Search Results for author:
Found 22 papers, 15 papers with code
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification.
ParC-Net: Position Aware Circular Convolution with Merits from ConvNets and Transformer
Experiment results show that the proposed ParC-Net achieves better performance than popular light-weight ConvNets and vision transformer based models in common vision tasks and datasets, while having fewer parameters and faster inference speed.
Ranked #751 on
Image Classification
on ImageNet
Exploiting temporal consistency for real-time video depth estimation
The temporal consistency loss is combined with the spatial loss to update the model in an end-to-end fashion.
Ranked #5 on
Monocular Depth Estimation
on Mid-Air Dataset
Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image Classification
Compared with search spaces proposed in previous works, the proposed hybrid search space is more aligned with the characteristic of HSI data, that is, HSIs have a relatively low spatial resolution and an extremely high spectral resolution.
Fcaformer: Forward Cross Attention in Hybrid Vision Transformer
Currently, one main research line in designing a more efficient vision transformer is reducing the computational cost of self attention modules by adopting sparse attention or using local attention windows.
Bridging Sensor Gaps via Single-Direction Tuning for Hyperspectral Image Classification
In this paper, aiming to solve this problem, we propose the single-direction tuning (SDT) strategy, which serves as a bridge, allowing us to leverage existing labeled HSI datasets even RGB datasets to enhance the performance on new HSI datasets with limited samples.
Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning
In contrast to previous approaches, we do not impose restrictions over the source data sets, in which they do not have to be collected by the same sensors as the target data sets.
Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising
We also present analysis on the architectures found by NAS.
Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration
For the inner search space, we propose a layer-wise architecture sharing strategy (LWAS), resulting in more flexible architectures and better performance.
3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification
Correspondingly, different models need to be designed for different datasets, which further increases the workload of designing architectures; 2) the mainstream framework is a patch-to-pixel framework.
NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction
In this paper, we propose a neural architecture representation model that can be used to estimate these attributes holistically.
Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years.
ParCNetV2: Oversized Kernel with Enhanced Attention
Specifically, we propose a new convolutional neural network, ParCNetV2, that extends position-aware circular convolution (ParCNet) with oversized convolutions and bifurcate gate units to enhance attention.
NAR-Former V2: Rethinking Transformer for Universal Neural Network Representation Learning
It can learn efficient representations from both cell-structured networks and entire networks.
RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks
Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition.
Meta Learning with Differentiable Closed-form Solver for Fast Video Object Segmentation
This paper tackles the problem of video object segmentation.
Gradient Information Guided Deraining with A Novel Network and Adversarial Training
However, the existing methods usually do not have good generalization ability, which leads to the fact that almost all of existing methods have a satisfied performance on removing a specific type of rain streaks, but may have a relatively poor performance on other types of rain streaks.
Hyperspectral Image Classification with Spatial Consistence Using Fully Convolutional Spatial Propagation Network
However, the existing CNN-based models operate at the patch-level, in which pixel is separately classified into classes using a patch of images around it.
Pseudo-LiDAR Based Road Detection
Specifically, we exploit pseudo-LiDAR using depth estimation, and propose a feature fusion network where RGB and learned depth information are fused for improved road detection.
Connecting Compression Spaces with Transformer for Approximate Nearest Neighbor Search
Specifically, based on transformer, we propose a new network structure to compress the feature into a low dimensional space, and an inhomogeneous neighborhood relationship preserving (INRP) loss that aims to maintain high search accuracy.
Fast-ParC: Capturing Position Aware Global Feature for ConvNets and ViTs
Transformer models have made tremendous progress in various fields in recent years.
Teacher Agent: A Knowledge Distillation-Free Framework for Rehearsal-based Video Incremental Learning
Rehearsal-based video incremental learning often employs knowledge distillation to mitigate catastrophic forgetting of previously learned data.
