SOTAVerified

Object Categorization

Object categorization identifies which label, from a given set, best corresponds to an image region defined by an input image and bounding box.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 31–40 of 80 papers

TitleStatusHype
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning—0
Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer—0
On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation—0
Semi-supervised Node Splitting for Random Forest Construction—0
Semi-supervised Vocabulary-informed Learning—0
Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks—0
SimiNet: a Novel Method for Quantifying Brain Network Similarity—0
Sparse, Collaborative, or Nonnegative Representation: Which Helps Pattern Classification?—0
Thesis: Multiple Kernel Learning for Object Categorization—0
Towards Learning free Naive Bayes Nearest Neighbor-based Domain Adaptation—0
Show:102550
← PrevPage 4 of 8Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Unified-IOXLCategorization (ablation)61.7—Unverified
2GPV-2Categorization (ablation)54.7—Unverified
3CLIPCategorization (ablation)48.1—Unverified
4OFA_LargeCategorization (ablation)22.6—Unverified