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Found 19 papers, 12 papers with code
Generative Inbetweening through Frame-wise Conditions-Driven Video Generation
Generative inbetweening aims to generate intermediate frame sequences by utilizing two key frames as input.
Seeing Beyond Views: Multi-View Driving Scene Video Generation with Holistic Attention
Generating multi-view videos for autonomous driving training has recently gained much attention, with the challenge of addressing both cross-view and cross-frame consistency.
Reblurring-Guided Single Image Defocus Deblurring: A Learning Framework with Misaligned Training Pairs
Then, to effectively learn the baseline defocus deblurring network with misaligned training pairs, our reblurring module ensures spatial consistency between the deblurred image, the reblurred image and the input blurry image by reconstructing spatially variant isotropic blur kernels.
MC$^2$: Multi-concept Guidance for Customized Multi-concept Generation
Customized text-to-image generation, which synthesizes images based on user-specified concepts, has made significant progress in handling individual concepts.
Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning
By incorporating auxiliary information from CLIP and utilizing prompt fine-tuning, we effectively eliminate noisy samples from the clean set and mitigate confirmation bias during training.
Learning Real-World Image De-Weathering with Imperfect Supervision
Real-world image de-weathering aims at removing various undesirable weather-related artifacts.
Inferring and Leveraging Parts from Object Shape for Improving Semantic Image Synthesis
Despite the progress in semantic image synthesis, it remains a challenging problem to generate photo-realistic parts from input semantic map.
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution
On the other hand, alignment algorithms in existing VSR methods perform poorly for real-world videos, leading to unsatisfactory results.
A Survey on Leveraging Pre-trained Generative Adversarial Networks for Image Editing and Restoration
Generative adversarial networks (GANs) have drawn enormous attention due to the simple yet effective training mechanism and superior image generation quality.
Learning Diverse Tone Styles for Image Retouching
In particular, the style encoder predicts the target style representation of an input image, which serves as the conditional information in the RetouchNet for retouching, while the TSFlow maps the style representation vector into a Gaussian distribution in the forward pass.
Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones
However, BiRNN is intrinsically offline because it uses backward recurrent modules to propagate from the last to current frames, which causes high latency and large memory consumption.
Invertible Network for Unpaired Low-light Image Enhancement
Existing unpaired low-light image enhancement approaches prefer to employ the two-way GAN framework, in which two CNN generators are deployed for enhancement and degradation separately.
Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser
To begin with, the pre-trained denoiser is used to generate the pseudo clean images for the test images.
Unpaired Learning of Deep Image Denoising
As for knowledge distillation, we first apply the learned noise models to clean images to synthesize a paired set of training images, and use the real noisy images and the corresponding denoising results in the first stage to form another paired set.
Remove Cosine Window from Correlation Filter-based Visual Trackers: When and How
Therefore, we in this paper investigate the feasibility to remove cosine window from CF trackers with spatial regularization.
Joint Representation and Truncated Inference Learning for Correlation Filter based Tracking
In existing off-line deep learning models for CF trackers, the model adaptation usually is either abandoned or has closed-form solution to make it feasible to learn deep representation in an end-to-end manner.
VITAL: VIsual Tracking via Adversarial Learning
To augment positive samples, we use a generative network to randomly generate masks, which are applied to adaptively dropout input features to capture a variety of appearance changes.
Learning Support Correlation Filters for Visual Tracking
Sampling and budgeting training examples are two essential factors in tracking algorithms based on support vector machines (SVMs) as a trade-off between accuracy and efficiency.
F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation
The generalization error bound of support vector machine (SVM) depends on the ratio of radius and margin, while standard SVM only considers the maximization of the margin but ignores the minimization of the radius.
