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 151–160 of 2042 papers

Title	Date	Tasks	Status	Hype
Full-Glow: Fully conditional Glow for more realistic image generation	Dec 10, 2020	Image GenerationObject Recognition	CodeCode Available	1
The Lottery Ticket Hypothesis for Object Recognition	Dec 8, 2020	Instance SegmentationKeypoint Estimation	CodeCode Available	1
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations	Dec 1, 2020	Object Recognition	CodeCode Available	1
Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings	Nov 22, 2020	Bayesian InferenceObject Recognition	CodeCode Available	1
RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition	Nov 13, 2020	object-detectionObject Detection	CodeCode Available	1
Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions	Oct 24, 2020	Machine TranslationObject Recognition	CodeCode Available	1
LCD -- Line Clustering and Description for Place Recognition	Oct 21, 2020	ClusteringImage Retrieval	CodeCode Available	1
Natural Language Rationales with Full-Stack Visual Reasoning: From Pixels to Semantic Frames to Commonsense Graphs	Oct 15, 2020	Language ModelingLanguage Modelling	CodeCode Available	1
The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain	Oct 12, 2020	Action RecognitionActive Object Detection	CodeCode Available	1
Robust and Efficient Post-Processing for Video Object Detection (REPP)	Oct 1, 2020	Autonomous DrivingDense Object 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