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Found 13 papers, 5 papers with code
MLLMReID: Multimodal Large Language Model-based Person Re-identification
This paper will investigate how to adapt them for the task of ReID.
ProtoHPE: Prototype-guided High-frequency Patch Enhancement for Visible-Infrared Person Re-identification
In contrast, we find that some cross-modal correlated high-frequency components contain discriminative visual patterns and are less affected by variations such as wavelength, pose, and background clutter than holistic images.
PHA: Patch-Wise High-Frequency Augmentation for Transformer-Based Person Re-Identification
Although recent studies empirically show that injecting Convolutional Neural Networks (CNNs) into Vision Transformers (ViTs) can improve the performance of person re-identification, the rationale behind it remains elusive.
CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion
The previous knowledge graph embedding (KGE) techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction, limiting KGC's performance.
Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference
Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models.
Entity Concept-enhanced Few-shot Relation Extraction
Few-shot relation extraction (FSRE) is of great importance in long-tail distribution problem, especially in special domain with low-resource data.
Spatiotemporal Transformer for Video-based Person Re-identification
Recently, the Transformer module has been transplanted from natural language processing to computer vision.
UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification
The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains.
Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference
Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering.
AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding
Recent advances in Knowledge Graph Embedding (KGE) allow for representing entities and relations in continuous vector spaces.
Rule-Guided Compositional Representation Learning on Knowledge Graphs
Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces.
Background Segmentation for Vehicle Re-Identification
Vehicle re-identification (Re-ID) is very important in intelligent transportation and video surveillance. Prior works focus on extracting discriminative features from visual appearance of vehicles or using visual-spatio-temporal information. However, background interference in vehicle re-identification have not been explored. In the actual large-scale spatio-temporal scenes, the same vehicle usually appears in different backgrounds while different vehicles might appear in the same background, which will seriously affect the re-identification performance.
Single Camera Training for Person Re-identification
Differently, this paper investigates ReID in an unexplored single-camera-training (SCT) setting, where each person in the training set appears in only one camera.
