If CLIP Could Talk: Understanding Vision-Language Model Representations
  Through Their Preferred Concept Descriptions
Reza Esfandiarpoor Cristina Menghini Stephen H. Bach
Recent works often assume that Vision-Language Model (VLM) representations
are based on visual attributes like shape. However, it is unclear to what
extent VLMs prioritize this information to represent concepts. We propose
Extract and Explore (EX2), a novel approach to characterize important textual
features for VLMs. EX2 uses reinforcement learning to align a large language
model with VLM preferences and generates descriptions that incorporate the
important features for the VLM. Then, we inspect the descriptions to identify
the features that contribute to VLM representations. We find that spurious
descriptions have a major role in VLM representations despite providing no
helpful information, e.g., Click to enlarge photo of CONCEPT. More importantly,
among informative descriptions, VLMs rely significantly on non-visual
attributes like habitat to represent visual concepts. Also, our analysis
reveals that different VLMs prioritize different attributes in their
representations. Overall, we show that VLMs do not simply match images to scene
descriptions and that non-visual or even spurious descriptions significantly
influence their representations.