Search Results for author:
Found 35 papers, 6 papers with code
SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance.
Physics-informed deep diffusion MRI reconstruction: break the bottleneck of training data in artificial intelligence
In this work, we propose a Physics-Informed Deep Diffusion magnetic resonance imaging (DWI) reconstruction method (PIDD).
Optimizing Crop Management with Reinforcement Learning and Imitation Learning
Moreover, the partial-observation management policies are directly deployable in the real world as they use readily available information.
LO-Det: Lightweight Oriented Object Detection in Remote Sensing Images
Specifically, a channel separation-aggregation (CSA) structure is designed to simplify the complexity of stacked separable convolutions, and a dynamic receptive field (DRF) mechanism is developed to maintain high accuracy by customizing the convolution kernel and its perception range dynamically when reducing the network complexity.
Task-wise Sampling Convolutions for Arbitrary-Oriented Object Detection in Aerial Images
However, the inconsistent features for the localization and classification tasks in AOOD models may lead to ambiguity and low-quality object predictions, which constrains the detection performance.
Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification
Text information including extensive prior knowledge about land cover classes has been ignored in hyperspectral image classification (HSI) tasks.
Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification
Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing attention.
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets
Beyond classification, Conv-Adapter can generalize to detection and segmentation tasks with more than 50% reduction of parameters but comparable performance to the traditional full fine-tuning.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
MPANet: Multi-Patch Attention For Infrared Small Target object Detection
To this end, a multi-patch attention network (MPANet) based on the axial-attention encoder and the multi-scale patch branch (MSPB) structure is proposed.
Optimizing Nitrogen Management with Deep Reinforcement Learning and Crop Simulations
Nitrogen (N) management is critical to sustain soil fertility and crop production while minimizing the negative environmental impact, but is challenging to optimize.
Powering Finetuning in Few-Shot Learning: Domain-Agnostic Bias Reduction with Selected Sampling
Class-agnostic bias is defined as the distribution shifting introduced by domain difference, which we propose Distribution Calibration Module(DCM) to reduce.
MS-HLMO: Multi-scale Histogram of Local Main Orientation for Remote Sensing Image Registration
Considering the characteristics and differences of multi-source remote sensing images, a feature-based registration algorithm named Multi-scale Histogram of Local Main Orientation (MS-HLMO) is proposed.
A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging
Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio.
DeepSSN: a deep convolutional neural network to assess spatial scene similarity
Spatial-query-by-sketch is an intuitive tool to explore human spatial knowledge about geographic environments and to support communication with scene database queries.
Deep Learning for UAV-based Object Detection and Tracking: A Survey
Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS).
A General Gaussian Heatmap Label Assignment for Arbitrary-Oriented Object Detection
Specifically, an anchor-free object-adaptation label assignment (OLA) strategy is presented to define the positive candidates based on two-dimensional (2-D) oriented Gaussian heatmaps, which reflect the shape and direction features of arbitrary-oriented objects.
Ranked #24 on
Object Detection In Aerial Images
on DOTA
Dynamic Proximal Unrolling Network for Compressive Imaging
In this paper, a dynamic proximal unrolling network (dubbed DPUNet) was proposed, which can handle a variety of measurement matrices via one single model without retraining.
Unsupervised Disentanglement of Linear-Encoded Facial Semantics
We propose a method to disentangle linear-encoded facial semantics from StyleGAN without external supervision.
Improve Novel Class Generalization By Adaptive Feature Distribution for Few-Shot Learning
By addressing the difference between feature distributions of base and novel classes, we propose the adaptive feature distribution method which is to finetune one scale vector using the support set of novel classes.
Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network
In addition, the generalization ability of Ms-AFt in dense remote sensing scenes is further verified on stereo aerial imagery of a large camping site.
Model Decay in Long-Term Tracking
Updating the tracker model with adverse bounding box predictions adds an unavoidable bias term to the learning.
Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection
To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB).
Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification
Particularly, long short-term memory (LSTM), as a special deep learning structure, has shown great ability in modeling long-term dependencies in the time dimension of video or the spectral dimension of HSIs.
ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features
To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning, fast image pyramid matching, and boosting strategy.
Estimating Small Differences in Car-Pose from Orbits
The generated orbit in the latent space records all the differences in pose in the original observational space, and as a result, the method is capable of finding subtle differences in pose.
Long-term Tracking in the Wild: A Benchmark
We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms.
Seeing Small Faces from Robust Anchor's Perspective
This paper introduces a novel anchor design to support anchor-based face detection for superior scale-invariant performance, especially on tiny faces.
Tracking for Half an Hour
Long-term tracking requires extreme stability to the multitude of model updates and robustness to the disappearance and loss of the target as such will inevitably happen.
Tracking by Natural Language Specification
This paper strives to track a target object in a video.
Ranked #14 on
Referring Expression Segmentation
on J-HMDB
Generic Instance Search and Re-identification from One Example via Attributes and Categories
Searching among instances from the same category as the query, the category-specific attributes outperform existing approaches by a large margin on shoes and cars and perform on par with the state-of-the-art on buildings.
Siamese Instance Search for Tracking
In this paper we present a tracker, which is radically different from state-of-the-art trackers: we apply no model updating, no occlusion detection, no combination of trackers, no geometric matching, and still deliver state-of-the-art tracking performance, as demonstrated on the popular online tracking benchmark (OTB) and six very challenging YouTube videos.
Attributes and Categories for Generic Instance Search From One Example
This paper aims for generic instance search from one example where the instance can be an arbitrary 3D object like shoes, not just near-planar and one-sided instances like buildings and logos.
Locality in Generic Instance Search from One Example
This paper aims for generic instance search from a single example.
