Multimodal & Spatial Computing · core
Vision Models
Classification, detection, segmentation, embeddings, vision transformers, data augmentation, and visual evaluation.
Mental model
Vision systems convert pixels into task-specific spatial representations. Architecture, labels, augmentations, resolution, and evaluation must match deployment conditions.
How to study Vision Models
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use Stanford CS231n to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Vision Models with Multimodal Models, Voice & Audio Systems. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Vision Models and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
- Stanford CS231n (doc)
Practice and explain it back
Design exercise: Vision Models
Classification, detection, segmentation, embeddings, vision transformers, data augmentation, and visual evaluation. Implement designOutline() returning non-empty values for: taskDefinition, representation, visualEval. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with taskDefinition, representation, visualEval plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- Why do CNNs still often beat vision transformers on small datasets?
Build evidence
Synthesize: AI Models & Training
Move from transformer foundations through pre-training, fine-tuning, post-training, compression, and evaluation. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks
Synthesize: Multimodal & Spatial Computing
Connect vision, audio, generation, on-device intelligence, robotics, spatial interfaces, and HCI. Produce one working system, benchmark, or evidence-backed design that integrates the path.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks
Prerequisites
None assigned yet.