30 citations · 106 across the 17 of their papers we have counts for
17 papers
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…
Self-supervised Feature-Gate Coupling for Dynamic Network Pruning
Mengnan Shi, Chang Liu, Jianbin Jiao +1
Gating modules have been widely explored in dynamic network pruning to reduce the run-time computational cost of deep neural networks while preserving the representation of feature…
Learnable Expansion-and-Compression Network for Few-shot Class-Incremental Learning
Boyu Yang, Mingbao Lin, Binghao Liu +4
Few-shot class-incremental learning (FSCIL), which targets at continuously expanding model's representation capacity under few supervisions, is an important yet challenging problem…
Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection
Bohao Li, Boyu Yang, Chang Liu +3
Few-shot object detection has made substantial progressby representing novel class objects using the feature representation learned upon a set of base class objects. However,an imp…
Towards Spatio-Temporal Video Scene Text Detection via Temporal Clustering
Yuanqiang Cai, Chang Liu, Weiqiang Wang +1
With only bounding-box annotations in the spatial domain, existing video scene text detection (VSTD) benchmarks lack temporal relation of text instances among video frames, which h…
Adaptive Linear Span Network for Object Skeleton Detection
Chang Liu, Yunjie Tian, Jianbin Jiao +1
Conventional networks for object skeleton detection are usually hand-crafted. Although effective, they require intensive priori knowledge to configure representative features for o…
Prototype Mixture Models for Few-shot Semantic Segmentation
Boyu Yang, Chang Liu, Bohao Li +2
Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose. Using a single prototype acquired direct…
Progressive Cluster Purification for Unsupervised Feature Learning
Yifei Zhang, Chang Liu, Yu Zhou +3
In unsupervised feature learning, sample specificity based methods ignore the inter-class information, which deteriorates the discriminative capability of representation models. Cl…
Discretization-Aware Architecture Search
Yunjie Tian, Chang Liu, Lingxi Xie +2
The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods. These methods optimize a super-network with all possible edges and operation…
Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning
Yuan Yao, Chang Liu, Dezhao Luo +2
In self-supervised spatio-temporal representation learning, the temporal resolution and long-short term characteristics are not yet fully explored, which limits representation capa…
C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
Fang Wan, Chang Liu, Wei Ke +3
Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detec…
Linear Span Network for Object Skeleton Detection
Chang Liu, Wei Ke, Fei Qin +1
Robust object skeleton detection requires to explore rich representative visual features and effective feature fusion strategies. In this paper, we first re-visit the implementatio…