#Multimodal

Discover curated tutorials on Large Language Models and AI Painting for all skill levels. Explore recent AI developments in multimodal learning and AI systems with courses from leading universities. Engage with resources on prompt engineering and generative AI methods without unnecessary embellishments. Ideal for staying informed about AI advancements.

AgentChain employs advanced Large Language Models to coordinate tasks among multiple agents, supporting a range of applications such as natural language processing, image analysis, and communication. This tool offers adaptability with customizable agents for specific projects, providing comprehensive solutions from concept to delivery. With inputs and outputs across text, image, audio, and data, AgentChain delivers efficient solutions for complex challenges with its multimodal features.

This guide covers a comprehensive tutorial on implementing the CLIP model in PyTorch, demonstrating its ability to link textual input with relevant image retrieval. By integrating established research examples and benchmark results, it explains the core principles of Contrastive Language-Image Pre-training, surpassing conventional classifiers such as those optimized for ImageNet. The content includes essential processes like encoding and projecting multimodal data, detailing the CLIP model's architecture and loss calculation while highlighting its applicability in advanced research and practical applications.

This project uses a post-training self-supervised diffusion approach to enhance CLIP models. By integrating text-to-image generative feedback, it enhances visual precision across benchmarks, boosting performance by 3-7% on the MMVP-VLM. It retains CLIP's zero-shot ability across 29 classification benchmarks, while acting as a new Visual Assistant for improved multimodal insights.

ULIP provides a model-agnostic framework for multimodal pre-training, combining image and language data for advanced 3D understanding without added latency. Compatible with models like Pointnet2, PointBERT, PointMLP, and PointNeXt, it supports tasks such as zero-shot classification. It includes official implementations, pre-trained models, and datasets, allowing customization and integration for varied 3D data processing needs.

Xtreme1 is an open-source platform that enhances data annotation and ontology management for machine learning, particularly in computer vision and language models. It offers AI-driven tools to improve annotation efficiency, supporting tasks like 2D/3D object detection, segmentation, and LiDAR-Camera Fusion. The platform, available with a free plan on Xtreme1 Cloud, includes features such as dataset support, pre-labeling, interactive models, and an ontology center. It facilitates model visualization and quality monitoring and is compatible with Docker and NVIDIA GPU for model support.

SWIFT delivers a scalable framework for the training and deployment of over 350 language models and 100 multimodal models. It includes a comprehensive library of adapters for integrating advanced techniques like NEFTune and LoRA+, allowing for seamless workflow integration without proprietary scripts. With a Gradio web interface and abundant documentation, SWIFT enhances accessibility in deep learning, benefiting both beginners and professionals by improving model training efficiency.

This literature review examines the development of recommender systems with a focus on foundational models that do not rely on explicit ID features. It discusses the potential for these systems to evolve independently, akin to foundational models in natural language processing and computer vision, and the ongoing debate regarding the necessity of ID embeddings. The review further explores how Large Language Models (LLMs) may transform recommender systems by shifting focus from matching to generative paradigms. Additionally, it highlights advancements in multimodal and transferable recommender systems, offering insights from empirical research into universal user representation. This review serves as a comprehensive guide to understanding current trends and future directions in the field of recommender systems.

Examine the collection of papers on parameter-efficient transfer learning aimed at computer vision and multimodal fields. The collection focuses on methods for efficiently adapting large-scale pre-trained models, minimizing overfitting risks and storage requirements associated with comprehensive fine-tuning. Utilizing insights from NLP, the papers enhance applications in image classification, prompt learning, and multimodal tasks. This project provides a thorough overview of advancements and methodologies in optimizing transfer learning across various computational landscapes.

VITA is an open-source model that processes video, image, text, and audio simultaneously, enhancing capabilities in multilingual, vision, and audio tasks. It features non-awakening and audio interrupt interactions for real-time queries without manual activation, employing state token differentiation and a duplex scheme for adaptive responses during user interruptions. VITA's advanced processing abilities support diverse multimodal applications.

