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

Showing 5175 of 2042 papers

TitleStatusHype
Comics Datasets Framework: Mix of Comics datasets for detection benchmarkingCode1
The 3D-PC: a benchmark for visual perspective taking in humans and machinesCode1
Bilateral Event Mining and Complementary for Event Stream Super-ResolutionCode1
Exploring the Transferability of Visual Prompting for Multimodal Large Language ModelsCode1
Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language ModelsCode1
EventRPG: Event Data Augmentation with Relevance Propagation GuidanceCode1
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual GroundingCode1
CLoVe: Encoding Compositional Language in Contrastive Vision-Language ModelsCode1
SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language ModelsCode1
Self-supervised learning of video representations from a child's perspectiveCode1
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
Object Recognition as Next Token PredictionCode1
COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy PredictionCode1
E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation LearningCode1
Lidar Annotation Is All You NeedCode1
Recognize Any RegionsCode1
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D ObjectsCode1
Matching the Neuronal Representations of V1 is Necessary to Improve Robustness in CNNs with V1-like Front-endsCode1
Intriguing properties of generative classifiersCode1
LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object RecognitionCode1
Divergences in Color Perception between Deep Neural Networks and HumansCode1
Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-ArtCode1
Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNNCode1
Decoding Natural Images from EEG for Object RecognitionCode1
Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from EventsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Imagenshape bias98.7Unverified
2Stable Diffusionshape bias92.7Unverified
3Partishape bias91.7Unverified
4ViT-22B-384shape bias86.4Unverified
5ViT-22B-560shape bias83.8Unverified
6CLIP (ViT-B)shape bias79.9Unverified
7ViT-22B-224shape bias78Unverified
8ResNet-50 (L2 eps 5.0 adv trained)shape bias69.5Unverified
9ResNet-50 (with strong augmentations)shape bias62.2Unverified
10SWSL (ResNeXt-101)shape bias49.8Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.55Unverified
2SSNNAccuracy (% )78.57Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )85.62Unverified
2SSNNAccuracy (% )79.25Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy18.75Unverified
2yunTop 5 Accuracy14.75Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2DYTop 5 Accuracy0.08Unverified
#ModelMetricClaimedVerifiedStatus
1ObjectNet-BaselineTop 5 Accuracy52.24Unverified
2AJ2021Top 5 Accuracy27.68Unverified
#ModelMetricClaimedVerifiedStatus
1SSNNAccuracy (% )94.91Unverified
#ModelMetricClaimedVerifiedStatus
1Faster-RCNNmAP30.39Unverified
#ModelMetricClaimedVerifiedStatus
1Spike-VGG11Accuracy (% )96Unverified