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Fangfang Wu

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3 papers

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IJCAI Conference 2025 Conference Paper

PatternCIR Benchmark and TisCIR: Advancing Zero-Shot Composed Image Retrieval in Remote Sensing

Zhechun Liang
Tao Huang
Fangfang Wu
Shiwen Xue
Zhenyu Wang
Weisheng Dong
Xin Li
Guangming Shi

Remote sensing composed image retrieval (RSCIR) is a new vision-language task that takes a composed query of an image and text, aiming to search for a target remote sensing image satisfying two conditions from intricate remote sensing imagery. However, the existing attribute-based benchmark Patterncom in RSCIR has significant flaws, including the lack of query text sentences and paired triplets, thus making it unable to evaluate the latest methods. To address this, we propose the Zero-Shot Query Text Generator (ZS-QTG) that can generate full query text sentences based on attributes, and then, by capitalizing on ZS-QTG, we develop the PatternCIR benchmark. PatternCIR rectifies Patterncom’s deficiencies and enables the evaluation of existing methods. Additionally, we explore zero-shot composed image retrieval methods that do not rely on massive pre-collected triplets for training. Existing methods use only the text during retrieval, performing poorly in RSCIR. To improve this, we propose Text-image Sequential Training of Composed Image Retrieval (TisCIR). TisCIR undergoes sequential training of multiple self-masking projection and fine-grained image attention modules, which endows it with the capacity to filter out conflicting information between the image and text, enhancing the retrieval by utilizing both modalities in harmony. TisCIR outperforms existing methods by 12. 40% to 62. 03% on PatternCIR, achieving state-of-the-art performance in RSCIR. The data and code are available here.

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IJCAI Conference 2022 Conference Paper

Learning Degradation Uncertainty for Unsupervised Real-world Image Super-resolution

Qian Ning
Jingzhu Tang
Fangfang Wu
Weisheng Dong
Xin Li
Guangming Shi

Acquiring degraded images with paired high-resolution (HR) images is often challenging, impeding the advance of image super-resolution in real-world applications. By generating realistic low-resolution (LR) images with degradation similar to that in real-world scenarios, simulated paired LR-HR data can be constructed for supervised training. However, most of the existing work ignores the degradation uncertainty of the generated realistic LR images, since only one LR image has been generated given an HR image. To address this weakness, we propose learning the degradation uncertainty of generated LR images and sampling multiple LR images from the learned LR image (mean) and degradation uncertainty (variance) and construct LR-HR pairs to train the super-resolution (SR) networks. Specifically, uncertainty can be learned by minimizing the proposed loss based on Kullback-Leibler (KL) divergence. Furthermore, the uncertainty in the feature domain is exploited by a novel perceptual loss; and we propose to calculate the adversarial loss from the gradient information in the SR stage for stable training performance and better visual quality. Experimental results on popular real-world datasets show that our proposed method has performed better than other unsupervised approaches.

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AAAI Conference 2020 Conference Paper

Spatial-Temporal Gaussian Scale Mixture Modeling for Foreground Estimation

Qian Ning
Weisheng Dong
Fangfang Wu
Jinjian Wu
Jie Lin
Guangming Shi

Subtracting the backgrounds from the video frames is an important step for many video analysis applications. Assuming that the backgrounds are low-rank and the foregrounds are sparse, the robust principle component analysis (RPCA)based methods have shown promising results. However, the RPCA-based methods suffered from the scale issue, i. e. , the 1-sparsity regularizer fails to model the varying sparsity of the moving objects. While several efforts have been made to address this issue with advanced sparse models, previous methods cannot fully exploit the spatial-temporal correlations among the foregrounds. In this paper, we proposed a novel spatial-temporal Gaussian scale mixture (STGSM) model for foreground estimation. In the proposed STGSM model, a temporal consistent constraint is imposed over the estimated foregrounds through nonzero-means Gaussian models. Speciﬁcally, the estimates of the foregrounds obtained in the previous frame are used as the prior for these of the current frame, and nonzero means Gaussian scale mixture models (GSM) are developed. To better characterize the temporal correlations, the optical ﬂow has been used to model the correspondences between foreground pixels in adjacent frames. The spatial correlations have also been exploited by considering that local correlated pixels should be characterized by the same STGSM model, leading to further performance improvements. Experimental results on real video datasets show that the proposed method performs comparably or even better than current state-of-the-art background subtraction methods.

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