34 citations · 82 across the 21 of their papers we have counts for
9 papers · 2 filters
Adversarial Example Defense via Perturbation Grading Strategy
Shaowei Zhu, Wanli Lyu, Bin Li +2
Deep Neural Networks have been widely used in many fields. However, studies have shown that DNNs are easily attacked by adversarial examples, which have tiny perturbations and grea…
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…
LongShortNet: Exploring Temporal and Semantic Features Fusion in Streaming Perception
Chenyang Li, Zhi-Qi Cheng, Jun-Yan He +6
Streaming perception is a critical task in autonomous driving that requires balancing the latency and accuracy of the autopilot system. However, current methods for streaming perce…
ProContEXT: Exploring Progressive Context Transformer for Tracking
Jin-Peng Lan, Zhi-Qi Cheng, Jun-Yan He +6
Existing Visual Object Tracking (VOT) only takes the target area in the first frame as a template. This causes tracking to inevitably fail in fast-changing and crowded scenes, as i…
Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification
Xixi Wang, Xiao Wang, Bo Jiang +1
Few-shot classification which aims to recognize unseen classes using very limited samples has attracted more and more attention. Usually, it is formulated as a metric learning prob…
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…
Universal adversarial perturbation for remote sensing images
Qingyu Wang, Guorui Feng, Zhaoxia Yin +1
Recently, with the application of deep learning in the remote sensing image (RSI) field, the classification accuracy of the RSI has been dramatically improved compared with traditi…