Decoding Long-CLIP: Understand the Power of Zero-Shot Classification

Introduction

CLIP, has been a tool for text-image tasks, widely known for zero-shot classification, text-image retrieval and much more. However, the model has certain limitations due to its short text input, which is restricted to 77. Long-CLIP, released in 22 March 2024, addresses this by supporting longer text inputs without sacrificing its zero-shot performance. This improvement comes with challenges like maintaining original capabilities and costly pretraining. Long-CLIP offers efficient fine-tuning methods, resulting in significant performance gains over CLIP in tasks like long caption retrieval and traditional text-image retrieval. Additionally, it enhances image generation from detailed text descriptions seamlessly.

In this article we will perform zero-shot image classification using Long-CLIP and understand the underlying concept of the model.

Prerequisites

• Basic Machine Learning Knowledge: Familiarity with supervised and unsupervised learning.
• Understanding of Transformers: Knowledge of transformer models and their architecture.
• Computer Vision Basics: Concepts like image representation, feature extraction, and classification.
• Intro to CLIP: Awareness of how CLIP combines text and image embeddings for tasks.
• Python Proficiency: Experience with Python for running model implementations.