Explore a curated repository of foundation models designed to improve reasoning in language, vision, and multimodal contexts. The database classifies models and outlines their use in commonsense, mathematical, logical reasoning, and more. Additionally, it covers reasoning techniques like pre-training and fine-tuning. Contributions are welcome to broaden the resource collection for AI reasoning advances.

Discover the LLMGA project, a multimodal assistant for image generation and editing utilizing Large Language Models. This project enhances prompt accuracy for detailed and interpretable outcomes. It includes a two-phase training process aligning MLLMs with Stable Diffusion models, offering reference-based restoration to harmonize texture and brightness. Suitable for creating interactive designs across various formats, with multilingual support and plugin integration. Learn about its models, datasets, and novel tools supporting both English and Chinese.

Explore FROMAGe, a versatile framework that connects language models to images, enhancing multimodal input and output capabilities. It offers pretrained model weights and extensive documentation for seamless image retrieval and contextual understanding. The repository includes essential code for replicating image-text alignment tasks using Conceptual Captions datasets. FROMAGe excels in image generation and retrieval, supported by thorough evaluation scripts. Built for flexibility, it supports multiple visual model settings and reduces disk usage via model weight pruning. Try the interactive Gradio demo for practical insights.

CRAB is a versatile framework for deploying and evaluating multimodal language model agents across diverse environments, utilizing intuitive configuration and detailed benchmarking metrics.

This collection provides a wide range of papers on contrastive self-supervised learning, useful for scholars and industry professionals. Regular updates ensure coverage of various topics such as topic modeling, vision-language representation, 3D medical image analysis, and multimodal sentiment analysis. Each paper entry includes links to the paper and code, if available, facilitating access to cutting-edge methods and experimental setups. Well-suited for those aiming to enhance their understanding of recent progress in contrastive learning, this collection serves as an essential reference for its comprehensive scope and pertinence.

The i-Code project develops integrative and composable AI technologies aimed at advancing multimodal learning across datasets like vision, language, and speech. It includes i-Code V1 for foundational multimodal models, i-Code V2 for autoregressive generation, i-Code V3 for any-to-any diffusion, and i-Code Studio for configurable AI frameworks. The project further strengthens document intelligence via i-Code Doc and facilitates knowledge-based visual question answering with MM-Reasoner, encouraging contributions under the Microsoft open-source code of conduct.

BLIVA offers a streamlined approach to handling visual questions abundant in text, securing significant rankings in both perception and cognition tasks. Featuring models that are commercially and openly accessible, BLIVA demonstrates high efficacy in multiple VQA benchmarks, providing precise insights across varied datasets.

This repository presents state-of-the-art emotion recognition models in conversational contexts, featuring frameworks such as COSMIC, DialogGCN, and DialogueRNN. Utilizing commonsense reasoning, graph-based approaches, and recurrent networks, these models are designed to detect emotions effectively. The project also includes tools for emotion cause analysis across datasets like IEMOCAP, MELD, and EmoryNLP, aiming to facilitate understanding of inter-party dynamics and context-related challenges in dialogues, thereby contributing to empathetic dialogue creation.

SEED-Bench offers a structured evaluation setup for multimodal large language models with 28K expertly annotated multiple-choice questions across 34 dimensions. Encompassing both text and image generation evaluations, it includes iterations like SEED-Bench-2 and SEED-Bench-2-Plus. Designed to assess model comprehension in complex text scenarios, SEED-Bench is a valuable resource for researchers and developers looking to compare and enhance model performance. Explore datasets and engage with the leaderboard now.

The GILL model efficiently generates and retrieves images through interleaved text and image processing. Access the model's code, pretrained weights, and comprehensive setup instructions for inference and training. Utilize Conceptual Captions for model training and extensive evaluation scripts for performance testing. The Gradio demo facilitates practical exploration for researchers and developers interested in multimodal language models.

The platform presents a comprehensive overview of the application of multimodal large language models (MLLMs) in remote sensing. It includes carefully curated resources like model architectures, training processes, datasets, and evaluation benchmarks. The site continuously updates to reflect the latest in the field, emphasizing intelligent agents, instruction tuning, and the vision-language interface, while documenting contributions from numerous researchers to provide the latest on remote sensing MLLMs.