# Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance

arXiv: https://arxiv.org/abs/2305.05228 · HTML: https://mlsanity.com/paper/2305.05228/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Tell Me Where to Look: Guided Attention Inference Network](https://mlsanity.com/paper/1802.10171/) (2018)
- [Human Semantic Parsing for Person Re-identification](https://mlsanity.com/paper/1804.00216/) (2018)
- [Dense Pose Transfer](https://mlsanity.com/paper/1809.01995/) (2018)
- [A Generative Model of People in Clothing](https://mlsanity.com/paper/1705.04098/) (2017)
- [Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks](https://mlsanity.com/paper/1905.09646/) (2019)
- [Multi-Label Image Classification with Regional Latent Semantic Dependencies](https://mlsanity.com/paper/1612.01082/) (2016)
- [Deep Semantic Dictionary Learning for Multi-label Image Classification](https://mlsanity.com/paper/2012.12509/) (2020)
- [Two-stage Training for Learning from Label Proportions](https://mlsanity.com/paper/2105.10635/) (2021)
- [Multi-Stage Pathological Image Classification using Semantic Segmentation](https://mlsanity.com/paper/1910.04473/) (2019)
- [WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering](https://mlsanity.com/paper/2305.04410/) (2023)
- [Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema](https://mlsanity.com/paper/2009.05403/) (2020)
- [Data Efficient Training with Imbalanced Label Sample Distribution for Fashion Detection](https://mlsanity.com/paper/2305.04379/) (2023)

## Built on by (most cited first, 1 of 1)

- [G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer](https://mlsanity.com/paper/2306.14314/) (2023)

## Abstract (arXiv)

Fine-grained multi-label classification models have broad applications in e-commerce, such as visual based label predictions ranging from fashion attribute detection to brand recognition. One challenge to achieve satisfactory performance for those classification tasks in real world is the wild visual background signal that contains irrelevant pixels which confuses model to focus onto the region of interest and make prediction upon the specific region. In this paper, we introduce a generic semantic-embedding deep neural network to apply the spatial awareness semantic feature incorporating a channel-wise attention based model to leverage the localization guidance to boost model performance for multi-label prediction. We observed an Avg.relative improvement of 15.27% in terms of AUC score across all labels compared to the baseline approach. Core experiment and ablation studies involve multi-label fashion attribute classification performed on Instagram fashion apparels' image. We compared the model performances among our approach, baseline approach, and 3 alternative approaches to leverage semantic features. Results show favorable performance for our approach.