MENTOR: Efficient Multimodal-Conditioned Tuning for Autoregressive Vision Generation Models

• URL: http://arxiv.org/abs/2507.09574v1
• Date: Sun, 13 Jul 2025 10:52:59 GMT
• Title: MENTOR: Efficient Multimodal-Conditioned Tuning for Autoregressive Vision Generation Models
• Authors: Haozhe Zhao, Zefan Cai, Shuzheng Si, Liang Chen, Jiuxiang Gu, Wen Xiao, Junjie Hu,
• Abstract summary: Recent text-to-image models struggle with precise visual control, balancing multimodal inputs, and requiring extensive training for complex image generation.<n>We propose MENTOR, a novel framework for efficient Multimodal-conditioned Tuning for Autoregressive multimodal image generation.<n>Our method delivers superior image reconstruction fidelity, broad task adaptability, and improved training efficiency compared to diffusion-based methods.
• Score: 30.494968865008513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent text-to-image models produce high-quality results but still struggle with precise visual control, balancing multimodal inputs, and requiring extensive training for complex multimodal image generation. To address these limitations, we propose MENTOR, a novel autoregressive (AR) framework for efficient Multimodal-conditioned Tuning for Autoregressive multimodal image generation. MENTOR combines an AR image generator with a two-stage training paradigm, enabling fine-grained, token-level alignment between multimodal inputs and image outputs without relying on auxiliary adapters or cross-attention modules. The two-stage training consists of: (1) a multimodal alignment stage that establishes robust pixel- and semantic-level alignment, followed by (2) a multimodal instruction tuning stage that balances the integration of multimodal inputs and enhances generation controllability. Despite modest model size, suboptimal base components, and limited training resources, MENTOR achieves strong performance on the DreamBench++ benchmark, outperforming competitive baselines in concept preservation and prompt following. Additionally, our method delivers superior image reconstruction fidelity, broad task adaptability, and improved training efficiency compared to diffusion-based methods. Dataset, code, and models are available at: https://github.com/HaozheZhao/MENTOR

Related papers

• LAMIC: Layout-Aware Multi-Image Composition via Scalability of Multimodal Diffusion Transformer [32.9330637921386]
  LAMIC is a Layout-Aware Multi-Image Composition framework.<n>It extends single-reference diffusion models to multi-reference scenarios in a training-free manner.<n>It consistently outperforms existing multi-reference baselines in ID-S, BG-S, IN-R and AVG scores across all settings.
  arXiv  Detail & Related papers  (2025-08-01T09:51:54Z)
• MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings [75.0617088717528]
  MoCa is a framework for transforming pre-trained VLM backbones into effective bidirectional embedding models.<n>MoCa consistently improves performance across MMEB and ViDoRe-v2 benchmarks, achieving new state-of-the-art results.
  arXiv  Detail & Related papers  (2025-06-29T06:41:00Z)
• Policy Optimized Text-to-Image Pipeline Design [72.87655664038617]
  We introduce a novel reinforcement learning-based framework for text-to-image generation.<n>Our approach first trains an ensemble of reward models capable of predicting image quality scores directly from prompt-workflow combinations.<n>We then implement a two-phase training strategy: initial vocabulary training followed by GRPO-based optimization.
  arXiv  Detail & Related papers  (2025-05-27T17:50:47Z)
• Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation [54.588082888166504]
  We present Mogao, a unified framework that enables interleaved multi-modal generation through a causal approach.<n>Mogoo integrates a set of key technical improvements in architecture design, including a deep-fusion design, dual vision encoders, interleaved rotary position embeddings, and multi-modal classifier-free guidance.<n>Experiments show that Mogao achieves state-of-the-art performance in multi-modal understanding and text-to-image generation, but also excels in producing high-quality, coherent interleaved outputs.
  arXiv  Detail & Related papers  (2025-05-08T17:58:57Z)
• MMGen: Unified Multi-modal Image Generation and Understanding in One Go [60.97155790727879]
  We introduce MMGen, a unified framework that integrates multiple generative tasks into a single diffusion model.<n>Our approach develops a novel diffusion transformer that flexibly supports multi-modal output, along with a simple modality-decoupling strategy.
  arXiv  Detail & Related papers  (2025-03-26T15:37:17Z)
• Multimodal Autoregressive Pre-training of Large Vision Encoders [85.39154488397931]
  We present AIMV2, a family of generalist vision encoders characterized by a straightforward pre-training process. Our encoders excel not only in multimodal evaluations but also in vision benchmarks such as localization, grounding, and classification.
  arXiv  Detail & Related papers  (2024-11-21T18:31:25Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• Improving Cross-modal Alignment for Text-Guided Image Inpainting [36.1319565907582]
  Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. We propose a novel model for TGII by improving cross-modal alignment. Our model achieves state-of-the-art performance compared with other strong competitors.
  arXiv  Detail & Related papers  (2023-01-26T19:18:27Z)
• Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis [52.41439725865149]
  Cross-modality magnetic resonance (MR) image synthesis can be used to generate missing modalities from given ones. Existing (supervised learning) methods often require a large number of paired multi-modal data to train an effective synthesis model. We propose a Multi-scale Transformer Network (MT-Net) with edge-aware pre-training for cross-modality MR image synthesis.
  arXiv  Detail & Related papers  (2022-12-02T11:40:40Z)

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.