# Brief Review — Looking at Outfit to Parse Clothing

## FCN + Outfit Filter + CRF

3 min readSep 15, 2024

Looking at Outfit to Parse ClothingFCN+ Outfit Filter + CRF, by Tohoku University, and The University of Tokyo2017 arXiv v1, Over 70 Citations(Sik-Ho Tsang @ Medium)

Image Segmentation[YOLACT++]

2017 … 20222023[Segment Anthing Model (SAM)] [FastSAM] [MobileSAM]

==== My Other Paper Readings Are Also Over Here ====

**FCN****side-branch network**which is referred as an**outfit encoder**to predict a consistent set of clothing labels to encourage combinatorial preference, with also the use of**conditional random field (CRF)**to explicitly consider coherent label assignment to the given image.

# Outline

**FCN + Outfit Encoder + CRF****Results**

**1. ****FCN**** + Outfit Encoder + CRF**

## 1.1. FCN

**FCN****-8s**is used.

## 1.2. Outfit Encoder (Green)

- The outfit encoder is a side branch, which inserts
**two fully-connected (FC) layers and a sigmoid layer**to predict a vector of clothing indicators. **The first FC layer**has**256 dimensions**, and**the second FC layer**has**dimensions equal to the number of classes**in the dataset. The second layer predicts confidences of existence of each garment, which can be viewed as soft-attention or**gating function to the segmentation pipeline.**- The 2nd FC is connected with a sigmoid.
- With the
**heat-maps of the****FCN****denoted by**for each label*Fi**i*, and the**scalar prediction by our outfit encoder denoted as**. A product to obtain the*gi***filtered heat-maps**:*Gi*

- (This concept is similar to the one in SENet.)

## 1.3. Conditional Random Fields (CRF)

- The energy function is a
**fully-connected pairwise function**:

- where
*x*is the label assignment for pixels. - As seen, there is an unary term and a pairwise term. One for the class probability. One for the correlation between 2 positions.
- (This is used as a post-processing filter to further boost the segmentation performance for early segmentation neural network, e.g.: DeepLabv1 & DeepLabv2)

# 2. Results

FCN+ Outfit Filter + CRF does NOT always obtain the best results.

- Authors believe that there might be a drawback from the proposed side-path architecture, which increase
**s the risk of overfitting against small datasets**, because the proposed outfit encoder must be trained to predict an image-level category.

- Some examples are shown above.