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Found 6 papers, 4 papers with code
LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models
Specifically, to tailor the pre-trained latent diffusion model to operate on the RAW domain, we train a set of lightweight taming modules to inject the RAW information into the diffusion denoising process via modulating the intermediate features of UNet.
Video Waterdrop Removal via Spatio-Temporal Fusion in Driving Scenes
The waterdrops on windshields during driving can cause severe visual obstructions, which may lead to car accidents.
Optimizing Video Prediction via Video Frame Interpolation
Extensive experiments on the Cityscapes, KITTI, DAVIS, Middlebury, and Vimeo90K datasets show that our video prediction results are robust in general scenarios, and our approach outperforms other video prediction methods that require a large amount of training data or extra semantic information.
Unifying Global-Local Representations in Salient Object Detection with Transformer
In this paper, we introduce a new attention-based encoder, vision transformer, into salient object detection to ensure the globalization of the representations from shallow to deep layers.
Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes
In this way, arbitrary attributes can be edited by collecting positive data only, and the proposed method learns a controllable representation enabling manipulation of non-binary attributes like anime styles and facial characteristics.
Single Image Reflection Removal Beyond Linearity
Second, we design a cascaded network for reflection removal with three tasks: predicting the transmission layer, reflection layer, and the non-linear alpha blending mask.
