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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 26–50 of 2042 papers

TitleStatusHype
Topology-Guided Knowledge Distillation for Efficient Point Cloud ProcessingCode0
Visually Interpretable Subtask Reasoning for Visual Question AnsweringCode0
ArtRAG: Retrieval-Augmented Generation with Structured Context for Visual Art Understanding—0
Beyond Recognition: Evaluating Visual Perspective Taking in Vision Language Models—0
Transferable Adversarial Attacks on Black-Box Vision-Language Models—0
Zoomer: Adaptive Image Focus Optimization for Black-box MLLM—0
LM-MCVT: A Lightweight Multi-modal Multi-view Convolutional-Vision Transformer Approach for 3D Object Recognition—0
Benchmarking Multimodal Mathematical Reasoning with Explicit Visual DependencyCode1
Disaggregated Deep Learning via In-Physics Computing at Radio Frequency—0
V^2R-Bench: Holistically Evaluating LVLM Robustness to Fundamental Visual Variations—0
Naturally Computed Scale Invariance in the Residual Stream of ResNet18Code0
Quantum Doubly Stochastic Transformers—0
Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU SimulationCode2
DVLTA-VQA: Decoupled Vision-Language Modeling with Text-Guided Adaptation for Blind Video Quality Assessment—0
Visual Language Models show widespread visual deficits on neuropsychological tests—0
MASSeg : 2nd Technical Report for 4th PVUW MOSE TrackCode0
Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review—0
P2Object: Single Point Supervised Object Detection and Instance SegmentationCode2
D-Feat Occlusions: Diffusion Features for Robustness to Partial Visual Occlusions in Object Recognition—0
Advancing Egocentric Video Question Answering with Multimodal Large Language Models—0
ForcePose: A Deep Learning Approach for Force Calculation Based on Action Recognition Using MediaPipe Pose Estimation Combined with Object Detection—0
Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users—0
Foveated Instance SegmentationCode0
DuckSegmentation: A segmentation model based on the AnYue Hemp Duck Dataset—0
Leveraging 3D Geometric Priors in 2D Rotation Symmetry Detection—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