SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking

• URL: http://arxiv.org/abs/2303.04378v1
• Date: Wed, 8 Mar 2023 05:01:00 GMT
• Title: SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking
• Authors: Liangliang Yao, Changhong Fu, Sihang Li, Guangze Zheng, and Junjie Ye
• Abstract summary: This work presents a novel saliency-guided dynamic vision Transformer (SGDViT) for UAV tracking. The proposed method designs a new task-specific object saliency mining network to refine the cross-correlation operation. A lightweight saliency filtering Transformer further refines saliency information and increases the focus on appearance information.
• Score: 12.447854608181833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-based object tracking has boosted extensive autonomous applications for unmanned aerial vehicles (UAVs). However, the dynamic changes in flight maneuver and viewpoint encountered in UAV tracking pose significant difficulties, e.g. , aspect ratio change, and scale variation. The conventional cross-correlation operation, while commonly used, has limitations in effectively capturing perceptual similarity and incorporates extraneous background information. To mitigate these limitations, this work presents a novel saliency-guided dynamic vision Transformer (SGDViT) for UAV tracking. The proposed method designs a new task-specific object saliency mining network to refine the cross-correlation operation and effectively discriminate foreground and background information. Additionally, a saliency adaptation embedding operation dynamically generates tokens based on initial saliency, thereby reducing the computational complexity of the Transformer architecture. Finally, a lightweight saliency filtering Transformer further refines saliency information and increases the focus on appearance information. The efficacy and robustness of the proposed approach have been thoroughly assessed through experiments on three widely-used UAV tracking benchmarks and real-world scenarios, with results demonstrating its superiority. The source code and demo videos are available at https://github.com/vision4robotics/SGDViT.

Related papers

• LaVin-DiT: Large Vision Diffusion Transformer [99.98106406059333]
  LaVin-DiT is a scalable and unified foundation model designed to tackle over 20 computer vision tasks in a generative framework. We introduce key innovations to optimize generative performance for vision tasks. The model is scaled from 0.1B to 3.4B parameters, demonstrating substantial scalability and state-of-the-art performance across diverse vision tasks.
  arXiv  Detail & Related papers  (2024-11-18T12:05:27Z)
• SFTrack: A Robust Scale and Motion Adaptive Algorithm for Tracking Small and Fast Moving Objects [2.9803250365852443]
  This paper addresses the problem of multi-object tracking in Unmanned Aerial Vehicle (UAV) footage. It plays a critical role in various UAV applications, including traffic monitoring systems and real-time suspect tracking by the police. We propose a new tracking strategy, which initiates the tracking of target objects from low-confidence detections.
  arXiv  Detail & Related papers  (2024-10-26T05:09:20Z)
• Learning Motion Blur Robust Vision Transformers with Dynamic Early Exit for Real-Time UAV Tracking [14.382072224997074]
  Single-stream architectures utilizing pre-trained ViT backbones offer improved performance, efficiency, and robustness. We boost the efficiency of this framework by tailoring it into an adaptive framework that dynamically exits Transformer blocks for real-time UAV tracking. We also improve the effectiveness of ViTs in handling motion blur, a common issue in UAV tracking caused by the fast movements of either the UAV, the tracked objects, or both.
  arXiv  Detail & Related papers  (2024-07-07T14:10:04Z)
• Adaptively Bypassing Vision Transformer Blocks for Efficient Visual Tracking [11.361394596302334]
  ABTrack is an adaptive computation framework that adaptively bypassing transformer blocks for efficient visual tracking. We propose a Bypass Decision Module (BDM) to determine if a transformer block should be bypassed. We introduce a novel ViT pruning method to reduce the dimension of the latent representation of tokens in each transformer block.
  arXiv  Detail & Related papers  (2024-06-12T09:39:18Z)
• Rotation Invariant Transformer for Recognizing Object in UAVs [66.1564328237299]
  We propose a novel rotation invariant vision transformer (RotTrans) forRecognizing targets of interest from UAVs. RotTrans greatly outperforms the current state-of-the-arts, which is 5.9% and 4.8% higher than the highest mAP and Rank1. Our solution wins the first place in the UAV-based person re-recognition track in the Multi-Modal Video Reasoning and Analyzing Competition.
  arXiv  Detail & Related papers  (2023-11-05T03:55:08Z)
• AViTMP: A Tracking-Specific Transformer for Single-Branch Visual Tracking [17.133735660335343]
  We propose an Adaptive ViT Model Prediction tracker (AViTMP) to design a customised tracking method. This method bridges the single-branch network with discriminative models for the first time. We show that AViTMP achieves state-of-the-art performance, especially in terms of long-term tracking and robustness.
  arXiv  Detail & Related papers  (2023-10-30T13:48:04Z)
• Leveraging the Power of Data Augmentation for Transformer-based Tracking [64.46371987827312]
  We propose two data augmentation methods customized for tracking. First, we optimize existing random cropping via a dynamic search radius mechanism and simulation for boundary samples. Second, we propose a token-level feature mixing augmentation strategy, which enables the model against challenges like background interference.
  arXiv  Detail & Related papers  (2023-09-15T09:18:54Z)
• Dyna-DepthFormer: Multi-frame Transformer for Self-Supervised Depth Estimation in Dynamic Scenes [19.810725397641406]
  We propose a novel Dyna-Depthformer framework, which predicts scene depth and 3D motion field jointly. Our contributions are two-fold. First, we leverage multi-view correlation through a series of self- and cross-attention layers in order to obtain enhanced depth feature representation. Second, we propose a warping-based Motion Network to estimate the motion field of dynamic objects without using semantic prior.
  arXiv  Detail & Related papers  (2023-01-14T09:43:23Z)
• Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks [88.77951448313486]
  We present a new approach for model acceleration by exploiting spatial sparsity in visual data. We propose a dynamic token sparsification framework to prune redundant tokens. We extend our method to hierarchical models including CNNs and hierarchical vision Transformers.
  arXiv  Detail & Related papers  (2022-07-04T17:00:51Z)
• An Extendable, Efficient and Effective Transformer-based Object Detector [95.06044204961009]
  We integrate Vision and Detection Transformers (ViDT) to construct an effective and efficient object detector. ViDT introduces a reconfigured attention module to extend the recent Swin Transformer to be a standalone object detector. We extend it to ViDT+ to support joint-task learning for object detection and instance segmentation.
  arXiv  Detail & Related papers  (2022-04-17T09:27:45Z)
• ViDT: An Efficient and Effective Fully Transformer-based Object Detector [97.71746903042968]
  Detection transformers are the first fully end-to-end learning systems for object detection. vision transformers are the first fully transformer-based architecture for image classification. In this paper, we integrate Vision and Detection Transformers (ViDT) to build an effective and efficient object detector.
  arXiv  Detail & Related papers  (2021-10-08T06:32:05Z)

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.