A Deep Guide to Text-Guided Open-Vocabulary Segmentation
Discover the power of text-guided open-vocabulary segmentation using large language models like GPT-4 & ChatGPT for automating image and video processing tasks.
Deep learning has achieved miraculous results for some years now. It can match human-level vision and speech capabilities, generate realistic art, and beat top players in games. But a major obstacle to using machine learning in a real production environment is the training or fine-tuning process. Gathering data, labeling classes in images, training models, and managing data variance coverage can be extremely expensive and time intensive and pretty low ROI when looking to get something out as quickly as possible.
This is where zero-shot learning methods can be immensely useful to businesses looking to quickly leverage computer vision models. Zero-shot methods enable engineers to use a model out-of-the-box with very little data and manual resources required for building the datasets.
How is this possible? In this article, you'll learn about zero-shot object detection, a capability that is potentially useful to a wide swath of industries.
Given an image it hasn't seen already, normal object detection (OD) locates and labels all the objects that it's trained to identify. Importantly, the labels it assigns are always from a fixed set. That's why the task is sometimes called "closed vocabulary" detection. Because all the labels are present in the training data, they're called "seen" labels or classes.
In contrast, zero-shot object detection (ZSOD) attempts to identify objects with labels it's never seen before while training. It does this by figuring out the semantic distances of the unseen labels from the seen classes and projecting those distances on the visual concepts it's learned.
One of the main benefits of ZSOD is that it allows you to start using object detection with a much smaller or no dataset.
A demo of zero-shot object detection using a smartphone in a retail setting is shown below (the odd labels are because the unseen labels supplied are from a non-retail dataset).
Where do unseen labels come from? The set of unseen classes depends on the problem being solved and must be supplied to ZSOD along with the image being processed. For every image, a different set of unseen categories, possibly context-dependent, can be supplied.
ZSOD by itself is not a generative task that can create new labels on its own. Instead, it selects the most relevant label from a set it's given. Such a set is often generated using other natural language processing tasks like image captioning.
Another aspect to note is that, traditionally, labels are nouns or short noun phrases (like "wine bottle" or "blinder for horses"). But modern ZSOD supports long, natural language descriptions with any number of words, phrases, sentences, or even paragraphs. For these reasons, modern ZSOD is a type of "open vocabulary" detection.
ZSOD's use of unseen labels may seem a little non-intuitive. Later in this article, we'll demonstrate its usefulness with practical examples.
Here are some benefits of modern zero-shot detection (ZSD) compared to traditional object detection:
If you're familiar with machine learning, you're probably thinking right now about other tasks that seem confusingly similar to ZSOD. We clarify their differences below:
These tasks are not mutually exclusive with each other or ZSOD. They can be combined for your particular use case and desired goals and many can be used in a zero-shot setting as well.
In the next section, we take the retail industry as an example to explore how ZSOD can be used in practice.
Let's see some practical applications of zero-shot object detection in different industries. You'll also understand how unseen labels work in practice. While imagery often comes from customers, employees, or monitoring systems, unseen labels can come from various internal and external systems, and that is the key to exploiting ZSOD for novel intelligent capabilities.
Many retail and product recognition workflows can benefit from zero-shot object detection by potentially saving time, reducing costs, or improving sales.
In-store inventory management keeps track of all the items that are kept on display to be sold. It tracks their current state (displayed, sold, spoiled, stolen, and so on), their current stock, their locations in the store, their prices, any offers on them, and other details.
ZSOD can help retailers in several ways. For most of these use cases, the images come from customers or employees while unseen labels can be sourced from the inventory database, public retail datasets, or product catalogs:
ZSOD can also enable personalized recommendations for customers. For example, a customer can ask the system to alert them when a product of interest is in stock using descriptive (unseen) queries like "a rose-colored dress" rather than specifying particular brands and possibly missing out on a great buy.
Factory floors and assembly lines are busy places where floor managers and technicians may need to track parts, spares, tools, and other objects in real time. It's time-consuming for employees to enter these details manually into a tracking system.
Instead, they can use ZSOD to snap photos regularly, visually detect objects, identify them, and enter their state or count in the system. The unseen labels can come from procurement systems or industry-specific catalogs.
