Transreid: Transformer-based object re-identification
Extracting robust feature representation is one of the key challenges in object re-
identification (ReID). Although convolution neural network (CNN)-based methods have …
identification (ReID). Although convolution neural network (CNN)-based methods have …
Dual cross-attention learning for fine-grained visual categorization and object re-identification
Recently, self-attention mechanisms have shown impressive performance in various NLP
and CV tasks, which can help capture sequential characteristics and derive global …
and CV tasks, which can help capture sequential characteristics and derive global …
Transformer for object re-identification: A survey
Abstract Object Re-identification (Re-ID) aims to identify specific objects across different
times and scenes, which is a widely researched task in computer vision. For a prolonged …
times and scenes, which is a widely researched task in computer vision. For a prolonged …
TBE-Net: A three-branch embedding network with part-aware ability and feature complementary learning for vehicle re-identification
Vehicle re-identification (Re-ID) is one of the promising applications in the field of computer
vision. Existing vehicle Re-ID methods mainly focus on global appearance features or pre …
vision. Existing vehicle Re-ID methods mainly focus on global appearance features or pre …
Gan-siamese network for cross-domain vehicle re-identification in intelligent transport systems
Z Zhou, Y Li, J Li, K Yu, G Kou, M Wang… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
The vehicle re-identification (Re-ID) has become one of most important techniques for
tracking vehicles in intelligent transport system. Vehicle Re-ID aims at matching identical …
tracking vehicles in intelligent transport system. Vehicle Re-ID aims at matching identical …
Git: Graph interactive transformer for vehicle re-identification
Transformers are more and more popular in computer vision, which treat an image as a
sequence of patches and learn robust global features from the sequence. However, pure …
sequence of patches and learn robust global features from the sequence. However, pure …
Adaptive sparse pairwise loss for object re-identification
X Zhou, Y Zhong, Z Cheng… - Proceedings of the …, 2023 - openaccess.thecvf.com
Object re-identification (ReID) aims to find instances with the same identity as the given
probe from a large gallery. Pairwise losses play an important role in training a strong ReID …
probe from a large gallery. Pairwise losses play an important role in training a strong ReID …
CLIP-ReID: exploiting vision-language model for image re-identification without concrete text labels
Pre-trained vision-language models like CLIP have recently shown superior performances
on various downstream tasks, including image classification and segmentation. However, in …
on various downstream tasks, including image classification and segmentation. However, in …
Accurate fine-grained object recognition with structure-driven relation graph networks
Fine-grained object recognition (FGOR) aims to learn discriminative features that can
identify the subtle distinctions between visually similar objects. However, less effort has …
identify the subtle distinctions between visually similar objects. However, less effort has …
Cross-directional consistency network with adaptive layer normalization for multi-spectral vehicle re-identification and a high-quality benchmark
To tackle the challenge of vehicle re-identification (Re-ID) in complex lighting environments
and diverse scenes, multi-spectral sources like visible and infrared information are taken …
and diverse scenes, multi-spectral sources like visible and infrared information are taken …