Image Classification

1965 papers with code • 74 benchmarks • 141 datasets

Image Classification is a fundamental task that attempts to comprehend an entire image as a whole. The goal is to classify the image by assigning it to a specific label. Typically, Image Classification refers to images in which only one object appears and is analyzed. In contrast, object detection involves both classification and localization tasks, and is used to analyze more realistic cases in which multiple objects may exist in an image.

Source: Metamorphic Testing for Object Detection Systems

Latest papers with code

TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification

shengcailiao/QAConv NeurIPS 2021

In this work, we further investigate the possibility of applying Transformers for image matching and metric learning given pairs of images.

Generalizable Person Re-identification Image Classification +2

01 Dec 2021

Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning

chongjiange/care NeurIPS 2021

Motivated by the transformers that explore visual attention effectively in recognition scenarios, we propose a CNN Attention REvitalization (CARE) framework to train attentive CNN encoders guided by transformers in SSL.

Image Classification Object Detection +2

01 Dec 2021

Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models

echoyi/rps_lje NeurIPS 2021

Explaining the influence of training data on deep neural network predictions is a critical tool for debugging models through data curation.

Image Classification Text Classification

01 Dec 2021

Pooling by Sliced-Wasserstein Embedding

navid-naderi/pswe NeurIPS 2021

Learning representations from sets has become increasingly important with many applications in point cloud processing, graph learning, image/video recognition, and object detection.

Graph Learning Image Classification +3

01 Dec 2021

KNAS: Green Neural Architecture Search

jingjing-nlp/knas 26 Nov 2021

Many existing neural architecture search (NAS) solutions rely on downstream training for architecture evaluation, which takes enormous computations.

Image Classification Neural Architecture Search +1

26 Nov 2021

Transferability Estimation using Bhattacharyya Class Separability

thuml/LogME 24 Nov 2021

Then, we estimate their pairwise class separability using the Bhattacharyya coefficient, yielding a simple and effective measure of how well the source model transfers to the target task.

Classification Fine-tuning +3

24 Nov 2021

Information Bottleneck-Based Hebbian Learning Rule Naturally Ties Working Memory and Synaptic Updates

darsnack/biological-hsic 24 Nov 2021

The global component computes a layer-wise modulatory signal that depends on a batch of samples.

Image Classification

24 Nov 2021

Focal and Global Knowledge Distillation for Detectors

yzd-v/FGD 23 Nov 2021

Global distillation rebuilds the relation between different pixels and transfers it from teachers to students, compensating for missing global information in focal distillation.

Knowledge Distillation Object Detection

23 Nov 2021

Using mixup as regularization and tuning hyper-parameters for ResNets

pvbhanuteja/mixrnet 23 Nov 2021

Considering the ease of training with limited resources this work revisits the ResNets and improves the ResNet50 \cite{resnets} by using mixup data-augmentation as regularization and tuning the hyper-parameters.

Data Augmentation Image Classification

23 Nov 2021

Multi-label Iterated Learning for Image Classification with Label Ambiguity

rajeswar18/mile 23 Nov 2021

We also show that MILe is effective reducing label noise, achieving state-of-the-art performance on real-world large-scale noisy data such as WebVision.

Fine-tuning Image Classification +2

23 Nov 2021