In Part 2 of our , we show how foundation models (e.g., GPT-4o, Claude Sonnet 3.5 and YOLO-World) can be used to generate visual inputs (e.g., bounding boxes) for SAM 2. Learn how this approach is likely to disrupt the field of Computer Vision as we know it.
In case you missed Part 1:
- — SAM 2 + GPT-4o Cascading Foundation Models via Visual Prompting.
🔥 Learn more about the cutting edge of and foundation models in our CVPR 2024 series:
- (RAG, Multimodal, Embeddings, and more).
- — Embodied AI, GenAI, Foundation Models, and Video Understanding.
Table of contents
- Cascading Foundation Models
- Foundation Models as Visual Prompting tools
- GPT-4o (or Claude Sonnet 3.5) + SAM 2
- YOLO-World + SAM 2
- Conclusions
1. Cascading Foundation Models
1.1 Recap from Part 1
In , we introduced Segment Anything Model 2 (SAM 2) [1], an object segmentation model for images and video:
- SAM 2 treats images as single-frame videos, using memory to process both images and videos uniformly.
- It allows for promptable segmentation in videos, making predictions and refining them across frames based on user input.
- SAM 2 is trained on the large SA-V dataset, leading to state-of-the-art performance in video segmentation.
Figure 1. SAM 2 applied to a custom video in
Figure 1 demonstrates a practical application of SAM 2. Our provides step-by-step instructions for setting up and running SAM 2 on your own machine.
1.2 What do we mean by cascading foundation models?
Cascading foundation models simply means assembling a pipeline where you use the outputs of one model as inputs for a subsequent model.
You may ask, “But what’s novel about this approach?” 🤔 The answer lies in the Zero-Shot [2] nature of foundation models. Models such as GPT-4o or SAM 2 are called zero-shot, which means they can perform inference without a previous training step. As a result, these models can be connected in a systems perspective, as illustrated in Figure 2.
In fact, some research approaches such as CaFO [3] combine multiple pre-trained foundation models (CLIP, DINO, DALL-E, GPT-3) to enhance few-shot visual recognition by leveraging diverse pre-training knowledge and generating synthetic data.
2. Foundation Models as Visual Prompting tools
2.1 Computer Vision Pipeline 2.0: a new paradigm
We have explored before . It refers to using visual information (like images, bounding boxes, or points) as inputs or “prompts” for foundation models that can process both visual and textual information.
One of the key strengths at is visual search. We process tens of thousands of daily queries using visual prompting. For instance, Figure 3 shows how you can select a bounding box of an object to search for fine-grained details in your data.
Figure 3. Using visual prompting to search for objects (e.g., school buses) even if no class for this object exist
As we have previously argued, at , we believe that the traditional pipeline in the field of vision is at the beginning of a transition where many stages in the pipeline (e.g., labeling, training) will be replaced by modules containing that will form a so-called .
2.2 Challenges of Visual Prompting as a glue for Foundation Models
Now, when you start to connect foundation models together, you immediately notice that building a robust system based on this paradigm is not the same as building a prototype over the weekend.
Here are some of the main challenges you can expect to encounter as soon as you get started:
1. Performance and Scalability 🚀📈
- Ensuring the system can handle high volumes of data and requests in real-time.
- Maintaining accuracy and speed as the scale of operations increases.
2. Integration and Compatibility 🔗🛠️
- Seamlessly incorporating the multi-model system into existing infrastructure.
- Ensuring interoperability with various data formats, APIs, and legacy systems.
3. Reliability and Error Handling 🔄🛡️
- Developing robust error detection and correction mechanisms.
- Implementing redundancies to maintain operational continuity.
3. GPT-4o (or Claude Sonnet 3.5) + SAM 2
3.1 A visual prompting pipeline
Figure 4 shows a simple pipeline consisting of two steps. The assumption is that GPT-4o or Claude Sonnet 3.5 are powerful enough for a prompt such as:
“For the given image, please provide three (x,y) coordinates of the gymnast”
“For the given image, please provide the bounding box coordinates of the gymnast”
Figure 5 and 6 show the results for GPT-4o and Claude Sonnet 3.5 respectively.
While GPT-4o, most of the time, provides incorrect coordinates, Claude Sonnet 3.5 simply refuses to provide either (x,y) coordinates or bounding boxes.
Figure 7 shows how this behaviour was consistent across 200 API requests for GPT-4o.
So, can’t we really use any decent foundation model (in 2024) as input for a second foundation model? 😱
4. YOLO World + SAM 2
4.1 Zero-shot for Computer Vision: YOLO-World
Despite our hopes being crushed by realizing that neither GPT-4o nor Claude Sonnet 3.5 is good enough to provide visual answers from an image, we found a specialized model that made the cut: YOLO-World [4].
Figure 7 shows how, given a text input (i.e., classes), this model accurately predicts the bounding boxes for each of the given inputs! 🍾
YOLO-World’s vocabulary even includes the word: gymnast! (see 0.93 mAP on the right hand side of Figure 8).
- 🔥 Spoiler alert: We’ll be discussing more about YOLO-World (Zero-Shot) vs YOLO v8 (Fine-tuning) in an upcoming post!
For starters, YOLO-World is a zero-shot model for object detection that can detect and localize objects in images without requiring prior training on specific object classes.
We used YOLO-World to provide bounding boxes to SAM 2 as shown in Figure 9.
The only input we were required to provide to the whole system was the class definition for the YOLO-World vocabulary, in this case, “gymnast.” This word is enough for YOLO-World to provide the bounding box coordinates to SAM 2.
Check this for details on the implementation. The final result is shown in Figure 8.
5. Conclusions
In this series about SAM 2, we described and set up the Segment Anything Model 2 (SAM 2). Then, we cascaded two foundation models (i.e., we used the outputs of model A as the inputs for model B) using visual prompting.
We discovered that the two leading in the market, GPT-4o and Claude Sonnet 3.5, are quite inaccurate at providing coordinates or bounding boxes of objects for a given image. Instead, we found that specialized models (e.g., YOLO-World) are better suited for this job.
is one thing, but in reality, there are challenges that even the best ML teams struggle with (e.g., integration, reliability, and adaptability when chaining together foundation models).
How can you address all these challenges for your system while keeping maintenance costs low? 💸 ↘️ . Well, has taken the time to build visual prompting for interconnected foundation models so that you don’t have to.
As we have argued before, a new paradigm in computer vision is here: some traditional stages in a vision pipeline are likely to be replaced by (zero-shot) foundation models that will continually improve over time.
How long do we have to wait to get precise bounding box coordinates from the upcoming GPT member of the family? Probably not long.
🔥 Learn about Multimodal Large Language Models (MLLM):
- 🆕 (MLLMs) transforming Computer Vision
References
[ Segment Anything Model 2
[] An embarrassingly simple approach to zero-shot learning
[] Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners
[] YOLO-World: Real-Time Open-Vocabulary Object Detection
Authors: Jose Gabriel Islas Montero, Dmitry Kazhdan
If you’d like to know more about , try .
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