SOTAVerified

Object Recognition

Object recognition is a computer vision technique for detecting + classifying objects in images or videos. Since this is a combined task of object detection plus image classification, the state-of-the-art tables are recorded for each component task here and here.

( Image credit: Tensorflow Object Detection API )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 of 2042 papers

TitleStatusHype
Comparing Photorealism in Game Engines for Synthetic Maritime Computer Vision Datasets—0
LRSAA: Large-scale Remote Sensing Image Target Recognition and Automatic AnnotationCode1
Fine-Grained Open-Vocabulary Object Recognition via User-Guided Segmentation—0
ViSTa Dataset: Do vision-language models understand sequential tasks?Code0
Interactive Medical Image Segmentation: A Benchmark Dataset and BaselineCode3
Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot LearningCode1
Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media ContextsCode0
LightFFDNets: Lightweight Convolutional Neural Networks for Rapid Facial Forgery Detection—0
Multiscale Dubuc: A New Similarity Measure for Time SeriesCode0
Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction—0
DipMe: Haptic Recognition of Granular Media for Tangible Interactive Applications—0
Large-scale Remote Sensing Image Target Recognition and Automatic AnnotationCode1
Hidden in Plain Sight: Evaluating Abstract Shape Recognition in Vision-Language ModelsCode0
Scaling Laws for Task-Optimized Models of the Primate Visual Ventral StreamCode0
Object Recognition in Human Computer Interaction:- A Comparative Analysis—0
Lost in Context: The Influence of Context on Feature Attribution Methods for Object RecognitionCode0
Learning Where to Edit Vision TransformersCode0
Active Gaze Behavior Boosts Self-Supervised Object Learning—0
Investigating the Gestalt Principle of Closure in Deep Convolutional Neural NetworksCode0
Unsupervised Object Discovery: A Comprehensive Survey and Unified Taxonomy—0
Training the Untrainable: Introducing Inductive Bias via Representational Alignment—0
Few-shot target-driven instance detection based on open-vocabulary object detection models—0
MomentumSMoE: Integrating Momentum into Sparse Mixture of ExpertsCode1
Development of Image Collection Method Using YOLO and Siamese Network—0
big.LITTLE Vision Transformer for Efficient Visual Recognition—0
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All datasetsshape biasCIFAR10-DVSN-Caltech 101ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 GestureMECCANON-CARS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7—Unverified
2Stable Diffusionshape bias92.7—Unverified
3Partishape bias91.7—Unverified
4ViT-22B-384shape bias86.4—Unverified
5ViT-22B-560shape bias83.8—Unverified
6CLIP (ViT-B)shape bias79.9—Unverified
7ViT-22B-224shape bias78—Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5—Unverified
9ResNet-50 (with strong augmentations)shape bias62.2—Unverified
10SWSL (ResNeXt-101)shape bias49.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55—Unverified
2SSNNAccuracy (% )78.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62—Unverified
2SSNNAccuracy (% )79.25—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75—Unverified
2yunTop 5 Accuracy14.75—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2DYTop 5 Accuracy0.08—Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24—Unverified
2AJ2021Top 5 Accuracy27.68—Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91—Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39—Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96—Unverified