Insights: CV Projects

The Three-Legged Stool: Why Most CV Projects Wobble

After interviewing 100+ AI founders on my podcast Impact AI, one pattern emerges repeatedly: the most successful computer vision companies didn't start with the fanciest models—they started by obsessing over data quality and domain expertise. Meanwhile, the cautionary tales always involve teams who thought superior algorithms could overcome everything else.

I created this visualization to capture what separates successful CV solutions from those that struggle to gain traction—it all comes down to building on a stable foundation.

Your CV solution needs three legs to stand:
🔹 𝗗𝗮𝘁𝗮 - Clean, representative, well-annotated datasets
🔹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 - Appropriate algorithms and robust training
🔹 𝗗𝗼𝗺𝗮𝗶𝗻 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 - Deep understanding of the real-world problem

Some teams obsess over one leg while neglecting the others. The result? A wobbly solution that can't handle production realities.

𝗗𝗮𝘁𝗮 𝗶𝘀𝘀𝘂𝗲𝘀? Garbage in, garbage out. No amount of clever modeling can overcome fundamentally flawed training data.

𝗡𝗼 𝘀𝘂𝗯𝗷𝗲𝗰𝘁 𝗺𝗮𝘁𝘁𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝘁 (𝗦𝗠𝗘) 𝗶𝗻𝗽𝘂𝘁? You'll build something technically impressive that solves the wrong problem or misses critical edge cases.

𝗪𝗲𝗮𝗸 𝗺𝗼𝗱𝗲𝗹? Even perfect data won't help if your approach is fundamentally unsuited to the task.

The strongest CV deployments I've worked on had balanced investment across all three areas. The failures? They typically had one strong leg and two weak ones.

Before your next sprint planning, ask yourself: 𝗗𝗼𝗲𝘀 𝘆𝗼𝘂𝗿 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗰𝗮𝗿𝗿𝘆 𝗶𝘁𝘀 𝘄𝗲𝗶𝗴𝗵𝘁 𝗼𝗻 𝗲𝗮𝗰𝗵 𝗹𝗲𝗴?

Which leg is your team neglecting?

Research: Fine-tuning Foundation Models

Towards Flood Extent Forecasting: Evaluating a Weather Foundation Model and U-Net for Flood Forecasting

Flood forecasting presents unique ML challenges: multi-modal data fusion (meteorological, geographical, and soil variables), high-resolution spatial modeling, and capturing complex temporal dynamics. While foundation models promise transfer learning benefits, they can struggle with domain adaptation from global patterns to local contexts.

Eric Wanjau and Samuel Maina explored data-driven approaches to flood extent forecasting in Rwanda, a region particularly vulnerable to flooding due to its mountainous terrain and increasingly frequent heavy rainfall.

They compared three approaches:
- A standard U-Net architecture
- A ClimaX variant trained from scratch
- Fine-tuned ClimaX model

ClimaX is a transformer-based weather and climate foundation model. They found that a ClimaX variant trained from scratch with a linear projection decoder outperformed both the U-Net baseline and the fine-tuned ClimaX models.

Perhaps most interesting was that pre-training on coarse global climate data didn't transfer effectively to the high-resolution local forecasting task in Rwanda.

This suggests that foundation models might need region-specific pre-training at appropriate resolutions to bridge the gap between global patterns and local flood dynamics.

Research: Distribution Shift

Predicting out-of-domain performance under geographic distribution shifts

When developing machine learning models for satellite imagery, we face a common dilemma: data is plentiful in some regions but scarce in others. How can we know in advance if a model trained on data-rich areas will work effectively when applied to regions with limited training data?

Haoran Zhang et al. demonstrate that certain domain distance measures can serve as reliable predictors of how well models will transfer between geographic regions - even without labeled data from the target region.

They found that when calculating distances between domain-specific data distributions (using image and location embeddings), larger distances typically correlate with greater performance drops during domain adaptation. This relationship held across different datasets, geographic domain definitions, and model architectures.

This approach could improve how we deploy machine learning models across different geographic regions, helping to address the uneven distribution of data availability while ensuring models perform reliably where they're needed most.

Insights: Spurious Correlations

Uncomfortable Trade-off in AI Medical Imaging: Performance vs. True Learning

When developing medical imaging AI models, we often face a difficult question: Is your model truly learning clinically relevant features, or just exploiting shortcuts in your data?

During my recent webinar on bias and batch effects in medical imaging, an insightful question emerged: "Have you ever successfully implemented adversarial training to remove spurious feature prediction without degrading performance on target metrics?"

Here's the uncomfortable truth: When you implement adversarial training to prevent your model from using spurious correlations, you should expect some performance degradation. This isn't a bug—it's a feature of responsible AI development.

Why? Because you're essentially telling your model, "Stop taking shortcuts." When you force your model to ignore easy correlations that don't generalize well (like scanner type, acquisition parameters, or institutional patterns), you're making its job harder—but ultimately more clinically valuable.

The real goal isn't to maintain artificially high performance metrics at all costs, but to build models that learn genuine medical signals that will generalize across diverse populations and clinical settings.

So the next time your adversarial training causes your metrics to dip, consider whether your model is becoming less impressive on paper but more reliable in practice.

Have you experienced this trade-off in your work? How do you balance performance metrics against more robust feature learning? I'd love to hear your experiences.

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