Where Does Visual Representation End and “Understanding” Begin in Computer Vision ???

[hasheramin5-cyber]

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I’m trying to understand Computer Vision beyond the usual “CNNs learn features” explanation, and I’ve been thinking about a deeper question:

If a CNN or Vision Transformer (viTs) learns increasingly complex visual representations, from edges and textures to shapes and semantic features, what does it actually mean for the model to “understand” an image ???

More specifically:

• Are learned visual features actually representing properties of the visual world?
• Or are models primarily learning statistical patterns and shortcuts that correlate with the training labels?
• Where should we draw the boundary between signal processing, feature extraction, visual representation, and semantic understanding?
• How can we experimentally distinguish a model that has learned a meaningful visual representation from one that has simply learned dataset-specific shortcuts?
• If two models achieve similar accuracy but rely on fundamentally different visual features, how would we identify that difference?

I’m especially interested in the experimental side of this question, not just how we define “understanding,” but how we could actually test whether a vision model has learned something meaningful about the underlying visual structure.

Would really realy love to hear how people working deeply in Computer Vision think about this...

[Syed-Asadullah-Nasir]

The tricky part is that correct predictions don’t necessarily mean the model understands the image.

A model might rely on statistical shortcuts rather than the visual concept we think it learned.

A few experiments can help separate the two:

• Break irrelevant correlations: Change the background, lighting, texture, or other factors that shouldn’t affect the label. If performance drops significantly, the model may be relying on a shortcut.

• Use synthetic data: Create images where you control shape, color, texture, position, background, etc. This makes it easier to test whether the model is actually using the feature you care about.

• Test out-of-distribution data: Give the model examples that look different from its training data but still contain the same underlying concept.

• Look beyond accuracy: Two models can have the same accuracy while learning very different representations. Comparing their internal representations and testing them under controlled changes can reveal this.

• Use interventions, not just visualizations: Saliency maps and feature visualizations can be useful, but showing what changes when you manipulate a feature gives stronger evidence about what the model actually relies on.

So I’d separate “the model predicts correctly” from “the model has learned a meaningful representation.”

The interesting question is:

If we change everything that shouldn’t matter, does the model still recognize what actually matters?

That seems like a much more useful experimental definition of meaningful visual understanding.