Video-based person re-identification by an end-to-end learning architecture with hybrid deep appearance-temporal feature. Multi-level bottom-top and top-bottom feature fusion for crowd counting Proceedings of the IEEE International Conference on Computer Vision Seoul, Korea. Clothoid: an integrated hierarchical framework for autonomous driving in a dynamic urban environment. Detecting multi-resolution pedestrians using group cost-sensitive boosting with channel features. Moreover, the extension of our method towards the rainy image segmentation and detection tasks validates the practicality of the proposed method in outdoor applications.Ĭlean background preservation occluded region filtering raindrop and raindrop-free images raindrop detection and removal shape adaptive network. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art raindrop removal methods. Second, a two-branch Multi-scale Shape Adaptive Network (MSANet) is proposed to detect and remove diverse raindrops, effectively filtering the occluded raindrop regions and keeping the clean background well-preserved. First, we establish a large-scale dataset named RaindropCityscapes, which includes 11,583 pairs of raindrop and raindrop-free images, covering a wide variety of raindrops and background scenarios. In this paper, we address these raindrop removal problems from two perspectives. Second, recent deraining methods tend to apply shape-invariant filters to cope with diverse rainy images and fail to remove raindrops that are especially varied in shape and scale. First, publicly available raindrop image datasets have limited capacity in terms of modeling raindrop characteristics (e.g., raindrop collision and fusion) in real-world scenes. Previous explorations have mainly been limited in two ways. Removing raindrops from a single image is a challenging problem due to the complex changes in shape, scale, and transparency among raindrops.
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