Color in Visual-Language Models: CLIP deficiencies

TL;DR

This paper investigates how CLIP encodes color, revealing biases and deficiencies in its color representation, and analyzes neuron-level features to understand and improve its color understanding capabilities.

Contribution

It identifies specific biases and neuron-level mechanisms in CLIP's color encoding, proposing directions for refining multimodal color representations.

Findings

CLIP shows bias against achromatic stimuli in color labeling.

CLIP tends to prioritize text over visual information in color recognition.

Neuron analysis reveals a dominance of text-selective neurons and fewer multi-modal color neurons.

Abstract

This work explores how color is encoded in CLIP (Contrastive Language-Image Pre-training) which is currently the most influential VML (Visual Language model) in Artificial Intelligence. After performing different experiments on synthetic datasets created for this task, we conclude that CLIP is able to attribute correct color labels to colored visual stimulus, but, we come across two main deficiencies: (a) a clear bias on achromatic stimuli that are poorly related to the color concept, thus white, gray and black are rarely assigned as color labels; and (b) the tendency to prioritize text over other visual information. Here we prove it is highly significant in color labelling through an exhaustive Stroop-effect test. With the aim to find the causes of these color deficiencies, we analyse the internal representation at the neuron level. We conclude that CLIP presents an important amount of neurons selective to text, specially in deepest layers of the network, and a smaller amount of multi-modal color neurons which could be the key of understanding the concept of color properly. Our investigation underscores the necessity of refining color representation mechanisms in neural networks to foster a more comprehensive comprehension of colors as humans understand them, thereby advancing the efficacy and versatility of multimodal models like CLIP in real-world scenarios.