In this paper, we propose to address CISS without exemplar memory and resolve catastrophic forgetting as well as semantic drift synchronously.
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.
1 code implementation • 12 Jul 2023 • Hao Wang, Jiatai Lin, Danyi Li, Jing Wang, Bingchao Zhao, Zhenwei Shi, Xipeng Pan, Huadeng Wang, Bingbing Li, Changhong Liang, Guoqiang Han, Li Liang, Chu Han, Zaiyi Liu
And the feature diversity is preserved by inter- and intra- class feature diversity-preserved module (InCDP).
Leveraging vast training data (SA-1B), the foundation Segment Anything Model (SAM) proposed by Meta AI Research exhibits remarkable generalization and zero-shot capabilities.
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.
Many existing adversarial attacks generate $L_p$-norm perturbations on image RGB space.
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.
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
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.
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.
1 code implementation • 24 Feb 2023 • Tianpeng Deng, Yanqi Huang, Zhenwei Shi, Jiatai Lin, Qi Dou, Ke Zhao, Fang-Fang Liu, Yu-Mian Jia, Jin Wang, Bingchao Zhao, Changhong Liang, Zaiyi Liu, Xiao-jing Guo, Guoqiang Han, Xin Chen, Chu Han
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.
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.
no code implementations • 15 Jul 2022 • Jianwei Lin, Jiatai Lin, Cheng Lu, Hao Chen, Huan Lin, Bingchao Zhao, Zhenwei Shi, Bingjiang Qiu, Xipeng Pan, Zeyan Xu, Biao Huang, Changhong Liang, Guoqiang Han, Zaiyi Liu, Chu Han
To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder.
To achieve this, we obtain multiple points via class-balanced sampling on the overlapped area between views using the semantic mask.
In this paper, we propose a novel remote sensing view synthesis method by leveraging the recent advances in implicit neural representations.
no code implementations • 17 May 2022 • Yuhao Mo, Chu Han, Yu Liu, Min Liu, Zhenwei Shi, Jiatai Lin, Bingchao Zhao, Chunwang Huang, Bingjiang Qiu, Yanfen Cui, Lei Wu, Xipeng Pan, Zeyan Xu, Xiaomei Huang, Zaiyi Liu, Ying Wang, Changhong Liang
In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations.
no code implementations • 13 Apr 2022 • Chu Han, Xipeng Pan, Lixu Yan, Huan Lin, Bingbing Li, Su Yao, Shanshan Lv, Zhenwei Shi, Jinhai Mai, Jiatai Lin, Bingchao Zhao, Zeyan Xu, Zhizhen Wang, Yumeng Wang, Yuan Zhang, Huihui Wang, Chao Zhu, Chunhui Lin, Lijian Mao, Min Wu, Luwen Duan, Jingsong Zhu, Dong Hu, Zijie Fang, Yang Chen, Yongbing Zhang, Yi Li, Yiwen Zou, Yiduo Yu, Xiaomeng Li, Haiming Li, Yanfen Cui, Guoqiang Han, Yan Xu, Jun Xu, Huihua Yang, Chunming Li, Zhenbing Liu, Cheng Lu, Xin Chen, Changhong Liang, Qingling Zhang, Zaiyi Liu
According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.
Tropical cyclone (TC) is an extreme tropical weather system and its trajectory can be described by a variety of spatio-temporal data.
Here, we propose a semantic decoupled representation learning for RS image CD.
Deep learning methods have achieved considerable progress in remote sensing image building extraction.
no code implementations • 4 Nov 2021 • Jiatai Lin, Guoqiang Han, Xipeng Pan, Hao Chen, Danyi Li, Xiping Jia, Zhenwei Shi, Zhizhen Wang, Yanfen Cui, Haiming Li, Changhong Liang, Li Liang, Zaiyi Liu, Chu Han
Histopathological tissue classification is a fundamental task in pathomics cancer research.
1 code implementation • 14 Oct 2021 • Chu Han, Jiatai Lin, Jinhai Mai, Yi Wang, Qingling Zhang, Bingchao Zhao, Xin Chen, Xipeng Pan, Zhenwei Shi, Xiaowei Xu, Su Yao, Lixu Yan, Huan Lin, Zeyan Xu, Xiaomei Huang, Guoqiang Han, Changhong Liang, Zaiyi Liu
In the segmentation phase, we achieved tissue semantic segmentation by our proposed Multi-Layer Pseudo-Supervision.
The proliferation of remote sensing satellites has resulted in a massive amount of remote sensing images.
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.
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)
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.
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.
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.
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.
Remote sensing image change detection (CD) is done to identify desired significant changes between bitemporal images.
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, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years.