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Found 9 papers, 8 papers with code
YYDS: Visible-Infrared Person Re-Identification with Coarse Descriptions
To this end, we present the Refer-VI-ReID settings, which aims to match target visible images from both infrared images and coarse language descriptions (e. g., "a man with red top and black pants") to complement the missing color information.
iKUN: Speak to Trackers without Retraining
Referring multi-object tracking (RMOT) aims to track multiple objects based on input textual descriptions.
Video-based Visible-Infrared Person Re-Identification with Auxiliary Samples
Previous methods focus on learning from cross-modality person images in different cameras.
Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identification
To address this problem, we propose a dynamic clustering and cluster contrastive learning (DCCC) method.
EnsembleMOT: A Step towards Ensemble Learning of Multiple Object Tracking
Multiple Object Tracking (MOT) has rapidly progressed in recent years.
OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval
For the textual representation, one global embedding, three local embeddings and a color-type prompt embedding are extracted to represent various granularities of semantic features.
PAMI-AD: An Activity Detector Exploiting Part-attention and Motion Information in Surveillance Videos
In this work, we propose a comprehensive and effective activity detection system in untrimmed surveillance videos for person-centered and vehicle-centered activities.
StrongSORT: Make DeepSORT Great Again
As a result, the construction of a good baseline for a fair comparison is essential.
Ranked #7 on
Multi-Object Tracking
on MOT17
(using extra training data)
GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021
In recent years, algorithms for multiple object tracking tasks have benefited from great progresses in deep models and video quality.
