UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style

• URL: http://arxiv.org/abs/2402.10381v2
• Date: Wed, 17 Apr 2024 13:46:56 GMT
• Title: UMAIR-FPS: User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style
• Authors: Yan Kang, Hao Lin, Mingjian Yang, Shin-Jye Lee,
• Abstract summary: We propose the User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style (UMAIR-FPS) In the feature extract phase, for image features, we are the first to combine image painting style features with semantic features to construct a dual-output image encoder. For text features, we obtain text embeddings based on fine-tuning Sentence-Transformers. In the multi-modal fusion phase, we novelly propose a user-aware multi-modal contribution measurement mechanism.
• Score: 5.441554441737648
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of high-quality image generation models based on AI has generated a deluge of anime illustrations. Recommending illustrations to users within massive data has become a challenging and popular task. However, existing anime recommendation systems have focused on text features but still need to integrate image features. In addition, most multi-modal recommendation research is constrained by tightly coupled datasets, limiting its applicability to anime illustrations. We propose the User-aware Multi-modal Animation Illustration Recommendation Fusion with Painting Style (UMAIR-FPS) to tackle these gaps. In the feature extract phase, for image features, we are the first to combine image painting style features with semantic features to construct a dual-output image encoder for enhancing representation. For text features, we obtain text embeddings based on fine-tuning Sentence-Transformers by incorporating domain knowledge that composes a variety of domain text pairs from multilingual mappings, entity relationships, and term explanation perspectives, respectively. In the multi-modal fusion phase, we novelly propose a user-aware multi-modal contribution measurement mechanism to weight multi-modal features dynamically according to user features at the interaction level and employ the DCN-V2 module to model bounded-degree multi-modal crosses effectively. UMAIR-FPS surpasses the stat-of-the-art baselines on large real-world datasets, demonstrating substantial performance enhancements.

Related papers

• MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model [49.931663904599205]
  MaVEn is an innovative framework designed to enhance the capabilities of Multimodal Large Language Models (MLLMs) in multi-image reasoning. We show that MaVEn significantly enhances MLLMs' understanding in complex multi-image scenarios, while also improving performance in single-image contexts.
  arXiv  Detail & Related papers  (2024-08-22T11:57:16Z)
• ARMADA: Attribute-Based Multimodal Data Augmentation [93.05614922383822]
  Attribute-based Multimodal Data Augmentation (ARMADA) is a novel multimodal data augmentation method via knowledge-guided manipulation of visual attributes. ARMADA is a novel multimodal data generation framework that: (i) extracts knowledge-grounded attributes from symbolic KBs for semantically consistent yet distinctive image-text pair generation. This also highlights the need to leverage external knowledge proxies for enhanced interpretability and real-world grounding.
  arXiv  Detail & Related papers  (2024-08-19T15:27:25Z)
• VIP: Versatile Image Outpainting Empowered by Multimodal Large Language Model [76.02314305164595]
  This work presents a novel image outpainting framework that is capable of customizing the results according to the requirement of users. We take advantage of a Multimodal Large Language Model (MLLM) that automatically extracts and organizes the corresponding textual descriptions of the masked and unmasked part of a given image. In addition, a special Cross-Attention module, namely Center-Total-Surrounding (CTS), is elaborately designed to enhance further the the interaction between specific space regions of the image and corresponding parts of the text prompts.
  arXiv  Detail & Related papers  (2024-06-03T07:14:19Z)
• MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation [22.69019130782004]
  We present MoMA: an open-vocabulary, training-free personalized image model that boasts flexible zero-shot capabilities. We train MoMA to serve a dual role as both a feature extractor and a generator. We introduce a novel self-attention shortcut method that efficiently transfers image features to an image diffusion model.
  arXiv  Detail & Related papers  (2024-04-08T16:55:49Z)
• FSMR: A Feature Swapping Multi-modal Reasoning Approach with Joint Textual and Visual Clues [20.587249765287183]
  Feature Swapping Multi-modal Reasoning (FSMR) model is designed to enhance multi-modal reasoning through feature swapping. FSMR incorporates a multi-modal cross-attention mechanism, facilitating the joint modeling of textual and visual information. Experiments on the PMR dataset demonstrate FSMR's superiority over state-of-the-art baseline models.
  arXiv  Detail & Related papers  (2024-03-29T07:28:50Z)
• UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion [36.06457895469353]
  UNIMO-G is a conditional diffusion framework that operates on multimodal prompts with interleaved textual and visual inputs. It excels in both text-to-image generation and zero-shot subject-driven synthesis.
  arXiv  Detail & Related papers  (2024-01-24T11:36:44Z)
• 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)
• Encoder Fusion Network with Co-Attention Embedding for Referring Image Segmentation [87.01669173673288]
  We propose an encoder fusion network (EFN), which transforms the visual encoder into a multi-modal feature learning network. A co-attention mechanism is embedded in the EFN to realize the parallel update of multi-modal features. The experiment results on four benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-05-05T02:27:25Z)
• TediGAN: Text-Guided Diverse Face Image Generation and Manipulation [52.83401421019309]
  TediGAN is a framework for multi-modal image generation and manipulation with textual descriptions. StyleGAN inversion module maps real images to the latent space of a well-trained StyleGAN. visual-linguistic similarity learns the text-image matching by mapping the image and text into a common embedding space. instance-level optimization is for identity preservation in manipulation.
  arXiv  Detail & Related papers  (2020-12-06T16:20:19Z)

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.