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Found 6 papers, 1 papers with code
ABO: Dataset and Benchmarks for Real-World 3D Object Understanding
ABO contains product catalog images, metadata, and artist-created 3D models with complex geometries and physically-based materials that correspond to real, household objects.
From Categories to Subcategories: Large-Scale Image Classification With Partial Class Label Refinement
Compared to approaches that disregard the extra coarse labeled data, we achieve a relative improvement in subcategory classification accuracy of up to 22% in our large-scale image classification experiments.
Quantized Kernel Learning for Feature Matching
Matching local visual features is a crucial problem in computer vision and its accuracy greatly depends on the choice of similarity measure.
Incremental Learning of NCM Forests for Large-Scale Image Classification
NCMFs not only outperform conventional random forests, but are also well suited for integrating new classes.
Closed-Form Training of Conditional Random Fields for Large Scale Image Segmentation
It is inspired by existing closed-form expressions for the maximum likelihood parameters of a generative graphical model with tree topology.
Fast Energy Minimization Using Learned State Filters
However, when the graph is fully connected and the pairwise potentials are arbitrary, the complexity of even approximate minimization algorithms such as TRW-S grows quadratically both in the number of nodes and in the number of states a node can take.
