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

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Showing 41–50 of 80 papers

TitleStatusHype
Dual Skipping Networks—0
Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment—0
Are we done with object recognition? The iCub robot's perspectiveCode0
SimiNet: a Novel Method for Quantifying Brain Network Similarity—0
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization—0
Weakly-Supervised Spatial Context Networks—0
Evolution in Groups: A deeper look at synaptic cluster driven evolution of deep neural networks—0
Object categorization in finer levels requires higher spatial frequencies, and therefore takes longer—0
Learning Deep Visual Object Models From Noisy Web Data: How to Make it WorkCode0
Emergence of Selective Invariance in Hierarchical Feed Forward Networks—0
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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