UCM-VeID V2: A Richer Dataset and A Pre-training Method for UAV Cross-Modality Vehicle Re-Identification
Cross-Modality Re-Identification (VI-ReID) aims to achieve around-the-clock target matching, benefiting from the strengths of both RGB and infrared (IR) modalities. However, the field is hindered by limited datasets, particularly for vehicle VI-ReID, and by challenges such as modality bias training (MBT), stemming from biased pre-training on ImageNet. To tackle the above issues, this paper introduces an UCM-VeID V2 dataset benchmark for vehicle VI-ReID, and proposes a new self-supervised pre-training method, Cross-Modality Patch-Mixed Self-supervised Learning (PMSL). UCM-VeID V2 dataset features a significant increase in data volume, along with enhancements in multiple aspects. PMSL addresses MBT by learning modality-invariant features through Patch-Mixed Image Reconstruction (PMIR) and Modality Discrimination Adversarial Learning (MDAL), and enhances discriminability with Modality-Augmented Contrasting Cluster (MACC). Comprehensive experiments are carried out to validate the effectiveness of the proposed method.
Code (0)
등록된 구현이 없습니다.
Tasks
Image ReconstructionSelf-Supervised LearningVehicle Re-IdentificationSimilar Papers 제목 키워드 기반
Beyond Sharing Weights in Decoupling Feature Learning Network for UAV RGB-Infrared Vehicle Re-Identification
Owing to the capacity of performing full-time target search, cross-modality vehicle re-identification (Re-ID) based on unmanned aerial vehicle (UAV) is gaining more attention in both video surveillance and public securit…
Vehicle Re-IdentificationViewpoint and Scale Consistency Reinforcement for UAV Vehicle Re-Identification
This paper studies vehicle ReID in aerial videos taken by Unmanned Aerial Vehicles (UAVs). Compared with existing vehicle ReID tasks performed with fixed surveillance cameras, UAV vehicle ReID is still under-explored and…
Vehicle Re-IdentificationAVEID: Automatic Video System for Measuring Engagement In Dementia
Engagement in dementia is typically measured using behavior observational scales (BOS) that are tedious and involve intensive manual labor to annotate, and are therefore not easily scalable. We propose AVEID, a low cost …
A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and Recognition
In this paper, we propose a variance-preserving interpolation framework to improve diffusion models for single-channel speech enhancement (SE) and automatic speech recognition (ASR). This new variance-preserving interpol…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Speech Enhancementspeech-recognition+1Amplifying Prominent Representations in Multimodal Learning via Variational Dirichlet Process
Developing effective multimodal fusion approaches has become increasingly essential in many real-world scenarios, such as health care and finance. The key challenge is how to preserve the feature expressiveness in each m…
Representation Learning