Search Results for author:
Found 7 papers, 6 papers with code
Infrared and visible Image Fusion with Language-driven Loss in CLIP Embedding Space
A language-driven fusion model is then constructed in the embedding space, by establishing the relationship among the embedded vectors to represent the fusion objective and input image modalities.
Semantic Object-level Modeling for Robust Visual Camera Relocalization
Due to the improvement of CNN-based object detection algorithm, the robustness of visual relocalization is greatly enhanced especially in viewpoints where classical methods fail.
Deep Dive Into Gradients: Better Optimization for 3D Object Detection With Gradient-Corrected IoU Supervision
In this paper, we propose a Gradient-Corrected IoU (GCIoU) loss to achieve fast and accurate 3D bounding box regression.
Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss
In this paper, we propose a Representation Invariance Loss (RIL) to optimize the bounding box regression for the rotating objects.
Ranked #27 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images
The proposed framework creates more powerful semantic representations for objects in remote sensing images and achieves high-performance real-time object detection.
Ranked #44 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Dynamic Anchor Learning for Arbitrary-Oriented Object Detection
With the newly introduced DAL, we achieve superior detection performance for arbitrary-oriented objects with only a few horizontal preset anchors.
Ranked #1 on
Multi-Oriented Scene Text Detection
on ICDAR2015
(using extra training data)
A Novel CNN-based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box
By contrast, we are able to predict the orientation and other variables independently, and yet more effectively, with a novel dual-branch regression network, based on the observation that the ship targets are nearly rotation-invariant in remote sensing images.
