EE Seminar: Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language Understanding

13 באוקטובר 2024, 15:00 
אולם 011, בניין כיתות-חשמל 

Electrical Engineering Systems Seminar

 

Speaker: Michael Fiman

M.Sc. student under the supervision of Prof. Hadar Averbuch-Elor

 

Sunday, 13th October 2024, at 15:00

Room 011, Kitot Building, Faculty of Engineering

 

Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language Understanding

Abstract

Most humans use visual imagination to understand and reason about language, but models such as BERT reason about language using knowledge acquired during text-only pretraining. In this work, we investigate whether vision-and-language pretraining can improve performance on text-only tasks that involve implicit visual reasoning, focusing primarily on zero-shot probing methods. We propose a suite of visual language understanding (VLU) tasks for probing the visual reasoning abilities of text encoder models, as well as various non-visual natural language understanding (NLU) tasks for comparison. We also contribute a novel zero-shot knowledge probing method, Stroop probing, for applying models such as CLIP to text-only tasks without needing a prediction head such as the masked language modeling head of models like BERT. We show that SOTA multimodally trained text encoders outperform unimodally trained text encoders on the VLU tasks while being underperformed by them on the NLU tasks, lending new context to previously mixed results regarding the NLU capabilities of multimodal models. We conclude that exposure to images during pretraining affords inherent visual reasoning knowledge that is reflected in language-only tasks that require implicit visual reasoning. Our findings bear importance in the broader context of multimodal learning, providing principled guidelines for the choice of text encoders used in such contexts.

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