Multitask and Multimodal Neural Tuning for Large Models

• URL: http://arxiv.org/abs/2408.03001v1
• Date: Tue, 6 Aug 2024 07:19:51 GMT
• Title: Multitask and Multimodal Neural Tuning for Large Models
• Authors: Hao Sun, Yu Song, Jihong Hu, Yen-Wei Chen, Lanfen Lin,
• Abstract summary: We introduce a novel tuning method called neural tuning, designed to handle diverse multimodal tasks concurrently. Neural tuning emulates sparse distributed representation in human brain, where only specific subsets of neurons are activated for each task. We present a new benchmark, MMUD, where each sample is annotated with multiple task labels.
• Score: 15.34250271841119
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, large-scale multimodal models have demonstrated impressive capabilities across various domains. However, enabling these models to effectively perform multiple multimodal tasks simultaneously remains a significant challenge. To address this, we introduce a novel tuning method called neural tuning, designed to handle diverse multimodal tasks concurrently, including reasoning segmentation, referring segmentation, image captioning, and text-to-image generation. Neural tuning emulates sparse distributed representation in human brain, where only specific subsets of neurons are activated for each task. Additionally, we present a new benchmark, MMUD, where each sample is annotated with multiple task labels. By applying neural tuning to pretrained large models on the MMUD benchmark, we achieve simultaneous task handling in a streamlined and efficient manner. All models, code, and datasets will be publicly available after publication, facilitating further research and development in this field.

Related papers

• 4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities [17.374241865041856]
  We show the possibility of training one model to solve at least 3x more tasks/modalities than existing ones and doing so without a loss in performance. We successfully scale the training to a three billion parameter model using tens of modalities and different datasets. The resulting models and training code are open sourced at 4m.epfl.ch.
  arXiv  Detail & Related papers  (2024-06-13T17:59:42Z)
• Reliable Object Tracking by Multimodal Hybrid Feature Extraction and Transformer-Based Fusion [18.138433117711177]
  We propose a novel multimodal hybrid tracker (MMHT) that utilizes frame-event-based data for reliable single object tracking. The MMHT model employs a hybrid backbone consisting of an artificial neural network (ANN) and a spiking neural network (SNN) to extract dominant features from different visual modalities. Extensive experiments demonstrate that the MMHT model exhibits competitive performance in comparison with other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-05-28T07:24:56Z)
• Generative Multimodal Models are In-Context Learners [60.50927925426832]
  We introduce Emu2, a generative multimodal model with 37 billion parameters, trained on large-scale multimodal sequences. Emu2 exhibits strong multimodal in-context learning abilities, even emerging to solve tasks that require on-the-fly reasoning.
  arXiv  Detail & Related papers  (2023-12-20T18:59:58Z)
• MultiModN- Multimodal, Multi-Task, Interpretable Modular Networks [31.59812777504438]
  We present MultiModN, a network that fuses latent representations in a sequence of any number, combination, or type of modality. We show that MultiModN's sequential MM fusion does not compromise performance compared with a baseline of parallel fusion.
  arXiv  Detail & Related papers  (2023-09-25T13:16:57Z)
• StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data [129.92449761766025]
  We propose a novel data collection methodology that synchronously synthesizes images and dialogues for visual instruction tuning. This approach harnesses the power of generative models, marrying the abilities of ChatGPT and text-to-image generative models. Our research includes comprehensive experiments conducted on various datasets.
  arXiv  Detail & Related papers  (2023-08-20T12:43:52Z)
• An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training [79.78201886156513]
  We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently. Our approach achieves comparable results to single-task state-of-the-art models and demonstrates strong generalization on downstream tasks.
  arXiv  Detail & Related papers  (2023-06-29T17:59:57Z)
• Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning [101.66860222415512]
  Multi-Task Diffusion Model (textscMTDiff) is a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis. For generative planning, we find textscMTDiff outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D.
  arXiv  Detail & Related papers  (2023-05-29T05:20:38Z)
• OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models [72.8156832931841]
  Generalist models are capable of performing diverse multi-modal tasks in a task-agnostic way within a single model. We release a generalist model learning system, OFASys, built on top of a declarative task interface named multi-modal instruction.
  arXiv  Detail & Related papers  (2022-12-08T17:07:09Z)
• Multi-Task Dynamical Systems [5.881614676989161]
  Time series datasets are often composed of a variety of sequences from the same domain, but from different entities. This paper describes the multi-task dynamical system (MTDS); a general methodology for extending multi-task learning (MTL) to time series models. We apply the MTDS to motion-capture data of people walking in various styles using a multi-task recurrent neural network (RNN), and to patient drug-response data using a multi-task pharmacodynamic model.
  arXiv  Detail & Related papers  (2022-10-08T13:37:55Z)
• Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks [73.63892022944198]
  We present a generic perception architecture named Uni-Perceiver. It processes a variety of modalities and tasks with unified modeling and shared parameters. Results show that our pre-trained model without any tuning can achieve reasonable performance even on novel tasks.
  arXiv  Detail & Related papers  (2021-12-02T18:59:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.