Search Results for author:
Found 52 papers, 32 papers with code
Hyperspectral Adapter for Object Tracking based on Hyperspectral Video
However, the performance of hyperspectral trackers is limited by the loss of spectral information during the transformation, and fine-tuning the entire pretrained network is inefficient for practical applications.
M$^3$amba: CLIP-driven Mamba Model for Multi-modal Remote Sensing Classification
Multi-modal fusion holds great promise for integrating information from different modalities.
Computational Efficiency
Hyperspectral Image Classification
+1
DiffCLIP: Few-shot Language-driven Multimodal Classifier
To tackle this issue, we introduce DiffCLIP, a novel framework that extends CLIP to effectively convey comprehensive language-driven semantic information for accurate classification of high-dimensional multimodal remote sensing images.
Towards Accurate and Efficient Sub-8-Bit Integer Training
Our integer training framework includes two components: ShiftQuant to realize accurate gradient estimation, and L1 normalization to smoothen the loss landscape.
An End-to-End Real-World Camera Imaging Pipeline
Nevertheless, the real-world imaging pipeline still faces challenges including the lack of joint optimization in system components, computational redundancies, and optical distortions such as lens shading. In light of this, we propose an end-to-end camera imaging pipeline (RealCamNet) to enhance real-world camera imaging performance.
Beyond Feature Mapping GAP: Integrating Real HDRTV Priors for Superior SDRTV-to-HDRTV Conversion
However, the limited information in SDRTV and the diversity of styles in real-world conversions render this process an ill-posed problem, thereby constraining the performance and generalization of these methods.
SeaDATE: Remedy Dual-Attention Transformer with Semantic Alignment via Contrast Learning for Multimodal Object Detection
Multimodal object detection leverages diverse modal information to enhance the accuracy and robustness of detectors.
AMPO: Automatic Multi-Branched Prompt Optimization
Prompt engineering is very important to enhance the performance of large language models (LLMs).
Unleashing the Power of Generic Segmentation Models: A Simple Baseline for Infrared Small Target Detection
Recent advancements in deep learning have greatly advanced the field of infrared small object detection (IRSTD).
FusionSAM: Latent Space driven Segment Anything Model for Multimodal Fusion and Segmentation
In this paper, we introduce SAM into multimodal image segmentation for the first time, proposing a novel framework that combines Latent Space Token Generation (LSTG) and Fusion Mask Prompting (FMP) modules to enhance SAM's multimodal fusion and segmentation capabilities.
Reducing Spurious Correlation for Federated Domain Generalization
To comprehensively address this challenge, we introduce FedCD (Cross-Domain Invariant Federated Learning), an overall optimization framework at both the local and global levels.
FoRA: Low-Rank Adaptation Model beyond Multimodal Siamese Network
In this paper, we propose a novel multimodal object detector, named Low-rank Modal Adaptors (LMA) with a shared backbone.
IRSAM: Advancing Segment Anything Model for Infrared Small Target Detection
The recent Segment Anything Model (SAM) is a significant advancement in natural image segmentation, exhibiting potent zero-shot performance suitable for various downstream image segmentation tasks.
Beyond Alignment: Blind Video Face Restoration via Parsing-Guided Temporal-Coherent Transformer
Multiple complex degradations are coupled in low-quality video faces in the real world.
Hyperspectral Anomaly Detection with Self-Supervised Anomaly Prior
The majority of existing hyperspectral anomaly detection (HAD) methods use the low-rank representation (LRR) model to separate the background and anomaly components, where the anomaly component is optimized by handcrafted sparse priors (e. g., $\ell_{2, 1}$-norm).
E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection
Multimodal image fusion and object detection are crucial for autonomous driving.
Domain Adaptation for Large-Vocabulary Object Detectors
Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various downstream data.
DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception
Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space.
SwiMDiff: Scene-wide Matching Contrastive Learning with Diffusion Constraint for Remote Sensing Image
Effectively leveraging this data through self-supervised learning (SSL) is vital in the field of remote sensing.
Multimodal Informative ViT: Information Aggregation and Distribution for Hyperspectral and LiDAR Classification
Additionally, the information distribution flow (IDF) in MIVit enhances performance-awareness by distributing global classification information across different modalities' feature maps.
Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image
Accurate cloud recognition and warning are crucial for various applications, including in-flight support, weather forecasting, and climate research.
Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware Detector
Our method provides a new solution space for HAD that is closer to human visual perception with high confidence.
JointSQ: Joint Sparsification-Quantization for Distributed Learning
In this paper we propose Joint Sparsification-Quantization (JointSQ) inspired by the discovery that sparsification can be treated as 0-bit quantization regardless of architectures.
RS-DGC: Exploring Neighborhood Statistics for Dynamic Gradient Compression on Remote Sensing Image Interpretation
The key component of RS-DGC is a Neighborhood Statistical Indicator (NSI), which can quantify the importance of gradients within a specified neighborhood on each node to sparsify the local gradients before gradient transmission in each iteration.
