Search Results for author:
Found 33 papers, 18 papers with code
Inherit with Distillation and Evolve with Contrast: Exploring Class Incremental Semantic Segmentation Without Exemplar Memory
In this paper, we propose to address CISS without exemplar memory and resolve catastrophic forgetting as well as semantic drift synchronously.
Class-Incremental Semantic Segmentation
Contrastive Learning
+2
Zero-shot Text-driven Physically Interpretable Face Editing
Our method can also be flexibly extended to real-time video face editing.
Zero-Shot Image Harmonization with Generative Model Prior
Specifically, in the harmonization process, a human mainly utilizes his long-term prior on harmonious images and makes a composite image close to that prior.
Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation
And the feature diversity is preserved by inter- and intra- class feature diversity-preserved module (InCDP).
RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model
Leveraging vast training data (SA-1B), the foundation Segment Anything Model (SAM) proposed by Meta AI Research exhibits remarkable generalization and zero-shot capabilities.
Remote Sensing Image Change Detection Towards Continuous Bitemporal Resolution Differences
Toward continuous cross-resolution CD, we propose scale-invariant learning to enforce the model consistently predicting HR results given synthesized samples of varying bitemporal resolution differences.
Diffusion Models for Imperceptible and Transferable Adversarial Attack
Many existing adversarial attacks generate $L_p$-norm perturbations on image RGB space.
Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation
In the first stage, we optimize a neural field to encode the color and 3D structure of the remote sensing scene based on multi-view images.
Dense Pixel-to-Pixel Harmonization via Continuous Image Representation
High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing.
Ranked #6 on
Image Harmonization
on iHarmony4
Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation
In our method, taking the power of large-scale pre-trained multi-modal CLIP and neural rendering, T2P searches both continuous facial parameters and discrete facial parameters in a unified framework.
Progressive Scale-aware Network for Remote sensing Image Change Captioning
To sufficiently utilize the extracted multi-scale features for captioning, we propose a scale-aware reinforcement (SR) module and combine it with the Transformer decoding layer to progressively utilize the features from different PDP layers.
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification
In this paper, we propose a universal and lightweight federated learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication.
Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space
Despite its fruitful applications in remote sensing, image super-resolution is troublesome to train and deploy as it handles different resolution magnifications with separate models.
CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation
To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder.
Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection
To achieve this, we obtain multiple points via class-balanced sampling on the overlapped area between views using the semantic mask.
Remote Sensing Novel View Synthesis with Implicit Multiplane Representations
In this paper, we propose a novel remote sensing view synthesis method by leveraging the recent advances in implicit neural representations.
HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images
In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations.
WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Data Augmentation
Weakly supervised Semantic Segmentation
+1
Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast
Tropical cyclone (TC) is an extreme tropical weather system and its trajectory can be described by a variety of spatio-temporal data.
Semantic decoupled representation learning for remote sensing image change detection
Here, we propose a semantic decoupled representation learning for RS image CD.
Building Extraction from Remote Sensing Images with Sparse Token Transformers
Deep learning methods have achieved considerable progress in remote sensing image building extraction.
Building change detection for remote sensing images
Extracting Buildings In Remote Sensing Images
PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning
Histopathological tissue classification is a fundamental task in pathomics cancer research.
Multi-Layer Pseudo-Supervision for Histopathology Tissue Semantic Segmentation using Patch-level Classification Labels
In the segmentation phase, we achieved tissue semantic segmentation by our proposed Multi-Layer Pseudo-Supervision.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
+1
Geographical Knowledge-driven Representation Learning for Remote Sensing Images
The proliferation of remote sensing satellites has resulted in a massive amount of remote sensing images.
Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics
In this article, we treat lung cancer diagnosis as a multiple instance learning (MIL) problem in order to better reflect the diagnosis process in the clinical setting and for the higher interpretability of the output.
Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images
The key of IAug is to blend synthesized building instances onto appropriate positions of one of the bitemporal images.
Ranked #25 on
Building change detection for remote sensing images
on LEVIR-CD
(using extra training data)
Building change detection for remote sensing images
Change Detection
+1
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery
To be specific, we develop a new Feedback Structure and a Local-Global Spectral Block to alleviate the difficulty in spatial and spectral feature extraction.
Remote Sensing Image Change Detection with Transformers
To achieve this, we express the bitemporal image into a few tokens, and use a transformer encoder to model contexts in the compact token-based space-time.
Building change detection for remote sensing images
Change Detection
NeuralMagicEye: Learning to See and Understand the Scene Behind an Autostereogram
This paper studies an interesting question that whether a deep CNN can be trained to recover the depth behind an autostereogram and understand its content.
Stylized Neural Painting
Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of physically meaningful stroke parameters that can be further used for rendering.
A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection
Remote sensing image change detection (CD) is done to identify desired significant changes between bitemporal images.
Face-to-Parameter Translation for Game Character Auto-Creation
Character customization system is an important component in Role-Playing Games (RPGs), where players are allowed to edit the facial appearance of their in-game characters with their own preferences rather than using default templates.
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years.
