30 citations · 106 across the 17 of their papers we have counts for
6 papers · 1 filter
DQnet: Cross-Model Detail Querying for Camouflaged Object Detection
Wei Sun, Chengao Liu, Linyan Zhang +6
Camouflaged objects are seamlessly blended in with their surroundings, which brings a challenging detection task in computer vision. Optimizing a convolutional neural network (CNN)…
Proposal Distribution Calibration for Few-Shot Object Detection
Bohao Li, Chang Liu, Mengnan Shi +3
Adapting object detectors learned with sufficient supervision to novel classes under low data regimes is charming yet challenging. In few-shot object detection (FSOD), the two-step…
Local Magnification for Data and Feature Augmentation
Kun He, Chang Liu, Stephen Lin +1
In recent years, many data augmentation techniques have been proposed to increase the diversity of input data and reduce the risk of overfitting on deep neural networks. In this wo…
Local Manifold Augmentation for Multiview Semantic Consistency
Yu Yang, Wing Yin Cheung, Chang Liu +1
Multiview self-supervised representation learning roots in exploring semantic consistency across data of complex intra-class variation. Such variation is not directly accessible an…
Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning
Yifei Zhang, Chang Liu, Yu Zhou +3
Contrastive self-supervised learning (CSL) based on instance discrimination typically attracts positive samples while repelling negatives to learn representations with pre-defined…
ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation
Qiran Zou, Yu Yang, Wing Yin Cheung +2
Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially lay…