Search Results for author:
Found 10 papers, 7 papers with code
Physics Inspired Hybrid Attention for SAR Target Recognition
There has been a recent emphasis on integrating physical models and deep neural networks (DNNs) for SAR target recognition, to improve performance and achieve a higher level of physical interpretability.
Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation
We decompose the query video information into a clip prototype and a memory prototype for capturing local and long-term internal temporal guidance, respectively.
Progressively Dual Prior Guided Few-shot Semantic Segmentation
Few-shot semantic segmentation task aims at performing segmentation in query images with a few annotated support samples.
Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation
To solve this problem, we are the first to introduce an intermediate prototype for mining both deterministic category information from the support and adaptive category knowledge from the query.
Ranked #29 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
Towards Large-Scale Small Object Detection: Survey and Benchmarks
Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively.
Learning Non-target Knowledge for Few-shot Semantic Segmentation
Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO-free target object segmentation result.
Weakly Supervised Rotation-Invariant Aerial Object Detection Network
Object rotation is among long-standing, yet still unexplored, hard issues encountered in the task of weakly supervised object detection (WSOD) from aerial images.
Physically Explainable CNN for SAR Image Classification
In this paper, we first propose a novel physically explainable convolutional neural network for SAR image classification, namely physics guided and injected learning (PGIL).
Oriented R-CNN for Object Detection
Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes.
Ranked #10 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning
That is, we seamlessly embed various intra-view information, cross-view multi-dimension bilinear interactive information, and a new view ensemble mechanism into a unified framework to make a decision via the optimization.
