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 71–80 of 2042 papers

Title	Date	Tasks	Status	Hype
Divergences in Color Perception between Deep Neural Networks and Humans	Sep 11, 2023	image-classificationImage Classification	CodeCode Available	1
Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art	Sep 10, 2023	Objectobject-detection	CodeCode Available	1
Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN	Sep 4, 2023	object-detectionObject Detection	CodeCode Available	1
Decoding Natural Images from EEG for Object Recognition	Aug 25, 2023	Contrastive LearningEEG	CodeCode Available	1
Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from Events	Aug 18, 2023	Image ReconstructionObject	CodeCode Available	1
Scaling may be all you need for achieving human-level object recognition capacity with human-like visual experience	Aug 7, 2023	AllObject Recognition	CodeCode Available	1
DesCo: Learning Object Recognition with Rich Language Descriptions	Jun 24, 2023	Language ModelingLanguage Modelling	CodeCode Available	1
EventCLIP: Adapting CLIP for Event-based Object Recognition	Jun 10, 2023	Few-Shot LearningObject	CodeCode Available	1
Paxion: Patching Action Knowledge in Video-Language Foundation Models	May 18, 2023	Action UnderstandingDiagnostic	CodeCode Available	1
Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery	May 10, 2023	Contrastive Learningimage-classification	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