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Found 13 papers, 6 papers with code
Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
Secondly, a novel pyramidal graph network (PGN) is designed to comprehensively explore the spatial significance of feature maps at multiple scales.
Ranked #3 on
Vehicle Re-Identification
on VehicleID Small
(mAP metric)
A Competitive Method to VIPriors Object Detection Challenge
In this report, we introduce the technical details of our submission to the VIPriors object detection challenge.
GiT: Graph Interactive Transformer for Vehicle Re-identification
In the macro view, a list of GiT blocks are stacked to build a vehicle re-identification model, in where graphs are to extract discriminative local features within patches and transformers are to extract robust global features among patches.
Ranked #2 on
Vehicle Re-Identification
on VehicleID Small
(mAP metric)
A Competitive Method for Dog Nose-print Re-identification
Vision-based pattern identification (such as face, fingerprint, iris etc.)
HSGNet: Object Re-identification with Hierarchical Similarity Graph Network
Secondly, we divide the feature map along with the spatial and channel directions in each hierarchical graph.
Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identification
To address this problem, in this paper, we propose a simple Triplet Contrastive Representation Learning (TCRL) framework which leverages cluster features to bridge the part features and global features for unsupervised vehicle re-identification.
Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision
Although existing image anomaly detection methods yield impressive results, they are mostly an offline learning paradigm that requires excessive data pre-collection, limiting their adaptability in industrial scenarios with online streaming data.
The Second-place Solution for CVPR VISION 23 Challenge Track 1 -- Data Effificient Defect Detection
The Vision Challenge Track 1 for Data-Effificient Defect Detection requires competitors to instance segment 14 industrial inspection datasets in a data-defificient setting.
Exploring the Optimization Objective of One-Class Classification for Anomaly Detection
One-class classification (OCC) is a longstanding method for anomaly detection.
Investigating Shift Equivalence of Convolutional Neural Networks in Industrial Defect Segmentation
In industrial defect segmentation tasks, while pixel accuracy and Intersection over Union (IoU) are commonly employed metrics to assess segmentation performance, the output consistency (also referred to equivalence) of the model is often overlooked.
Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models
Specifically, in the first stage, we design a simple prior conditional diffusion model that predicts the global features of the target image by mining the global alignment relationship between pose coordinates and image appearance.
Low-Complexity Estimation Algorithm and Decoupling Scheme for FRaC System
Hence, a novel decomposed decoupled atomic norm minimization (DANM) method is proposed by splitting the 3D-parameter estimating matrix into multiple 2D matrices with sparsity constraints.
LR-FPN: Enhancing Remote Sensing Object Detection with Location Refined Feature Pyramid Network
To address this, we propose a novel location refined feature pyramid network (LR-FPN) to enhance the extraction of shallow positional information and facilitate fine-grained context interaction.
