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 251–260 of 2042 papers

Title	Date	Tasks	Status	Hype
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition	Mar 11, 2024	Graph GenerationLanguage Modeling	—Unverified	0
Textureless Object Recognition: An Edge-based Approach	Mar 10, 2024	ObjectObject Recognition	—Unverified	0
A spatiotemporal style transfer algorithm for dynamic visual stimulus generation	Mar 7, 2024	Image GenerationObject Recognition	—Unverified	0
LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition	Mar 6, 2024	Contrastive LearningFine-Grained Visual Recognition	—Unverified	0
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding	Mar 5, 2024	3D visual groundingDecision Making	CodeCode Available	1
Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for Recognition and Retrieval	Mar 1, 2024	ObjectObject Recognition	—Unverified	0
DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments	Feb 29, 2024	AttributeCollision Avoidance	—Unverified	0
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model	Feb 29, 2024	Language ModelingLanguage Modelling	—Unverified	0
Probing Multimodal Large Language Models for Global and Local Semantic Representations	Feb 27, 2024	Image to textobject-detection	CodeCode Available	0
CLoVe: Encoding Compositional Language in Contrastive Vision-Language Models	Feb 22, 2024	Object RecognitionRetrieval	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