Strategies for Testing Foundation Model Flexibility

Most "foundation model" benchmarks are hiding something important. 🎭 Not intentionally, but they're leaving out a critical detail that makes their results nearly useless for real deployment. Medical #SAM3 just called it out, and it changes how we should evaluate every vision foundation model. Here's what the researchers found when they stress-tested SAM3 on medical imaging: 👉 The model only "worked" when given ground-truth bounding boxes. In other words: tell the model exactly where the target is, and it'll refine the boundaries nicely. But ask it to find AND segment using only text prompts? Performance collapsed. ❌ The researchers call these "oracle localization cues," and they're everywhere in foundation model evaluations. It's like testing a radiologist's diagnostic skill by first showing them exactly where the tumor is. Sure, they'll describe it accurately, but that's not the hard part. What changed in Medical SAM3: ✨ ➡️ Full fine-tuning on 33 datasets across 10 modalities (76K+ images) ➡️ Text-driven semantic alignment, not just boundary refinement ➡️ Zero-shot performance jumped from 11.9% to 73.9% Dice score The uncomfortable takeaway:💡 When a "universal" foundation model needs full fine-tuning on 33 domain-specific datasets to actually work... ...maybe we should stop calling them "universal." What this means for ML teams: 🎯 ✅ Don't trust benchmarks that use ground-truth boxes at inference ✅ Budget for domain-specific training data, not just prompt engineering ✅ Test your models on text-only prompts if that's how they'll be deployed Foundation models are powerful tools. But they're not magic, and they're definitely not "segment anything." Research by Chongcong Jiang, Tianxingjian Ding, Chuhan Song, Jiachen Tu, Ziyang Yan, Yihua Shao, Zhenyi Wang, Yuzhang Shang, Tianyu Han, and Yu Tian (University of Central Florida, UCL, University of Illinois Urbana-Champaign, Università di Trento, The Hong Kong Polytechnic University, University of Pennsylvania) Paper link in the comments 📌 #MachineLearning #ComputerVision #MedicalAI #FoundationModels

𝐂𝐚𝐧 𝐆𝐞𝐧𝐞𝐫𝐚𝐥-𝐏𝐮𝐫𝐩𝐨𝐬𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 𝐀𝐝𝐚𝐩𝐭 𝐭𝐨 𝐏𝐥𝐚𝐧𝐭 𝐏𝐡𝐞𝐧𝐨𝐭𝐲𝐩𝐢𝐧𝐠? Foundation models pretrained on internet-scale data have shown remarkable transfer capabilities across many domains. But agricultural images present unique challenges—and full fine-tuning is prohibitively expensive. What's the most efficient path forward? Feng Chen et al. from the University of Edinburgh investigated this question, examining how general-purpose vision foundation models adapt to specialized plant phenotyping tasks. 𝐓𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: When foundation models are pretrained on ImageNet or web-scale data, can they efficiently adapt to the specialized domain of plant phenotyping without full fine-tuning? And how do different adaptation strategies compare? 𝐓𝐡𝐞 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡: The authors tested three foundation models (MAE, DINO, DINOv2) on three essential plant phenotyping tasks: leaf counting, instance segmentation, and disease classification. Critical constraint: they kept the pretrained backbones frozen. They evaluated two parameter-efficient fine-tuning methods: adapter tuning (using LoRA) and decoder tuning. 𝐊𝐞𝐲 𝐟𝐢𝐧𝐝𝐢𝐧𝐠𝐬: - Foundation models can be efficiently adapted to multiple plant phenotyping tasks with minimal fine-tuning - Performance approaches task-specific state-of-the-art models that were designed and trained specifically for each task - However, efficiently fine-tuned foundation models perform slightly worse than specialized SoTA models in some scenarios - The gap highlights an important research direction: understanding when general-domain pretraining suffices versus when domain-specific approaches are needed 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐞𝐝: This work represents a systematic investigation into parameter-efficient adaptation of vision foundation models for agriculture. It demonstrates feasibility while honestly acknowledging performance gaps—setting the stage for subsequent work on crop-specific foundation models that have since shown the value of domain-specific pretraining. The tension between general-purpose and domain-specific foundation models remains a central question in agricultural AI. https://lnkd.in/ejKRK_R8 #ComputerVision #PlantPhenotyping #FoundationModels #DigitalAgriculture #MachineLearning #ICCV2023 #TransferLearning #ParameterEfficientFinetuning #AgTech #AI — Subscribe to 𝘊𝘰𝘮𝘱𝘶𝘵𝘦𝘳 𝘝𝘪𝘴𝘪𝘰𝘯 𝘐𝘯𝘴𝘪𝘨𝘩𝘵𝘴 — weekly briefings on making vision AI work in the real world → https://lnkd.in/guekaSPf

Teaching a model new skills without forgetting old ones. A simple trick makes this possible. Foundation models face a brutal tradeoff: learn something new, lose something old. It's called catastrophic forgetting, and it's why fine-tuning often breaks capabilities you actually need. Researchers just introduced Self-Distillation Fine-Tuning (SDFT) - and the core idea is elegant. Think of it like learning a new language while talking to yourself in your native tongue. The model generates its own training data from its current knowledge, then mixes that with new demonstrations during fine-tuning. No reward functions needed. No complex replay buffers. Just the model teaching itself what it already knows while learning what it doesn't. Key results: 1. Maintains performance on existing tasks while acquiring new skills 2. Works with standard supervised fine-tuning pipelines 3. No explicit reward modeling required (unlike reinforcement learning approaches) This matters because most teams fine-tune models for specific use cases, then watch helplessly as general capabilities degrade. SDFT offers a path to specialization without sacrifice. If you're building on top of foundation models, this is a continual learning approach worth watching. ↓ 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐤𝐞𝐞𝐩 𝐮𝐩? Join my newsletter with 50k+ readers and be the first to learn about the latest AI research: llmwatch.com 💡