Semantic correspondence

69 papers with code • 5 benchmarks • 6 datasets

The task of semantic correspondence aims to establish reliable visual correspondence between different instances of the same object category.

Most implemented papers

Neighbourhood Consensus Networks

ignacio-rocco/ncnet NeurIPS 2018

Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences.

PatentMatch: A Dataset for Matching Patent Claims & Prior Art

julian-risch/PatentMatch 27 Dec 2020

For these reasons, we address the computer-assisted search for prior art by creating a training dataset for supervised machine learning called PatentMatch.

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

NVlabs/DiscoBox ICCV 2021

We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision.

Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings

zhaohengyuan1/Color2Style 15 Jun 2021

In this paper, we present a fast exemplar-based image colorization approach using color embeddings named Color2Embed.

End-to-end weakly-supervised semantic alignment

ignacio-rocco/weakalign CVPR 2018

We tackle the task of semantic alignment where the goal is to compute dense semantic correspondence aligning two images depicting objects of the same category.

iSPA-Net: Iterative Semantic Pose Alignment Network

val-iisc/iSPA-Net 3 Aug 2018

Such image comparison based approach also alleviates the problem of data scarcity and hence enhances scalability of the proposed approach for novel object categories with minimal annotation.

SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data

Shaoli-Huang/SnapMix 9 Dec 2020

As the main discriminative information of a fine-grained image usually resides in subtle regions, methods along this line are prone to heavy label noise in fine-grained recognition.

Cost Aggregation Is All You Need for Few-Shot Segmentation

Seokju-Cho/Volumetric-Aggregation-Transformer 22 Dec 2021

We introduce a novel cost aggregation network, dubbed Volumetric Aggregation with Transformers (VAT), to tackle the few-shot segmentation task by using both convolutions and transformers to efficiently handle high dimensional correlation maps between query and support.

Going Denser with Open-Vocabulary Part Segmentation

facebookresearch/vlpart ICCV 2023

In this paper, we propose a detector with the ability to predict both open-vocabulary objects and their part segmentation.

Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

matthewfl/nlp-entity-convnet NAACL 2016

A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts.