30 papers with code • 4 benchmarks • 4 datasets
The task of semantic correspondence aims to establish reliable visual correspondence between different instances of the same object category.
We present the full-resolution correspondence learning for cross-domain images, which aids image translation.
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.
Ranked #2 on Semantic correspondence on PF-PASCAL (PCK (weak) metric)
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.
Ranked #15 on Fine-Grained Image Classification on CUB-200-2011
Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain.
Establishing visual correspondences under large intra-class variations requires analyzing images at different levels, from features linked to semantics and context to local patterns, while being invariant to instance-specific details.
Ranked #1 on Semantic correspondence on Caltech-101
The intensity estimation of facial action units (AUs) is challenging due to subtle changes in the person's facial appearance.
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.
Establishing dense correspondences across semantically similar images is a challenging task.
Ranked #3 on Semantic correspondence on PF-WILLOW