Dense Pixel Correspondence Estimation
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Benchmarks
Most implemented
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Optical Flow Estimation using a Spatial Pyramid Network
Learning Accurate Dense Correspondences and When to Trust Them
DGC-Net: Dense Geometric Correspondence Network
Papers
Multi-Camera Multi-Person Association using Transformer-Based Dense Pixel Correspondence Estimation and Detection-Based Masking
Multi-camera Association (MCA) is the task of identifying objects and individuals across camera views and is an active research topic, given its numerous applications across robotics, surveillance, and agriculture. We in…
Dense Pixel Correspondence EstimationA Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence
Text-to-image diffusion models have made significant advances in generating and editing high-quality images. As a result, numerous approaches have explored the ability of diffusion model features to understand and proces…
Dense Pixel Correspondence EstimationRepresentation LearningSemantic correspondenceSemantic SegmentationGPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data
Semantic segmentation for autonomous driving should be robust against various in-the-wild environments. Nighttime semantic segmentation is especially challenging due to a lack of annotated nighttime images and a large do…
Autonomous DrivingDense Pixel Correspondence EstimationDomain AdaptationOptical Flow Estimation+2GAN-Supervised Dense Visual Alignment
We propose GAN-Supervised Learning, a framework for learning discriminative models and their GAN-generated training data jointly end-to-end. We apply our framework to the dense visual alignment problem. Inspired by the c…
Data AugmentationDense Pixel Correspondence EstimationDeep Matching Prior: Test-Time Optimization for Dense Correspondence
Conventional techniques to establish dense correspondences across visually or semantically similar images focused on designing a task-specific matching prior, which is difficult to model. To overcome this, recent learnin…
Dense Pixel Correspondence EstimationGeometric MatchingWarp Consistency for Unsupervised Learning of Dense Correspondences
The key challenge in learning dense correspondences lies in the lack of ground-truth matches for real image pairs. While photometric consistency losses provide unsupervised alternatives, they struggle with large appearan…
Dense Pixel Correspondence EstimationTriplet