The paper introduces the FALCON framework which aims to interpret and explain the feature representations learned by vision models. FALCON employs pre-trained vision models to extract feature representations from images, identifies "highly activating images" for these features, and then uses the CLIP model to generate captions for these images. These captions provide insights into the underlying concepts or attributes that the feature may be capturing.

FALCON can help you understand what sort of inputs the feature is sensitive to or what kind of 'knowledge' it might be capturing. The interpretability that FALCON provides can aid in model debugging, it can offer insights for further refinement of the model, and it can enhance trust in the model by offering a level of transparency about how the model works.

https://proceedings.mlr.press/v202/kalibhat23a/kalibhat23a.pdf

Difference from simCLR

• SimCLR aims to learn abstract representations that capture the semantic content of images for tasks like image classification, FALCON aims to directly associate visual features with natural language concepts, thus serving a different, but complementary, role in understanding visual data.
  • FALCON: words → visual features
  • SimCLR: image pixel → intrinsic representations (self labeling, no captions that restrict the space of mappings)

FALCON identifies a particular feature within an image, generates descriptive captions for relevant portions of the image using a large captioning dataset and a pre-trained vision-language model, and then determines the most representative words or concepts associated with the feature. This process allows the system to closely and accurately describe the target feature in a way that humans can understand.

We use a dataset that stores lots of caption data.

Uses Large captioning dataset (like LAION400m): This is a substantial dataset used for training the AI, composed of images and corresponding descriptions (or captions). LAION400m appears to be one such dataset.

• Image with caption

We then use a pre-trained model CLIP that understands and interprets both images and text, making it ideal for this captioning task.

Each word in the captions generated for the highly activating cropped images is evaluated (scored) and arranged (ranked). This process helps identify the most relevant or descriptive words related to the target feature.

Introduction

Understanding what needs to be encoded for generalized representation learning is tough.

FALCON

We are also particularly interested in understanding final-layer representations since they alone are accessible to downstream tasks, and their richness and quality is shown to be essential for better generalization