awesome-foodai
github.com/andrewzhuzju/awesome-foodai ↗AI Research Resources in Food Domain
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35
Curated Resources
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DatasetsRecognitionRetrievalGenerationInstructional Video AnalysisSurvey
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me instructional video analysis resources from awesome-foodai"
Installation instructions →What's inside
Instructional Video Analysis
- Action Modifiers: Learning from Adverbs in Instructional Videos
- CVF Finding “It”: Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos
- DDLSTM: Dual-Domain LSTM for Cross-Dataset Action Recognition
- Multi-Modal Domain Adaptation for Fine-Grained Action Recognition
- Scaling Egocentric Vision:The EPIC-KITCHENS Dataset
- Towards Automatic Learning of Procedures from Web Instructional Videos
Generation
Retrieval
- Cross-modal Recipe Retrieval: How to Cook This Dish?
- Cross-modal Recipe Retrieval with Rich Food Attributes
- Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings
- Deep Understanding of Cooking Procedure for Cross-modal Recipe Retrieval
- Learning Cross-modal Embeddings for Cooking Recipes and Food Images
- Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
Recognition
- Deep-based Ingredient Recognition for Cooking Recipe Retrieva
- FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging
- Mixed-dish Recognition with Contextual Relation Networks
- Multi-Scale Multi-View Deep Feature Aggregation for Food Recognition
- Wide-Slice Residual Networks for Food Recognition
- Zero-shot Ingredient Recognition by Multi-Relational Graph Convolutional Network
Showing a sample of 35 resources. View the full list on GitHub →