Multi-scale direction-aware SAR object detection network via global information fusion
Deep learning has driven significant progress in object detection using Synthetic Aperture Radar (SAR) imagery.
Physics Inspired Criterion for Pruning-Quantization Joint Learning
Pruning-quantization joint learning always facilitates the deployment of deep neural networks (DNNs) on resource-constrained edge devices.
Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning
Dataset pruning aims to construct a coreset capable of achieving performance comparable to the original, full dataset.
MDFL: Multi-domain Diffusion-driven Feature Learning
High-dimensional images, known for their rich semantic information, are widely applied in remote sensing and other fields.
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients
With the rapid development of imaging sensor technology in the field of remote sensing, multi-modal remote sensing data fusion has emerged as a crucial research direction for land cover classification tasks.
FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion
Multi-satellite, multi-modality in-orbit fusion is a challenging task as it explores the fusion representation of complex high-dimensional data under limited computational resources.
BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection
We present a novel solution BSDM (background suppression diffusion model) for HAD, which can simultaneously learn latent background distributions and generalize to different datasets for suppressing complex background.
Dual Inverse Degradation Network for Real-World SDRTV-to-HDRTV Conversion
In this study, we address the emerging necessity of converting Standard Dynamic Range Television (SDRTV) content into High Dynamic Range Television (HDRTV) in light of the limited number of native HDRTV content.
SAR-to-Optical Image Translation via Thermodynamics-inspired Network
Synthetic aperture radar (SAR) is prevalent in the remote sensing field but is difficult to interpret in human visual perception.
Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank
Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress.
Toward Stable, Interpretable, and Lightweight Hyperspectral Super-Resolution
Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework.
Guided Hybrid Quantization for Object detection in Multimodal Remote Sensing Imagery via One-to-one Self-teaching
More concretely, we first design a structure called guided quantization self-distillation (GQSD), which is an innovative idea for realizing lightweight through the synergy of quantization and distillation.
Ranked #1 on
Object Detection
on VEDAI
DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery
Specifically, the DGSSC comprises three components, a two-stage encoder, a decoder, and a classifier, which are trained in an end-to-end manner.
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
Furthermore, we design a simple and flexible SR branch to learn HR feature representations that can discriminate small objects from vast backgrounds with low-resolution (LR) input, thus further improving the detection accuracy.
Ranked #3 on
Object Detection
on VEDAI
Few-shot Learning with Class-Covariance Metric for Hyperspectral Image Classification
Recently, embedding and metric-based few-shot learning (FSL) has been introduced into hyperspectral image classification (HSIC) and achieved impressive progress.
Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification
In practice, the acquirement of labeled samples for hyperspectral image (HSI) is time-consuming and labor-intensive.
HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution
Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart.
Transcoded Video Restoration by Temporal Spatial Auxiliary Network
In most video platforms, such as Youtube, and TikTok, the played videos usually have undergone multiple video encodings such as hardware encoding by recording devices, software encoding by video editing apps, and single/multiple video transcoding by video application servers.
SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification
To conquer these issues, we propose an efficient symmetric graph metric learning (SGML) framework by incorporating metric learning into the SSGCN paradigm.
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification
In this paper, we propose a novel semi-supervised graph prototypical network (SSGPN) for high-precise HSIC.
Sparse Coding-inspired GAN for Weakly Supervised Hyperspectral Anomaly Detection
For solving these problems, this paper proposes a sparse coding-inspired generative adversarial network (GAN) for weakly supervised HAD, named sparseHAD.
MCM-aware Twin-least-square GAN for Hyperspectral Anomaly Detection
Hyperspectral anomaly detection under high-dimensional data and interference of deteriorated bands without any prior information has been challenging and attracted close attention in the exploration of the unknown in real scenarios.
Multi-direction Networks with Attentional Spectral Prior for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have achieved prominent progress in recent years and demonstrated remarkable properties in spectral-spatial hyperspectral image (HSI) classification.
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification
Recently, multiscale spatial features have been widely utilized to improve the hyperspectral image (HSI) classification performance.
Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image Classification
Recently, convolutional neural networks (CNNs) have attracted enormous attention in pattern recognition and demonstrated excellent performance in hyperspectral image (HSI) classification.
AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images
Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (CNNs).
Robust Interpolation of Correspondences for Large Displacement Optical Flow
In this paper, we present a Robust Interpolation method of Correspondences (called RicFlow) to overcome the weakness.
Efficient Coarse-To-Fine PatchMatch for Large Displacement Optical Flow
Inspired by the nearest neighbor field (NNF) algorithms, our approach, called CPM (Coarse-to-fine PatchMatch), blends an efficient random search strategy with the coarse-to-fine scheme for optical flow problem.