Text is an integral aspect of ZSOD in the form of unseen labels. For a long time, a limited set of concise labels was the norm. But many use cases can benefit by using longer, descriptive, open-vocabulary natural language labels. That's possible now, thanks to large language models and vision-language models.
Vision-language models can jointly learn visual and language features using expressive and efficient architectures like transformers. Newer models for computer vision tasks have also started using them but convolutional neural networks remain quite popular.
In these sections, we'll analyze Microsoft's RegionCLIP model in depth to understand how to detect object classes in a zero-shot setting using vision-language models.
CLIP stands for contrastive language-image pretraining. It's a pioneering vision-language model from OpenAI that has learned to reason about visual concepts in images and their natural language text descriptions from 400 million images and captions. When given an image, it selects the best text description for it; when given a text description, it selects the most relevant image that matches. Essentially, CLIP can do zero-shot recognition.
It uses contrastive learning to judge the closeness of any two data samples or find the nearest match. Each sample is an image-caption pair. The benefit of contrastive learning is that assessing the closeness of samples is much more efficient than learning to predict or generate a result. And by using readily available image-caption pairs from the web, it can scale up in a self-supervised manner without any manual annotations.
Unfortunately, CLIP is optimized for full-image classification. A naive approach to reuse it for object detection is using a normal detector like a region proposal network (RPN) to get object proposals, crop them as separate images, and submit them with a set of text labels that CLIP can identify. This is a similar architecture to what we use for our product recognition pipeline at a SKU level.
But this exact approach shows poor accuracy. One reason may be that a caption is for the full image and may not contain object-level descriptions. Another reason may be that when objects are cropped, it doesn't have the surrounding visual space to identify them better.
To overcome these problems while retaining CLIP's expressiveness, RegionCLIP reuses CLIP's models with some simple enhancements. Its simplicity makes it a useful template that other vision-language models can follow for zero-shot object detection.
In a nutshell, RegionCLIP is CLIP but for the regions of an image. While CLIP operates on the full image, RegionCLIP learns to reason about visual concepts and text descriptions of regions. It does so through three ideas:
In the next section, we delve into each of these ideas. But first, a note on RegionCLIP's architecture.
RegionCLIP builds on CLIP's architecture. To keep its architecture flexible, it decouples the three visual tasks of region localization, region representation, and object recognition:
We'll now dig into the details of the three main ideas of RegionCLIP.
Plenty of image-caption datasets are out there on the web. Unfortunately, most of these captions tend to describe the overall scene and not the objects therein. So where does one get region-description datasets?
RegionCLIP seeks to generate them from existing datasets as follows:
After this stage, you'll have:
The visual encoder must be taught to select the best description for each region. This is also called aligning the text descriptions with object regions.
For this, RegionCLIP uses knowledge distillation with CLIP's visual encoder as the teacher and its visual encoder as the student. In knowledge distillation, a student model learns to reproduce the teacher model's results. Remember that the student encoder has already been initialized with CLIP's own encoder weights. Now we have to refine it to align image regions with the best text description.
First, pair all the candidate region embeddings with all the description embeddings. This is our training dataset.
The loss function to be minimized has three components:
The result of this training is a pre-trained visual encoder that can do region representation and align image regions with text descriptions.
The final stage is transferring the pre-trained region encoder to an object detector through transfer learning. A stock object detector like faster RCNN with a ResNet50 backbone is used. This backbone is initialized with the pre-trained region encoder.
The network is now trained on a human-annotated detection dataset like the large vocabulary instance segmentation dataset using standard cross-entropy loss. This allows the pre-trained region encoder backbone, its region proposal head, and the classifier head to refine their weights to match the training dataset.
Given a set of unseen images and unseen labels, RegionCLIP produces the following results:
RegionCLIP achieved state-of-the-art results compared to other similar vision-language models:
Vision-language models are being innovated at a rapid pace. After RegionCLIP in 2021, many more capable models have come out:
Object detection — the simple task of identifying all the things in a photo, video, or camera feed — has a wide range of uses cutting across industries. For many years, upgrading such systems to handle new objects required technical expertise and cost money.
But modern zero-shot object detection has finally made it accessible, usable, maintenance-free, and future-proof for everyone, even laypeople. No matter what industry you're in or what business you're running, there's a good chance zero-shot object detection can help you save time, money, or effort. Contact us to learn how!
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