Synthetic Data Supervised Salient Object Detection

• URL: http://arxiv.org/abs/2210.13835v1
• Date: Tue, 25 Oct 2022 08:36:29 GMT
• Title: Synthetic Data Supervised Salient Object Detection
• Authors: Zhenyu Wu, Lin Wang, Wei Wang, Tengfei Shi, Chenglizhao Chen, Aimin Hao, Shuo Li
• Abstract summary: We propose a novel yet effective method for SOD, coined SODGAN, which can generate infinite high-quality image-mask pairs. For the first time, our SODGAN tackles SOD with synthetic data directly generated from the generative model. Our approach achieves a new SOTA performance in semi/weakly-supervised methods, and even outperforms several fully-supervised SOTA methods.
• Score: 40.991558165686136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although deep salient object detection (SOD) has achieved remarkable progress, deep SOD models are extremely data-hungry, requiring large-scale pixel-wise annotations to deliver such promising results. In this paper, we propose a novel yet effective method for SOD, coined SODGAN, which can generate infinite high-quality image-mask pairs requiring only a few labeled data, and these synthesized pairs can replace the human-labeled DUTS-TR to train any off-the-shelf SOD model. Its contribution is three-fold. 1) Our proposed diffusion embedding network can address the manifold mismatch and is tractable for the latent code generation, better matching with the ImageNet latent space. 2) For the first time, our proposed few-shot saliency mask generator can synthesize infinite accurate image synchronized saliency masks with a few labeled data. 3) Our proposed quality-aware discriminator can select highquality synthesized image-mask pairs from noisy synthetic data pool, improving the quality of synthetic data. For the first time, our SODGAN tackles SOD with synthetic data directly generated from the generative model, which opens up a new research paradigm for SOD. Extensive experimental results show that the saliency model trained on synthetic data can achieve $98.4\%$ F-measure of the saliency model trained on the DUTS-TR. Moreover, our approach achieves a new SOTA performance in semi/weakly-supervised methods, and even outperforms several fully-supervised SOTA methods. Code is available at https://github.com/wuzhenyubuaa/SODGAN

Related papers

• AeroGen: Enhancing Remote Sensing Object Detection with Diffusion-Driven Data Generation [38.89367726721828]
  Remote sensing image object detection (RSIOD) aims to identify and locate specific objects within satellite or aerial imagery. There is a scarcity of labeled data in current RSIOD datasets, which significantly limits the performance of current detection algorithms. This paper proposes a layout-controllable diffusion generative model (i.e. AeroGen) tailored for RSIOD.
  arXiv  Detail & Related papers  (2024-11-23T09:04:33Z)
• SAU: A Dual-Branch Network to Enhance Long-Tailed Recognition via Generative Models [9.340077455871736]
  Long-tailed distributions in image recognition pose a considerable challenge due to the severe imbalance between a few dominant classes. Recently, the use of large generative models to create synthetic data for image classification has been realized. We propose the use of synthetic data as a complement to long-tailed datasets to eliminate the impact of data imbalance.
  arXiv  Detail & Related papers  (2024-08-29T05:33:59Z)
• Learning to Discretize Denoising Diffusion ODEs [41.50816120270017]
  Diffusion Probabilistic Models (DPMs) are generative models showing competitive performance in various domains. We propose LD3, a lightweight framework designed to learn the optimal time discretization for sampling. We demonstrate analytically and empirically that LD3 improves sampling efficiency with much less computational overhead.
  arXiv  Detail & Related papers  (2024-05-24T12:51:23Z)
• StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
  We present StableDreamer, a methodology incorporating three advances. First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
  arXiv  Detail & Related papers  (2023-12-02T02:27:58Z)
• Randomize to Generalize: Domain Randomization for Runway FOD Detection [1.4249472316161877]
  Tiny Object Detection is challenging due to small size, low resolution, occlusion, background clutter, lighting conditions and small object-to-image ratio. We propose a novel two-stage methodology Synthetic Image Augmentation (SRIA) to enhance generalization capabilities of models encountering 2D datasets. We report that detection accuracy improved from an initial 41% to 92% for OOD test set.
  arXiv  Detail & Related papers  (2023-09-23T05:02:31Z)
• Lafite2: Few-shot Text-to-Image Generation [132.14211027057766]
  We propose a novel method for pre-training text-to-image generation model on image-only datasets. It considers a retrieval-then-optimization procedure to synthesize pseudo text features. It can be beneficial to a wide range of settings, including the few-shot, semi-supervised and fully-supervised learning.
  arXiv  Detail & Related papers  (2022-10-25T16:22:23Z)
• PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks [61.51515750218049]
  This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks. We propose Prompt-based Data Augmentation model (PromDA) which only trains small-scale Soft Prompt. PromDA generates synthetic data via two different views and filters out the low-quality data using NLU models.
  arXiv  Detail & Related papers  (2022-02-25T05:09:27Z)
• A Deep Learning Generative Model Approach for Image Synthesis of Plant Leaves [62.997667081978825]
  We generate via advanced Deep Learning (DL) techniques artificial leaf images in an automatized way. We aim to dispose of a source of training samples for AI applications for modern crop management.
  arXiv  Detail & Related papers  (2021-11-05T10:53:35Z)
• UltraPose: Synthesizing Dense Pose with 1 Billion Points by Human-body Decoupling 3D Model [58.70130563417079]
  We introduce a new 3D human-body model with a series of decoupled parameters that could freely control the generation of the body. Compared to the existing manually annotated DensePose-COCO dataset, the synthetic UltraPose has ultra dense image-to-surface correspondences without annotation cost and error.
  arXiv  Detail & Related papers  (2021-10-28T16:24:55Z)
• Synthetic Data and Hierarchical Object Detection in Overhead Imagery [0.0]
  We develop novel synthetic data generation and augmentation techniques for enhancing low/zero-sample learning in satellite imagery. To test the effectiveness of synthetic imagery, we employ it in the training of detection models and our two stage model, and evaluate the resulting models on real satellite images.
  arXiv  Detail & Related papers  (2021-01-29T22:52:47Z)

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.