10 citations · 15 across the 5 of their papers we have counts for
5 papers · 1 filter
Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer based Approach
Xixi Wang, Bo Jiang, Xiao Wang +1
Exploring sample relationships within each mini-batch has shown great potential for learning image representations. Existing works generally adopt the regular Transformer to model…
Data Dimension Reduction makes ML Algorithms efficient
Wisal Khan, Muhammad Turab, Waqas Ahmad +3
Data dimension reduction (DDR) is all about mapping data from high dimensions to low dimensions, various techniques of DDR are being used for image dimension reduction like Random…
Unified GCNs: Towards Connecting GCNs with CNNs
Ziyan Zhang, Bo Jiang, Bin Luo
Graph Convolutional Networks (GCNs) have been widely demonstrated their powerful ability in graph data representation and learning. Existing graph convolution layers are mainly des…
Tiny Object Tracking: A Large-scale Dataset and A Baseline
Yabin Zhu, Chenglong Li, Yao Liu +4
Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in meany vision tasks, such as object detection an…
Residual Objectness for Imbalance Reduction
Joya Chen, Dong Liu, Bin Luo +3
For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds. While several sampling/reweighting schemes have been explored to allevia…