Search Results for author:
Found 14 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.
AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes
This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA -- the adaptive horizontal pod auto-scaling system.
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.
Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment
The goal of sequential event prediction is to estimate the next event based on a sequence of historical events, with applications to sequential recommendation, user behavior analysis and clinical treatment.
NetRCA: An Effective Network Fault Cause Localization Algorithm
Localizing the root cause of network faults is crucial to network operation and maintenance.
Oriented Feature Alignment for Fine-grained Object Recognition in High-Resolution Satellite Imagery
In this technical report, we analyzed the key issues of fine-grained object recognition, and use an oriented feature alignment network (OFA-Net) to achieve high-performance fine-grained oriented object recognition in optical remote sensing images.
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)
Conditional Gradient Methods for Convex Optimization with General Affine and Nonlinear Constraints
Conditional gradient methods have attracted much attention in both machine learning and optimization communities recently.
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.
Dynamic Stochastic Approximation for Multi-stage Stochastic Optimization
We show that DSA can achieve an optimal ${\cal O}(1/\epsilon^4)$ rate of convergence in terms of the total number of required scenarios when applied to a three-stage stochastic optimization problem.
Algorithms for stochastic optimization with functional or expectation constraints
We then present a variant of CSA, namely the cooperative stochastic parameter approximation (CSPA) algorithm, to deal with the situation when the constraint is defined over problem parameters and show that it exhibits similar optimal rate of convergence to CSA.
