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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 121–130 of 2042 papers

Title	Date	Tasks	Status	Hype
Convolutional Neural Networks with Gated Recurrent Connections	Jun 5, 2021	object-detectionObject Detection	CodeCode Available	1
Matching the Neuronal Representations of V1 is Necessary to Improve Robustness in CNNs with V1-like Front-ends	Oct 16, 2023	Object Recognition	CodeCode Available	1
Adaptive Threshold for Online Object Recognition and Re-identification Tasks	Dec 28, 2020	ClassificationFace Recognition	CodeCode Available	1
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding	Mar 5, 2024	3D visual groundingDecision Making	CodeCode Available	1
MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts	Oct 18, 2024	Language ModelingLanguage Modelling	CodeCode Available	1
Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworks	Jul 4, 2021	Deep LearningObject Recognition	CodeCode Available	1
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects	Mar 27, 2018	General ClassificationObject	CodeCode Available	1
Compact Generalized Non-local Network	Oct 31, 2018	Object DetectionObject Recognition	CodeCode Available	1
Comics Datasets Framework: Mix of Comics datasets for detection benchmarking	Jul 3, 2024	BenchmarkingObject	CodeCode Available	1
Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection	Dec 23, 2024	object-detectionObject Detection	CodeCode Available	1

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All datasets shape bias CIFAR10-DVS N-Caltech 101 ObjectNet (All classes)ObjectNet (ImageNet classes)ObjectNet (ImageNet classes, trained on ImageNet)DVS128 Gesture MECCANO N-CARS

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Imagen	shape bias	98.7	—	Unverified
2	Stable Diffusion	shape bias	92.7	—	Unverified
3	Parti	shape bias	91.7	—	Unverified
4	ViT-22B-384	shape bias	86.4	—	Unverified
5	ViT-22B-560	shape bias	83.8	—	Unverified
6	CLIP (ViT-B)	shape bias	79.9	—	Unverified
7	ViT-22B-224	shape bias	78	—	Unverified
8	ResNet-50 (L2 eps 5.0 adv trained)	shape bias	69.5	—	Unverified
9	ResNet-50 (with strong augmentations)	shape bias	62.2	—	Unverified
10	SWSL (ResNeXt-101)	shape bias	49.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	85.55	—	Unverified
2	SSNN	Accuracy (% )	78.57	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	85.62	—	Unverified
2	SSNN	Accuracy (% )	79.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	18.75	—	Unverified
2	yun	Top 5 Accuracy	14.75	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	52.24	—	Unverified
2	DY	Top 5 Accuracy	0.08	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ObjectNet-Baseline	Top 5 Accuracy	52.24	—	Unverified
2	AJ2021	Top 5 Accuracy	27.68	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SSNN	Accuracy (% )	94.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Faster-RCNN	mAP	30.39	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Spike-VGG11	Accuracy (% )	96	—	Unverified