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Found 14 papers, 5 papers with code
Guided Semantic Flow
Establishing dense semantic correspondences requires dealing with large geometric variations caused by the unconstrained setting of images.
Zero-shot Building Attribute Extraction from Large-Scale Vision and Language Models
The proposed workflow contains two key components: image-level captioning and segment-level captioning for the building images based on the vocabularies pertinent to structural and civil engineering.
Local-Guided Global: Paired Similarity Representation for Visual Reinforcement Learning
Recent vision-based reinforcement learning (RL) methods have found extracting high-level features from raw pixels with self-supervised learning to be effective in learning policies.
Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
Existing pipelines of semantic correspondence commonly include extracting high-level semantic features for the invariance against intra-class variations and background clutters.
Unsupervised Scene Sketch to Photo Synthesis
Taking this advantage, we synthesize a photo-realistic image by combining the structure of a sketch and the visual style of a reference photo.
Self-Supervised Structured Representations for Deep Reinforcement Learning
The proposed method imposes similarity constraints on the three latent volumes; warped query representations by estimated flows, predicted target representations from the transition model, and target representations of future state.
Weakly-Supervised Learning of Disentangled and Interpretable Skills for Hierarchical Reinforcement Learning
Given the latent representations as skills, a skill-based policy network is trained to generate similar trajectories to the learned decoder of the trajectory VAE.
Mining Better Samples for Contrastive Learning of Temporal Correspondence
We present a novel framework for contrastive learning of pixel-level representation using only unlabeled video.
CATs: Cost Aggregation Transformers for Visual Correspondence
We propose a novel cost aggregation network, called Cost Aggregation Transformers (CATs), to find dense correspondences between semantically similar images with additional challenges posed by large intra-class appearance and geometric variations.
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Semantic correspondence
on PF-WILLOW
Joint Learning of Semantic Alignment and Object Landmark Detection
Based on the key insight that the two tasks can mutually provide supervisions to each other, our networks accomplish this through a joint loss function that alternatively imposes a consistency constraint between the two tasks, thereby boosting the performance and addressing the lack of training data in a principled manner.
Semantic Attribute Matching Networks
SAM-Net accomplishes this through an iterative process of establishing reliable correspondences by reducing the attribute discrepancy between the images and synthesizing attribute transferred images using the learned correspondences.
Recurrent Transformer Networks for Semantic Correspondence
Our networks accomplish this through an iterative process of estimating spatial transformations between the input images and using these transformations to generate aligned convolutional activations.
PARN: Pyramidal Affine Regression Networks for Dense Semantic Correspondence
To the best of our knowledge, it is the first work that attempts to estimate dense affine transformation fields in a coarse-to-fine manner within deep networks.
FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence
The sampling patterns of local structure and the self-similarity measure are jointly learned within the proposed network in an end-to-end and multi-scale manner.
