Semantic correspondence
70 papers with code • 5 benchmarks • 7 datasets
The task of semantic correspondence aims to establish reliable visual correspondence between different instances of the same object category.
Benchmarks
These leaderboards are used to track progress in Semantic correspondence
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CUB-200-2011
Caltech-101
SPair-71k
PF-PASCAL
PF-WILLOW
FunKPoint
AVMIT
Most implemented papers
Neighbourhood Consensus Networks
ignacio-rocco/ncnet
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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
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
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
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
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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
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
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
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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
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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
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.
