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 without code

Bayesian Adaptation for Covariate Shift

no code yet • NeurIPS 2021

When faced with distribution shift at test time, deep neural networks often make inaccurate predictions with unreliable uncertainty estimates. While improving the robustness of neural networks is one promising approach to mitigate this issue, an appealing alternate to robustifying networks against all possible test-time shifts is to instead directly adapt them to unlabeled inputs from the particular distribution shift we encounter at test time. However, this poses a challenging question: in the standard Bayesian model for supervised learning, unlabeled inputs are conditionally independent of model parameters when the labels are unobserved, so what can unlabeled data tell us about the model parameters at test-time?

Domain Adaptation Image Classification

Memory-efficient Patch-based Inference for Tiny Deep Learning

no code yet • NeurIPS 2021

We further propose receptive field redistribution to shift the receptive field and FLOPs to the later stage and reduce the computation overhead.

Image Classification Neural Architecture Search +1

Dynamic Grained Encoder for Vision Transformers

no code yet • NeurIPS 2021

Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region.

Image Classification Language Modelling +1

An Empirical Study of Adder Neural Networks for Object Detection

no code yet • NeurIPS 2021

Adder neural networks (AdderNets) have shown impressive performance on image classification with only addition operations, which are more energy efficient than traditional convolutional neural networks built with multiplications.

Autonomous Driving Face Detection +2

argmax centroid

no code yet • NeurIPS 2021

We propose a general method to construct centroid approximation for the distribution of maximum points of a random function (a. k. a.

Domain Adaptation Few-Shot Image Classification +2

Pareto Domain Adaptation

no code yet • NeurIPS 2021

On the other hand, since prior knowledge of weighting schemes for objectives is often unavailable to guide optimization to approach the optimal solution on the target domain, we propose a dynamic preference mechanism to dynamically guide our cooperative optimization by the gradient of the surrogate loss on a held-out unlabeled target dataset.

Domain Adaptation Image Classification +2

Adversarial Teacher-Student Representation Learning for Domain Generalization

no code yet • NeurIPS 2021

Domain generalization (DG) aims to transfer the learning task from a single or multiple source domains to unseen target domains.

Data Augmentation Domain Generalization +2

Explanation-based Data Augmentation for Image Classification

no code yet • NeurIPS 2021

This work proposes a framework that utilizes concept-based explanations to automatically augment the dataset with new images that can cover these under-represented regions to improve the model performance.

Classification Data Augmentation +1

Adaptive Risk Minimization: Learning to Adapt to Domain Shift

no code yet • NeurIPS 2021

A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution.

Domain Generalization Image Classification