Search Results for author:
Found 12 papers, 4 papers with code
GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly Detection
In recent years, video anomaly detection has been extensively investigated in both unsupervised and weakly supervised settings to alleviate costly temporal labeling.
Spatial Cascaded Clustering and Weighted Memory for Unsupervised Person Re-identification
We introduce the Spatial Cascaded Clustering and Weighted Memory (SCWM) method to address these challenges.
DMRNet++: Learning Discriminative Features with Decoupled Networks and Enriched Pairs for One-Step Person Search
Person search aims at localizing and recognizing query persons from raw video frames, which is a combination of two sub-tasks, i. e., pedestrian detection and person re-identification.
Ranked #3 on
Person Search
on PRW
Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining
The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO.
Ranked #8 on
Object Detection
on COCO test-dev
Content-Variant Reference Image Quality Assessment via Knowledge Distillation
The comparisons of distribution differences between HQ and LQ images can help our model better assess the image quality.
Multi-Centroid Representation Network for Domain Adaptive Person Re-ID
First, we present a Domain-Specific Contrastive Learning (DSCL) mechanism to fully explore intradomain information by comparing samples only from the same domain.
Contrastive Learning
Domain Adaptive Person Re-Identification
+2
Modality-Aware Triplet Hard Mining for Zero-shot Sketch-Based Image Retrieval
By combining two fundamental learning approaches in DML, e. g., classification training and pairwise training, we set up a strong baseline for ZS-SBIR.
Weakly Supervised Person Search with Region Siamese Networks
Large-scale labeled training data is often difficult to collect, especially for person identities.
Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search
Specifically, to reconcile the conflicts of multiple objectives, we simplify the standard tightly coupled pipelines and establish a deeply decoupled multi-task learning framework.
Ranked #8 on
Person Search
on PRW
Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians
In the conventional person Re-ID setting, it is widely assumed that cropped person images are for each individual.
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective
If a sample belongs to a tail class, the corresponding feature cloud will have relatively large distribution range, in compensation to its lack of diversity.
Re-ID Driven Localization Refinement for Person Search
The state-of-the-art methods train the detector individually, and the detected bounding boxes may be sub-optimal for the following re-ID task.
