Yiyang Shen

Yiyang Shen, Mingqiang Wei, Sen Deng et al.

The intricacy of rainy image contents often leads cutting-edge deraining models to image degradation including remnant rain, wrongly-removed details, and distorted appearance. Such degradation is further exacerbated when applying the models trained on synthetic data to real-world rainy images. We observe two types of domain gaps between synthetic and real-world rainy images: one exists in rain streak patterns; the other is the pixel-level appearance of rain-free images. To bridge the two domain gaps, we propose a semi-supervised detail-recovery image deraining network (Semi-DRDNet) with dual sample-augmented contrastive learning. Semi-DRDNet consists of three sub-networks:i) for removing rain streaks without remnants, we present a squeeze-and-excitation based rain residual network; ii) for encouraging the lost details to return, we construct a structure detail context aggregation based detail repair network; to our knowledge, this is the first time; and iii) for building efficient contrastive constraints for both rain streaks and clean backgrounds, we exploit a novel dual sample-augmented contrastive regularization network.Semi-DRDNet operates smoothly on both synthetic and real-world rainy data in terms of deraining robustness and detail accuracy. Comparisons on four datasets including our established Real200 show clear improvements of Semi-DRDNet over fifteen state-of-the-art methods. Code and dataset are available at https://github.com/syy-whu/DRD-Net.

Yiyang Shen, Mingqiang Wei, Yongzhen Wang et al.

Recent diffusion models have exhibited great potential in generative modeling tasks. Part of their success can be attributed to the ability of training stable on huge sets of paired synthetic data. However, adapting these models to real-world image deraining remains difficult for two aspects. First, collecting a large-scale paired real-world clean/rainy dataset is unavailable while regular conditional diffusion models heavily rely on paired data for training. Second, real-world rain usually reflects real-world scenarios with a variety of unknown rain degradation types, which poses a significant challenge for the generative modeling process. To meet these challenges, we propose RainDiff, the first real-world image deraining paradigm based on diffusion models, serving as a new standard bar for real-world image deraining. We address the first challenge by introducing a stable and non-adversarial unpaired cycle-consistent architecture that can be trained, end-to-end, with only unpaired data for supervision; and the second challenge by proposing a degradation-conditioned diffusion model that refines the desired output via a diffusive generative process conditioned by learned priors of multiple rain degradations. Extensive experiments confirm the superiority of our RainDiff over existing unpaired/semi-supervised methods and show its competitive advantages over several fully-supervised ones.

Yiyang Shen, Rongwei Yu, Peng Wu et al.

LiDAR and camera, as two different sensors, supply geometric (point clouds) and semantic (RGB images) information of 3D scenes. However, it is still challenging for existing methods to fuse data from the two cross sensors, making them complementary for quality 3D object detection (3OD). We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discrepancies by progressively fusing the multi-scale features of camera Images and LiDAR point clouds. ImLiDAR enables to provide the detection head with cross-sensor yet robustly fused features. To achieve this, two core designs exist in ImLiDAR. First, we propose a cross-sensor dynamic message propagation module to combine the best of the multi-scale image and point features. Second, we raise a direct set prediction problem that allows designing an effective set-based detector to tackle the inconsistency of the classification and localization confidences, and the sensitivity of hand-tuned hyperparameters. Besides, the novel set-based detector can be detachable and easily integrated into various detection networks. Comparisons on both the KITTI and SUN-RGBD datasets show clear visual and numerical improvements of our ImLiDAR over twenty-three state-of-the-art 3OD methods.

Yiyang Shen, Yongzhen Wang, Mingqiang Wei et al.

Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions. We observe that: (i) rain is a mixture of rain streaks and rainy haze; (ii) the scene depth determines the intensity of rain streaks and the transformation into the rainy haze; (iii) most existing deraining methods are only trained on synthetic rainy images, and hence generalize poorly to the real-world scenes. Motivated by these observations, we propose a new SEMI-supervised Mixture Of rain REmoval Generative Adversarial Network (Semi-MoreGAN), which consists of four key modules: (I) a novel attentional depth prediction network to provide precise depth estimation; (ii) a context feature prediction network composed of several well-designed detailed residual blocks to produce detailed image context features; (iii) a pyramid depth-guided non-local network to effectively integrate the image context with the depth information, and produce the final rain-free images; and (iv) a comprehensive semi-supervised loss function to make the model not limited to synthetic datasets but generalize smoothly to real-world heavy rainy scenes. Extensive experiments show clear improvements of our approach over twenty representative state-of-the-arts on both synthetic and real-world rainy images.

Baian Chen, Lipeng Gu, Xin Zhuang et al.

We propose PSFormer, an effective point transformer model for 3D salient object detection. PSFormer is an encoder-decoder network that takes full advantage of transformers to model the contextual information in both multi-scale point- and scene-wise manners. In the encoder, we develop a Point Context Transformer (PCT) module to capture region contextual features at the point level; PCT contains two different transformers to excavate the relationship among points. In the decoder, we develop a Scene Context Transformer (SCT) module to learn context representations at the scene level; SCT contains both Upsampling-and-Transformer blocks and Multi-context Aggregation units to integrate the global semantic and multi-level features from the encoder into the global scene context. Experiments show clear improvements of PSFormer over its competitors and validate that PSFormer is more robust to challenging cases such as small objects, multiple objects, and objects with complex structures.

Huan Ni, Yubin Zhao, Haiyan Guan et al.

Large-scale high-resolution land cover classification is a prerequisite for constructing Earth system models and addressing ecological and resource issues. Advancements in satellite sensor technology have led to an improvement in spatial resolution and wider coverage areas. Nevertheless, the lack of high-resolution labeled data is still a challenge, hindering the largescale application of land cover classification methods. In this paper, we propose a Transformerbased weakly supervised method for cross-resolution land cover classification using outdated data. First, to capture long-range dependencies without missing the fine-grained details of objects, we propose a U-Net-like Transformer based on a reverse difference mechanism (RDM) using dynamic sparse attention. Second, we propose an anti-noise loss calculation (ANLC) module based on optimal transport (OT). Anti-noise loss calculation identifies confident areas (CA) and vague areas (VA) based on the OT matrix, which relieves the impact of noises in outdated land cover products. By introducing a weakly supervised loss with weights and employing unsupervised loss, the RDM-based U-Net-like Transformer was trained. Remote sensing images with 1 m resolution and the corresponding ground-truths of six states in the United States were employed to validate the performance of the proposed method. The experiments utilized outdated land cover products with 30 m resolution from 2013 as training labels, and produced land cover maps with 1 m resolution from 2017. The results show the superiority of the proposed method compared to state-of-the-art methods. The code is available at https://github.com/yu-ni1989/ANLC-Former.

Yijie Zhang, Yiyang Shen, Weiran Wang

Multimodal data are prevalent across various domains, and learning robust representations of such data is paramount to enhancing generation quality and downstream task performance. To handle heterogeneity and interconnections among different modalities, recent multimodal generative models extract shared and private (modality-specific) information with two separate variables. Despite attempts to enforce disentanglement between these two variables, these methods struggle with challenging datasets where the likelihood model is insufficient. In this paper, we propose Information-disentangled Multimodal VAE (IDMVAE) to explicitly address this issue, with rigorous mutual information-based regularizations, including cross-view mutual information maximization for extracting shared variables, and a cycle-consistency style loss for redundancy removal using generative augmentations. We further introduce diffusion models to improve the capacity of latent priors. These newly proposed components are complementary to each other. Compared to existing approaches, IDMVAE shows a clean separation between shared and private information, demonstrating superior generation quality and semantic coherence on challenging datasets.