SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

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Showing 6326–6350 of 10420 papers

TitleStatusHype
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm—0
MinConvNets: A new class of multiplication-less Neural Networks—0
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization—0
Mind the Gap: Removing the Discretization Gap in Differentiable Logic Gate Networks—0
Mind the Modality Gap: Towards a Remote Sensing Vision-Language Model via Cross-modal Alignment—0
Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread—0
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks—0
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs—0
A Classifier-guided Approach for Top-down Salient Object Detection—0
AHA! an 'Artificial Hippocampal Algorithm' for Episodic Machine Learning—0
Learning More Discriminative Local Descriptors for Few-shot Learning—0
Learning Moderately Input-Sensitive Functions: A Case Study in QR Code Decoding—0
Minimizing Energy Costs in Deep Learning Model Training: The Gaussian Sampling Approach—0
Minimizing the Effect of Noise and Limited Dataset Size in Image Classification Using Depth Estimation as an Auxiliary Task with Deep Multitask Learning—0
Minimum Noticeable Difference based Adversarial Privacy Preserving Image Generation—0
Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification—0
Learning Mask Invariant Mutual Information for Masked Image Modeling—0
Learning Loss for Test-Time Augmentation—0
Learning Long Sequences in Spiking Neural Networks—0
Learning Longer-term Dependencies in RNNs with Auxiliary Losses—0
MirrorCheck: Efficient Adversarial Defense for Vision-Language Models—0
Depth Estimation with Simplified Transformer—0
Misaligned orientations of 4f optical neural network for image classification accuracy on various datasets—0
Beyond Batch Learning: Global Awareness Enhanced Domain Adaptation—0
Learning Location from Shared Elevation Profiles in Fitness Apps: A Privacy Perspective—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified