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

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Showing 251–300 of 2042 papers

TitleStatusHype
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition—0
Textureless Object Recognition: An Edge-based Approach—0
A spatiotemporal style transfer algorithm for dynamic visual stimulus generation—0
LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition—0
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual GroundingCode1
Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for Recognition and Retrieval—0
DOZE: A Dataset for Open-Vocabulary Zero-Shot Object Navigation in Dynamic Environments—0
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model—0
Probing Multimodal Large Language Models for Global and Local Semantic RepresentationsCode0
CLoVe: Encoding Compositional Language in Contrastive Vision-Language ModelsCode1
ISCUTE: Instance Segmentation of Cables Using Text Embedding—0
SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Autonomous Agents—0
Leveraging Self-Supervised Instance Contrastive Learning for Radar Object Detection—0
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View—0
Optimizing Sparse Convolution on GPUs with CUDA for 3D Point Cloud Processing in Embedded Systems—0
Logical recognition method for solving the problem of identification in the Internet of Things—0
A comparison between humans and AI at recognizing objects in unusual posesCode0
SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language ModelsCode1
Motion Mapping Cognition: A Nondecomposable Primary Process in Human Vision—0
Self-supervised learning of video representations from a child's perspectiveCode1
Lightweight Pixel Difference Networks for Efficient Visual Representation LearningCode4
Local Feature Matching Using Deep Learning: A SurveyCode2
Achieving More Human Brain-Like Vision via Human EEG Representational Alignment—0
EdgeOL: Efficient in-situ Online Learning on Edge Devices—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
The Machine Vision Iceberg Explained: Advancing Dynamic Testing by Considering Holistic Environmental Relations—0
pix2gestalt: Amodal Segmentation by Synthesizing WholesCode3
Synthetic data enables faster annotation and robust segmentation for multi-object grasping in clutter—0
Agricultural Object Detection with You Look Only Once (YOLO) Algorithm: A Bibliometric and Systematic Literature Review—0
ContextMix: A context-aware data augmentation method for industrial visual inspection systemsCode0
Geo-locating Road Objects using Inverse Haversine Formula with NVIDIA Driveworks—0
Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras—0
Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imageryCode2
Meta-forests: Domain generalization on random forests with meta-learning—0
Incorporating Geo-Diverse Knowledge into Prompting for Increased Geographical Robustness in Object Recognition—0
Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Network—0
Layerwise complexity-matched learning yields an improved model of cortical area V2—0
CLIP-guided Federated Learning on Heterogeneous and Long-Tailed DataCode1
Object Recognition from Scientific Document based on Compartment Refinement Framework—0
Representational constraints underlying similarity between task-optimized neural systems—0
Exploring Novel Object Recognition and Spontaneous Location Recognition Machine Learning Analysis Techniques in Alzheimer's MiceCode0
The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates—0
Scientific Preparation for CSST: Classification of Galaxy and Nebula/Star Cluster Based on Deep Learning—0
Are Vision Transformers More Data Hungry Than Newborn Visual Systems?Code0
SRTransGAN: Image Super-Resolution using Transformer based Generative Adversarial Network—0
COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy PredictionCode1
Object Recognition as Next Token PredictionCode1
Foveation in the Era of Deep LearningCode0
Developmental Pretraining (DPT) for Image Classification NetworksCode0
Learning for Semantic Knowledge Base-Guided Online Feature Transmission in Dynamic Channels—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