A Framework For Image Synthesis Using Supervised Contrastive Learning

• URL: http://arxiv.org/abs/2412.03957v1
• Date: Thu, 05 Dec 2024 08:15:37 GMT
• Title: A Framework For Image Synthesis Using Supervised Contrastive Learning
• Authors: Yibin Liu, Jianyu Zhang, Li Zhang, Shijian Li, Gang Pan,
• Abstract summary: Text-to-image (T2I) generation aims at producing realistic images corresponding to text descriptions. We propose a framework leveraging both inter- and inner-modal correspondence by label guided supervised contrastive learning. We demonstrate our framework on four novel T2I GANs by both single-object dataset CUB and multi-object dataset COCO.
• Score: 14.016543383212706
• License:
• Abstract: Text-to-image (T2I) generation aims at producing realistic images corresponding to text descriptions. Generative Adversarial Network (GAN) has proven to be successful in this task. Typical T2I GANs are 2 phase methods that first pretrain an inter-modal representation from aligned image-text pairs and then use GAN to train image generator on that basis. However, such representation ignores the inner-modal semantic correspondence, e.g. the images with same label. The semantic label in priory describes the inherent distribution pattern with underlying cross-image relationships, which is supplement to the text description for understanding the full characteristics of image. In this paper, we propose a framework leveraging both inter- and inner-modal correspondence by label guided supervised contrastive learning. We extend the T2I GANs to two parameter-sharing contrast branches in both pretraining and generation phases. This integration effectively clusters the semantically similar image-text pair representations, thereby fostering the generation of higher-quality images. We demonstrate our framework on four novel T2I GANs by both single-object dataset CUB and multi-object dataset COCO, achieving significant improvements in the Inception Score (IS) and Frechet Inception Distance (FID) metrics of imagegeneration evaluation. Notably, on more complex multi-object COCO, our framework improves FID by 30.1%, 27.3%, 16.2% and 17.1% for AttnGAN, DM-GAN, SSA-GAN and GALIP, respectively. We also validate our superiority by comparing with other label guided T2I GANs. The results affirm the effectiveness and competitiveness of our approach in advancing the state-of-the-art GAN for T2I generation

Related papers

• DAFT-GAN: Dual Affine Transformation Generative Adversarial Network for Text-Guided Image Inpainting [2.656795553429629]
  We propose a dual affine transformation generative adversarial network (DAFT-GAN) to maintain the semantic consistency for text-guided inpainting. Our proposed model outperforms the existing GAN-based models in both qualitative and quantitative assessments.
  arXiv  Detail & Related papers  (2024-08-09T09:28:42Z)
• CoBIT: A Contrastive Bi-directional Image-Text Generation Model [72.1700346308106]
  CoBIT employs a novel unicoder-decoder structure, which attempts to unify three pre-training objectives in one framework. CoBIT achieves superior performance in image understanding, image-text understanding (Retrieval, Captioning, VQA, SNLI-VE) and text-based content creation, particularly in zero-shot scenarios.
  arXiv  Detail & Related papers  (2023-03-23T17:24:31Z)
• Towards Better Text-Image Consistency in Text-to-Image Generation [15.735515302139335]
  We develop a novel CLIP-based metric termed as Semantic Similarity Distance (SSD) We further design the Parallel Deep Fusion Generative Adversarial Networks (PDF-GAN), which can fuse semantic information at different granularities. Our PDF-GAN can lead to significantly better text-image consistency while maintaining decent image quality on the CUB and COCO datasets.
  arXiv  Detail & Related papers  (2022-10-27T07:47:47Z)
• COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval [59.15034487974549]
  We propose a novel COllaborative Two-Stream vision-language pretraining model termed COTS for image-text retrieval. Our COTS achieves the highest performance among all two-stream methods and comparable performance with 10,800X faster in inference. Importantly, our COTS is also applicable to text-to-video retrieval, yielding new state-ofthe-art on the widely-used MSR-VTT dataset.
  arXiv  Detail & Related papers  (2022-04-15T12:34:47Z)
• Cycle-Consistent Inverse GAN for Text-to-Image Synthesis [101.97397967958722]
  We propose a novel unified framework of Cycle-consistent Inverse GAN for both text-to-image generation and text-guided image manipulation tasks. We learn a GAN inversion model to convert the images back to the GAN latent space and obtain the inverted latent codes for each image. In the text-guided optimization module, we generate images with the desired semantic attributes by optimizing the inverted latent codes.
  arXiv  Detail & Related papers  (2021-08-03T08:38:16Z)
• Improving Text-to-Image Synthesis Using Contrastive Learning [4.850820365312369]
  We propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images. We evaluate our approach over two popular text-to-image synthesis models, AttnGAN and DM-GAN, on datasets CUB and COCO.
  arXiv  Detail & Related papers  (2021-07-06T06:43:31Z)
• Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization [112.68171734288237]
  We propose a novel framework for discriminative pixel-level tasks using a generative model of both images and labels. We learn a generative adversarial network that captures the joint image-label distribution and is trained efficiently using a large set of unlabeled images. We demonstrate strong in-domain performance compared to several baselines, and are the first to showcase extreme out-of-domain generalization.
  arXiv  Detail & Related papers  (2021-04-12T21:41:25Z)
• Text to Image Generation with Semantic-Spatial Aware GAN [41.73685713621705]
  A text to image generation (T2I) model aims to generate photo-realistic images which are semantically consistent with the text descriptions. We propose a novel framework Semantic-Spatial Aware GAN, which is trained in an end-to-end fashion so that the text encoder can exploit better text information.
  arXiv  Detail & Related papers  (2021-04-01T15:48:01Z)
• DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis [80.54273334640285]
  We propose a novel one-stage text-to-image backbone that directly synthesizes high-resolution images without entanglements between different generators. We also propose a novel Target-Aware Discriminator composed of Matching-Aware Gradient Penalty and One-Way Output. Compared with current state-of-the-art methods, our proposed DF-GAN is simpler but more efficient to synthesize realistic and text-matching images.
  arXiv  Detail & Related papers  (2020-08-13T12:51:17Z)
• TIME: Text and Image Mutual-Translation Adversarial Networks [55.1298552773457]
  We propose Text and Image Mutual-Translation Adversarial Networks (TIME) TIME learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework. In experiments, TIME achieves state-of-the-art (SOTA) performance on the CUB and MS-COCO dataset.
  arXiv  Detail & Related papers  (2020-05-27T06:40:12Z)

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.