Fixing CLIP’s Blind Spots: How New Research Tackles AI’s Visual Misinterpretations
Tags: computervision, datascience, machinelearning, ai
Published: 2024-06-17 14:32:15

_Author: [Harpreet Sahota](https://www.linkedin.com/in/harpreetsahota204/) (Hacker in Residence at [Voxel51](https://voxel51.com/))_

## Overview
The paper [“Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs”](https://arxiv.org/abs/2401.06209) investigates the visual question-answering (VQA) capabilities of advanced multimodal large language models (MLLMs), particularly focusing on GPT-4V. It highlights systematic shortcomings in these models’ visual understanding and proposes a benchmark for evaluating their performance.

The authors introduce the Multimodal Visual Patterns (MMVP) benchmark and propose a Mixture of Features (MoF) approach to improve visual grounding in MLLMs.

## No time to read the blog? No worries! Here’s a video of me covering what’s in this blog!

{% embed https://www.youtube.com/watch?v=Iy9xClK65Bs %}

## Existing Challenge

Despite their impressive capabilities, multimodal AI models like GPT-4V often fail to correctly answer basic questions about images. These failures are mostly due to how these models interpret visual information.

## Why Current Methods Fail

The current methods rely heavily on a system called CLIP. CLIP pairs images with text descriptions to create a joint understanding of both. However, CLIP has a significant flaw: it can create what’s known as “CLIP-blind pairs.”

**CLIP-Blind Pairs**

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/1umw6a27sutee4982lm3.png)

When the researchers identify CLIP-blind pairs, they specifically address the issue by proposing a new method called the Mixture of Features (MoF). Here’s a detailed breakdown of what they do and how it helps:

- **Definition:** CLIP-blind pairs are sets of images that CLIP sees as very similar, even though they are quite different.
- **Example:** Imagine two images, one of a cat and one of a dog. If CLIP considers these images similar because they both have furry animals, it might treat them as nearly identical, even though cats and dogs are very different.
- **Impact:** This confusion leads to poor visual representations. When the multimodal model tries to answer questions about these images, it might confuse details or provide incorrect answers because it doesn’t truly understand the visual differences.

These issues with CLIP-blind pairs propagate to more advanced models that use CLIP as their visual backbone. As a result, these models:

- **Give Incorrect Answers:** They might misidentify objects or misunderstand their positions in the image.
- **Hallucinate Explanations:** They sometimes make up explanations for their incorrect answers, which can be misleading.

## The Solution: Mixture of Features (MoF)

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/w219mziazjp79reupnw5.png)
This method aims to improve the visual understanding of multimodal models by integrating better visual representations from another model called DINOv2.

## Proposed Solution

The researchers introduced the Mixture of Features (MoF) approach to tackle these visual shortcomings. MoF aims to improve the visual grounding capabilities of these models by integrating better visual representations.

### How the Solution Works

**Current Method (CLIP):**

- CLIP tries to understand images by comparing them to text descriptions, but it struggles with CLIP-blind pairs, leading to ambiguous or incorrect visual representations.

**Improvements with MoF:**

- **Additive-MoF (A-MoF):** This method combines features from CLIP with another system called DINOv2. The model's overall visual grounding improves by adding features from DINOv2, which better understand visual details. However, this can sometimes reduce the model’s ability to follow text instructions precisely.

- **Interleaved-MoF (I-MoF):** This method spatially mixes visual tokens from CLIP and DINOv2. This more integrated approach ensures that the model benefits from the detailed visual understanding of DINOv2 while maintaining its capability to follow instructions from text.

## Why It’s Better

The MoF approach offers several benefits:

- **Improved Visual Understanding:** By incorporating features from DINOv2, the models become better at distinguishing details in images, reducing errors from CLIP-blind pairs.
- **Balanced Capabilities:** The Interleaved-MoF method ensures that the models understand images and follow text instructions.
- **Systematic Error Reduction:** This approach directly addresses the visual confusion caused by CLIP-blind pairs, leading to more accurate answers.

## Key Contributions

The main contributions of the paper include:

- **Detailed Analysis:** An in-depth study of the visual shortcomings in current multimodal models, particularly those based on CLIP.

- **New Testing Tool:** The Multimodal Visual Patterns (MMVP) benchmark has been introduced to better evaluate how well these models understand images.

- **Improved Method:** The development of the Mixture of Features (MoF) approach, which integrates different types of visual understanding to enhance model performance.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/0roe3wduxj7now55pg0w.png)

## Results
The researchers tested their new method and found:

- All the models, including GPT-4V, struggled with simple visual questions.
- GPT-4V performed better than random guessing but still had significant room for improvement compared to humans.
- The MoF approach significantly improved visual grounding, reducing errors caused by CLIP-blind pairs.

## Real-World Applications

A better visual understanding of AI models can be useful in many fields:

- **Animation and Gaming:** It can help create more realistic characters and interactions.
- **Virtual and Augmented Reality:** It can make VR/AR environments more accurate and immersive.
- **Retail and Online Shopping:** It can improve product searches and recommendations.

## Final Thoughts

The improvements suggested in the paper are important because they improve AI models' understanding of images, which is crucial for many applications. This research helps make high-quality visual understanding more accessible and reliable.

Learn more about the paper by visiting:

- [Project Page](https://tsb0601.github.io/mmvp_blog/)
- [GitHub](https://github.com/tsb0601/MMVP)

**If you’re going to be at CVPR this year, be sure to come and say “Hi!”**

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/wfg54lbx1s3o9g8soukk.png)