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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10176–10200 of 10420 papers

TitleStatusHype
Learning Receptive Fields for Pooling from Tensors of Feature Response—0
Learning Representation in Colour Conversion—0
Learning Representations of Graph Data -- A Survey—0
Learning rich optical embeddings for privacy-preserving lensless image classification—0
Learning scale-variant and scale-invariant features for deep image classification—0
Learning Shape Features and Abstractions in 3D Convolutional Neural Networks for Detecting Alzheimer's Disease—0
Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks—0
Learning Soft Labels via Meta Learning—0
Learning Structured Inference Neural Networks with Label Relations—0
Learning Structured Low-Rank Representations for Image Classification—0
Learning Structures for Deep Neural Networks—0
Learning Subclass Representations for Visually-varied Image Classification—0
Learning task-agnostic representation via toddler-inspired learning—0
Learning Task-Independent Game State Representations from Unlabeled Images—0
Learning the Connections in Direct Feedback Alignment—0
Learning The Structure of Deep Convolutional Networks—0
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification—0
Learning to Adapt Category Consistent Meta-Feature of CLIP for Few-Shot Classification—0
Learning to aggregate feature representations—0
Learning to be Global Optimizer—0
Learning to Classify New Foods Incrementally Via Compressed Exemplars—0
Learning To Collaborate in Decentralized Learning of Personalized Models—0
Learning to combine foveal glimpses with a third-order Boltzmann machine—0
Learning to Complement with Multiple Humans—0
Learning to Detect Blue-white Structures in Dermoscopy Images with Weak Supervision—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
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified