# Spatial-context-aware deep neural network for multi-class image classification

Source: [Spatial-context-aware deep neural network for multi-class image classification](<https://www.sota2.com/research/paper/68fa6bd41e209131cea30616>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:57:05.246Z

Authors: Jialu Zhang, Qian Zhang, Jianfeng Ren, Yitian Zhao, Jiang Liu

Published: 2021-11-24T06:36:10.000Z

## Abstract

Multi-label image classification is a fundamental but challenging task in computer vision. Over the past few decades, solutions exploring relationships between semantic labels have made great progress. However, the underlying spatial-contextual information of labels is under-exploited. To tackle this problem, a spatial-context-aware deep neural network is proposed to predict labels taking into account both semantic and spatial information. This proposed framework is evaluated on Microsoft COCO and PASCAL VOC, two widely used benchmark datasets for image multi-labelling. The results show that the proposed approach is superior to the state-of-the-art solutions on dealing with the multi-label image classification problem.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2111.12296>)

[PDF](<https://arxiv.org/pdf/2111.12296.pdf>)