How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
EMER, OV-MER (ICML25), AffectGPT (ICML25, Oral), EmoPrefer (ICLR26)
The main features of zeroqiaoba/explainable-multimodal-emotion-reasoning are: Chain Of Thought, Multimodal Datasets.
Open-source alternatives to zeroqiaoba/explainable-multimodal-emotion-reasoning include: lupantech/scienceqa — Data and code for NeurIPS 2022 Paper "Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question… phodal/prompt-patterns — Prompt patterns is a framework for organizing AI-driven system design through structured prompt engineering and… om-ai-lab/vlm-r1 — VLM-R1 is a reasoning vision-language model and embodied AI framework designed to map visual inputs and language… pandabearlab/prompt-tutorial — This project serves as an educational resource and guide for prompt engineering, providing a structured methodology… jacoblee93/fully-local-pdf-chatbot — This project is a private document analysis tool that enables conversational interaction with PDF files by executing… qwenlm/qwen2-vl — Qwen2-VL is a multimodal large language model and vision language model designed to process and reason across text,…
Data and code for NeurIPS 2022 Paper "Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering".
VLM-R1 is a reasoning vision-language model and embodied AI framework designed to map visual inputs and language instructions into physical navigation waypoints and robotic actions. It functions as a multimodal policy optimizer and an open vocabulary detector capable of locating objects based on arbitrary natural language descriptions. The system distinguishes itself through the use of chain-of-thought reasoning and reinforcement learning to solve complex visual and spatial tasks. It utilizes a video semantic memory system, which employs a visual cache to maintain a history of live video for
Prompt patterns is a framework for organizing AI-driven system design through structured prompt engineering and domain-driven development methodologies. It provides a library of standardized interaction strategies designed to improve the consistency, accuracy, and logical reasoning of large language model outputs. By applying these patterns, users can translate complex business scenarios into structured domain models and technical specifications. The project distinguishes itself by integrating domain-driven design principles directly into the prompting workflow. It utilizes techniques such as
This project is a private document analysis tool that enables conversational interaction with PDF files by executing all language model inference and processing entirely on the local machine. By running models directly within the browser or local environment, it ensures that sensitive user data remains offline and inaccessible to external servers or third-party cloud providers. The system utilizes retrieval augmented generation to provide context-aware answers, supported by local document text extraction and vector embedding indexing. This architecture allows for semantic search and informati