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 1051–1075 of 2042 papers

TitleStatusHype
Applications of knowledge graphs for food science and industry—0
Towards Instance Segmentation with Object Priority: Prominent Object Detection and Recognition—0
Towards Learning Food Portion From Monocular Images With Cross-Domain Feature Adaptation—0
Towards ontology driven learning of visual concept detectors—0
Towards Open World Recognition—0
Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors—0
Towards real-time object recognition and pose estimation in point clouds—0
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation—0
Towards the Design of an End-to-End Automated System for Image and Video-based Recognition—0
Towards Zero-Shot & Explainable Video Description by Reasoning over Graphs of Events in Space and Time—0
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition—0
TrackVLA: Embodied Visual Tracking in the Wild—0
Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision—0
Training Deep Spiking Neural Networks—0
Training-Free Open-Ended Object Detection and Segmentation via Attention as Prompts—0
Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods—0
Training the Untrainable: Introducing Inductive Bias via Representational Alignment—0
Transferable Adversarial Attacks on Black-Box Vision-Language Models—0
Transfer Learning for Material Classification using Convolutional Networks—0
Transferred Fusion Learning using Skipped Networks—0
Transferring Landmark Annotations for Cross-Dataset Face Alignment—0
Transformational Sparse Coding—0
Transformer-Based Microbubble Localization—0
Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection—0
Transformer in Touch: A Survey—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