WeCLIP: New Method for Semantic Segmentation

Weakly Supervised Semantic Segmentation is a method used in computer vision to identify and segment objects in images with minimal manual labeling. Typically, this involves using image-level labels, which are easier to obtain than pixel-level annotations. This technology reduces the effort required to label each pixel in an image for training machine learning models.

In recent years, models like CLIP have gained popularity for their ability to associate images with text. Recent studies have shown promising results using CLIP to generate pseudo labels for training segmentation models. However, there has not been a direct approach to use CLIP as the main framework for segmenting objects based solely on image-level labels.

In this work, we introduce a new approach called WeCLIP. This method leverages the frozen CLIP model as a backbone to extract features for segmenting images in a single-step process. We also introduce a Decoder that interprets these features to produce final predictions for segmentation tasks. Additionally, we create a Refinement Module to improve the quality of the labels generated during training.

#Background on Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation aims to train a model to understand images at a pixel level while using limited supervision. The primary types of weak supervision include scribbles, bounding boxes, points, and image-level labels. Among these, using image-level labels is the most common due to their simplicity and ease of collection from various online sources.

There are generally two approaches to weakly supervised semantic segmentation with image-level labels: multi-stage training and single-stage training. Multi-stage training typically involves generating high-quality pseudo labels using several models, followed by training a separate segmentation model. On the other hand, single-stage training attempts to directly segment images using one model.

Previous single-stage models have largely relied on pre-trained models, usually from ImageNet, and fine-tuned during training. These models often attempt to refine their outputs using different techniques but generally underperform compared to multi-stage models.

In contrast, multi-stage models may involve complex pipelines where pixel-level pseudo labels are created from weak labels before training a segmentation model. Recent efforts have attempted to incorporate CLIP to produce high-quality pseudo labels using its ability to understand the relationship between images and text.

#Overview of WeCLIP

Our proposed WeCLIP method represents a step forward in weakly supervised semantic segmentation by using the CLIP model directly as the backbone for feature extraction. Unlike previous methods, which only used CLIP to enhance other models, WeCLIP utilizes the frozen CLIP model to generate features that can be directly input into a segmentation decoder.

By using the frozen CLIP model, we avoid the need for extensive training on the backbone, reducing the overall computational cost and memory requirements. The newly designed decoder interprets the frozen features, enabling the segmentation prediction process with minimal learnable parameters.

#The Structure of Our Approach

#Framework Components

WeCLIP comprises four main components:

1. Frozen CLIP Backbone: This part extracts image and text features from the input data. It does not require any training or fine-tuning, simplifying the overall process.

2. Classification Process: This step generates initial class activation maps (CAMs) based on the features extracted by the CLIP backbone. CAMs help identify areas of interest in the images.

3. Decoder: This is responsible for converting the features from the frozen backbone into semantic segmentation predictions. The decoder interprets the extracted features effectively while keeping the number of parameters low.

4. Refinement Module (RFM): This module dynamically updates the initial CAMs to create better pseudo labels for training the decoder. By utilizing relationships derived from the decoder, the RFM enhances the quality of the generated labels.

#Initial CAM Generation

The process begins by inputting an image into the frozen CLIP model. The model extracts image features that reflect the content of the image. Simultaneously, class labels are used to create text prompts that produce corresponding text features. By comparing the pooled image features with the text features, classification scores are generated, which inform the generation of the initial CAM through GradCAM.

#Function of the Decoder

Once the initial CAMs are created, the decoder steps in to interpret the features. The decoder takes the image features and produces segmentation predictions, focusing on identifying objects within the image. An affinity map generated from the decoder’s intermediate feature maps is also used to aid in refining the CAMs.

#Refinement Module Operation

The refinement module addresses the limitation of the frozen backbone providing only static CAMs. By leveraging features from the decoder, the RFM dynamically adjusts the CAMs during training. This process enhances the accuracy of the pseudo labels by utilizing more reliable feature relationships.

#Experimental Setup

We conducted extensive experiments to evaluate our approach on two popular datasets: PASCAL VOC 2012 and MS COCO-2014. These datasets are widely used in semantic segmentation tasks and contain various types of images with labeled objects.

#Dataset Details

• PASCAL VOC 2012: This dataset contains 10,582 training images, 1,446 validation images, and 1,456 test images across 20 foreground classes. The dataset is supplemented with additional labels to improve training outcomes.

• MS COCO-2014: This larger dataset includes approximately 82,000 training images and 40,504 validation images with 80 foreground classes. It poses a significant challenge due to its diverse range of objects and contexts.

#Evaluation Metric

We employed the Mean Intersection-over-Union (mIoU) metric for evaluating performance. This metric calculates the overlap between the predicted segmentation and the ground truth, providing a clear measure of the model's effectiveness.

#Results and Comparisons

#Performance on PASCAL VOC 2012

Our approach achieved remarkable results on the PASCAL VOC 2012 dataset. WeCLIP reached 76.4% mIoU on the validation set and 77.2% on the test set. These scores surpass those of previous single-stage and multi-stage approaches, demonstrating the effectiveness of using the frozen CLIP model for segmentation tasks.

#Comparisons with State-of-the-Art Methods

When compared with other leading methods, WeCLIP showed significant improvements. For instance, our approach outperformed the previous state-of-the-art single-stage approach by more than 5% mIoU on both validation and test sets. Furthermore, WeCLIP consistently exceeded performance metrics of multi-stage approaches, showcasing the advantages of our method.

#Performance on MS COCO-2014

WeCLIP also exhibited strong performance on the MS COCO-2014 validation set, achieving 47.1% mIoU. This result reflects a notable improvement over existing single-stage techniques and positions WeCLIP as a competitive option among multi-stage methods as well.

#Training Cost Analysis

One of the key benefits of WeCLIP is its reduced training cost. With only 6.2GB of GPU memory required, our approach demands significantly less computational resource compared to other methods, which often require 12GB or more. This efficiency is particularly advantageous for researchers and practitioners with limited access to high-end computing resources.

#Ablation Studies

To further validate our proposed technique, we conducted ablation studies focusing on various components of WeCLIP.

#Impact of the Decoder and RFM

The presence of the decoder is crucial, as it is necessary for generating predictions. Introducing the refinement module (RFM) led to a clear improvement of 6.2% mIoU. This enhancement reflects the RFM's role in improving the quality of pseudo labels.

#Evaluation of Transformer Layers

We examined how altering the number of transformer layers in the decoder affected performance. Increasing the number of layers helped capture more feature information, leading to improved performance. However, performance dropped when the number of layers exceeded a certain threshold, suggesting a balance is necessary to avoid overfitting.

#Performance on Fully Supervised Semantic Segmentation

In addition to weak supervision, we assessed WeCLIP’s capability within fully supervised settings. Without the need for the frozen text encoder or RFM, our decoder trained on accurate pixel-level labels from the dataset.

#Results for Fully Supervised Case

When evaluated on the PASCAL VOC 2012 dataset, WeCLIP maintained high segmentation performance while utilizing fewer trainable parameters. This finding highlights its potential utility in scenarios where precise annotations are available, while still providing a competitive edge in terms of resource consumption.

#Conclusion

In summary, we introduced WeCLIP, a novel single-stage pipeline designed for weakly supervised semantic segmentation. By leveraging the frozen CLIP model, we successfully reduced training costs and improved performance compared to traditional methods. Our decoder effectively interprets the frozen features, while the refinement module enhances the quality of output labels. Overall, WeCLIP offers a valuable alternative to existing techniques, advancing research in weakly supervised semantic segmentation.