects have been annotated. Comparing with general SOD datasets, we find that the cross-domain knowledge difference and task-specific scene gap are two main challenges to focus the salient objects when driving. Inspired by these findings, we proposed a baseline model for the driving task-aware SOD via a knowledge transfer convolutional neural network. In this network, we construct an attentionbased knowledge transfer module to make up the knowledge difference. In addition, an efficient boundary-aware feature decoding module is introduced to perform fine feature decoding for objects in the complex task-specific scenes. The whole network integrates the knowledge transfer and feature decoding modules in a progressive manner. Experiments show that the proposed dataset is very challenging, and the proposed method outperforms 12 state-of-the-art methods on the dataset, which facilitates the development of task-aware SOD.

Related papers

• Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
  Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data. We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
  arXiv  Detail & Related papers  (2024-10-31T06:55:24Z)
• PGNeXt: High-Resolution Salient Object Detection via Pyramid Grafting Network [24.54269823691119]
  We present an advanced study on more challenging high-resolution salient object detection (HRSOD) from both dataset and network framework perspectives. To compensate for the lack of HRSOD dataset, we thoughtfully collect a large-scale high resolution salient object detection dataset, called UHRSD. All the images are finely annotated in pixel-level, far exceeding previous low-resolution SOD datasets.
  arXiv  Detail & Related papers  (2024-08-02T09:31:21Z)
• Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection [101.15777242546649]
  Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection. We present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge.
  arXiv  Detail & Related papers  (2024-06-01T17:32:26Z)
• A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detection [33.30644598646274]
  We propose a simple yet effective network (SENet) based on vision Transformer (ViT) To enhance the Transformer's ability to model local information, we propose a local information capture module (LICM) We also propose a dynamic weighted loss (DW loss) based on Binary Cross-Entropy (BCE) and Intersection over Union (IoU) loss, which guides the network to pay more attention to those smaller and more difficult-to-find target objects.
  arXiv  Detail & Related papers  (2024-02-29T07:29:28Z)
• Evaluating the structure of cognitive tasks with transfer learning [67.22168759751541]
  This study investigates the transferability of deep learning representations between different EEG decoding tasks. We conduct extensive experiments using state-of-the-art decoding models on two recently released EEG datasets.
  arXiv  Detail & Related papers  (2023-07-28T14:51:09Z)
• LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving [12.713417063678335]
  We present a real-time multi-task convolutional neural network for LiDAR-based object detection, semantics, and motion segmentation. We propose a novel Semantic Weighting and Guidance (SWAG) module to transfer semantic features for improved object detection selectively. We achieve state-of-the-art results for two tasks, semantic and motion segmentation, and close to state-of-the-art performance for 3D object detection.
  arXiv  Detail & Related papers  (2023-07-17T21:22:17Z)
• The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection [62.1185839286255]
  Low-cost monocular 3D object detection plays a fundamental role in autonomous driving. We introduce a Dynamic Feature Reflecting Network, named DFR-Net. We rank 1st among all the monocular 3D object detectors in the KITTI test set.
  arXiv  Detail & Related papers  (2021-12-28T07:31:18Z)
• A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding [20.878931360708343]
  We show that it is possible to perform all perception tasks via a simple and efficient multi-task network. Our proposed network, LidarMTL, takes raw LiDAR point cloud as inputs, and predicts six perception outputs for 3D object detection and road understanding.
  arXiv  Detail & Related papers  (2021-03-06T08:00:26Z)
• EDN: Salient Object Detection via Extremely-Downsampled Network [66.38046176176017]
  We introduce an Extremely-Downsampled Network (EDN), which employs an extreme downsampling technique to effectively learn a global view of the whole image. Experiments demonstrate that EDN achieves sArt performance with real-time speed.
  arXiv  Detail & Related papers  (2020-12-24T04:23:48Z)
• Dense Multiscale Feature Fusion Pyramid Networks for Object Detection in UAV-Captured Images [0.09065034043031667]
  We propose a novel method called Dense Multiscale Feature Fusion Pyramid Networks(DMFFPN), which is aimed at obtaining rich features as much as possible. Specifically, the dense connection is designed to fully utilize the representation from the different convolutional layers. Experiments on the drone-based datasets named VisDrone-DET suggest a competitive performance of our method.
  arXiv  Detail & Related papers  (2020-12-19T10:05:31Z)
• Progressive Object Transfer Detection [84.48927705173494]
  We propose a novel Progressive Object Transfer Detection (POTD) framework. First, POTD can leverage various object supervision of different domains effectively into a progressive detection procedure. Second, POTD consists of two delicate transfer stages, i.e., Low-Shot Transfer Detection (LSTD), and Weakly-Supervised Transfer Detection (WSTD)
  arXiv  Detail & Related papers  (2020-02-12T00:16:24Z)

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.