VL-SAE: Interpreting and Enhancing Vision-Language Alignment with a
  Unified Concept Set
Authors: name: Shufan Shen, name: Junshu Sun, name: Qingming Huang, name: Shuhui Wang
Organization: ucas

The alignment of vision-language representations endows current
Vision-Language Models (VLMs) with strong multi-modal reasoning capabilities.
However, the interpretability of the alignment component remains uninvestigated
due to the difficulty in mapping the semantics of multi-modal representations
into a unified concept set. To address this problem, we propose VL-SAE, a
sparse autoencoder that encodes vision-language representations into its hidden
activations. Each neuron in its hidden layer correlates to a concept
represented by semantically similar images and texts, thereby interpreting
these representations with a unified concept set. To establish the
neuron-concept correlation, we encourage semantically similar representations
to exhibit consistent neuron activations during self-supervised training.
First, to measure the semantic similarity of multi-modal representations, we
perform their alignment in an explicit form based on cosine similarity. Second,
we construct the VL-SAE with a distance-based encoder and two modality-specific
decoders to ensure the activation consistency of semantically similar
representations. Experiments across multiple VLMs (e.g., CLIP, LLaVA)
demonstrate the superior capability of VL-SAE in interpreting and enhancing the
vision-language alignment. For interpretation, the alignment between vision and
language representations can be understood by comparing their semantics with
concepts. For enhancement, the alignment can be strengthened by aligning
vision-language representations at the concept level, contributing to
performance improvements in downstream tasks, including zero-shot image
classification and hallucination elimination. Codes are available at
https://github.com/ssfgunner/VL-SAE.