DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation

• URL: http://arxiv.org/abs/2403.08857v2
• Date: Wed, 3 Jul 2024 13:09:53 GMT
• Title: DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation
• Authors: Minbin Huang, Yanxin Long, Xinchi Deng, Ruihang Chu, Jiangfeng Xiong, Xiaodan Liang, Hong Cheng, Qinglin Lu, Wei Liu,
• Abstract summary: We propose DialogGen to align off-the-shelf MLLMs and T2I models to build a Multi-modal Interactive Dialogue System. It is composed of drawing prompt alignment, careful training data curation, and error correction. Our experiments on DialogGen and user study demonstrate the effectiveness of DialogGen compared with other State-of-the-Art models.
• Score: 46.085482021301516
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image (T2I) generation models have significantly advanced in recent years. However, effective interaction with these models is challenging for average users due to the need for specialized prompt engineering knowledge and the inability to perform multi-turn image generation, hindering a dynamic and iterative creation process. Recent attempts have tried to equip Multi-modal Large Language Models (MLLMs) with T2I models to bring the user's natural language instructions into reality. Hence, the output modality of MLLMs is extended, and the multi-turn generation quality of T2I models is enhanced thanks to the strong multi-modal comprehension ability of MLLMs. However, many of these works face challenges in identifying correct output modalities and generating coherent images accordingly as the number of output modalities increases and the conversations go deeper. Therefore, we propose DialogGen, an effective pipeline to align off-the-shelf MLLMs and T2I models to build a Multi-modal Interactive Dialogue System (MIDS) for multi-turn Text-to-Image generation. It is composed of drawing prompt alignment, careful training data curation, and error correction. Moreover, as the field of MIDS flourishes, comprehensive benchmarks are urgently needed to evaluate MIDS fairly in terms of output modality correctness and multi-modal output coherence. To address this issue, we introduce the Multi-modal Dialogue Benchmark (DialogBen), a comprehensive bilingual benchmark designed to assess the ability of MLLMs to generate accurate and coherent multi-modal content that supports image editing. It contains two evaluation metrics to measure the model's ability to switch modalities and the coherence of the output images. Our extensive experiments on DialogBen and user study demonstrate the effectiveness of DialogGen compared with other State-of-the-Art models.

Related papers

• MAGID: An Automated Pipeline for Generating Synthetic Multi-modal Datasets [29.737965533532577]
  Multimodal Augmented Generative Images Dialogues (MAGID) is a framework to augment text-only dialogues with diverse and high-quality images. Our results show that MAGID is comparable to or better than baselines, with significant improvements in human evaluation.
  arXiv  Detail & Related papers  (2024-03-05T18:31:28Z)
• Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception [63.03288425612792]
  We propose bfAnyRef, a general MLLM model that can generate pixel-wise object perceptions and natural language descriptions from multi-modality references. Our model achieves state-of-the-art results across multiple benchmarks, including diverse modality referring segmentation and region-level referring expression generation.
  arXiv  Detail & Related papers  (2024-03-05T13:45:46Z)
• DialCLIP: Empowering CLIP as Multi-Modal Dialog Retriever [83.33209603041013]
  We propose a parameter-efficient prompt-tuning method named DialCLIP for multi-modal dialog retrieval. Our approach introduces a multi-modal context generator to learn context features which are distilled into prompts within the pre-trained vision-language model CLIP. To facilitate various types of retrieval, we also design multiple experts to learn mappings from CLIP outputs to multi-modal representation space.
  arXiv  Detail & Related papers  (2024-01-02T07:40:12Z)
• CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation [88.33780780220091]
  CoDi-2 is a versatile and interactive Multimodal Large Language Model (MLLM) It can follow complex multimodal interleaved instructions, conduct in-context learning (ICL), reason, chat, edit, etc.
  arXiv  Detail & Related papers  (2023-11-30T18:21:25Z)
• DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention [55.2825684201129]
  DeepSpeed-VisualChat is designed to optimize Large Language Models (LLMs) by incorporating multi-modal capabilities. Our framework is notable for (1) its open-source support for multi-round and multi-image dialogues, (2) introducing an innovative multi-modal causal attention mechanism, and (3) utilizing data blending techniques on existing datasets to assure seamless interactions.
  arXiv  Detail & Related papers  (2023-09-25T17:53:29Z)
• IMAD: IMage-Augmented multi-modal Dialogue [0.043847653914745384]
  This paper presents a novel perspective on multi-modal dialogue systems, which interprets the image in the context of the dialogue. We propose a two-stage approach to automatically construct a multi-modal dialogue dataset. In the first stage, we utilize text-to-image similarity and sentence similarity to identify which utterances could be replaced with an image. In the second stage, we replace those utterances by selecting a subset of relevant images and filtering them with a visual question answering model.
  arXiv  Detail & Related papers  (2023-05-17T18:38:10Z)
• mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video [89.19867891570945]
  mPLUG-2 is a new unified paradigm with modularized design for multi-modal pretraining. It shares common universal modules for modality collaboration and disentangling different modality modules to deal with modality entanglement. It is flexible to select different modules for different understanding and generation tasks across all modalities including text, image, and video.
  arXiv  Detail & Related papers  (2023-02-01T12:40:03Z)

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.