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Franklin Wang

3 accepted papers

2024

A Multimodal Automated Interpretability Agent

ICML 2024poster

This paper describes MAIA, a Multimodal Automated Interpretability Agent. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpretation and failure mode discovery. It equips a pre-trained vision-language model with a set of tools that support itera…

Cited by 67SourcePDFScholar
2024

Nearest Neighbor Normalization Improves Multimodal Retrieval

EMNLP 2024main

Multimodal models leverage large-scale pretraining to achieve strong but still imperfect performance on tasks such as image captioning, visual question answering, and cross-modal retrieval. In this paper, we present a simple and efficient method for correcting errors in trained contrastive image-tex…

2020

Deep Shape from Polarization

ECCV 2020poster

This paper makes a first attempt to bring the Shape from Polarization (SfP) problem to the realm of deep learning. The previous state-of-the-art methods for SfP have been purely physics-based. We see value in these principled models, and blend these physical models as priors into a neural network ar…