SCOMatch: Alleviating Overtrusting in Open-set Semi-supervised Learning

Open-set semi-supervised learning (OSSL) leverages practical open-set unlabeled data, comprising both in-distribution (ID) samples from seen classes and out-of-distribution (OOD) samples from unseen classes, for semi-supervised learning (SSL). Prior OSSL methods initially learned the decision boundary between ID and OOD with labeled ID data, subsequently employing self-training to refine this boundary. These methods, however, suffer from the tendency to overtrust the labeled ID data: the scarcity of labeled data caused the distribution bias between the labeled samples and the entire ID data, which misleads the decision boundary to overfit. The subsequent self-training process, based on the overfitted result, fails to rectify this problem. In this paper, we address the overtrusting issue by treating OOD samples as an additional class, forming a new SSL process. Specifically, we propose SCOMatch, a novel OSSL method that 1) selects reliable OOD samples as new labeled data with an OOD memory queue and a corresponding update strategy and 2) integrates the new SSL process into the original task through our Simultaneous Close-set and Open-set self-training. SCOMatch refines the decision boundary of ID and OOD classes across the entire dataset, thereby leading to improved results. Extensive experimental results show that SCOMatch significantly outperforms the state-of-the-art methods on various benchmarks. The effectiveness is further verified through ablation studies and visualization.

Training-Free Sketch-Guided Diffusion with Latent Optimization

Based on recent advanced diffusion models, Text-to-image (T2I) generation models have demonstrated their capabilities in generating diverse and high-quality images. However, leveraging their potential for real-world content creation, particularly in providing users with precise control over the image generation result, poses a significant challenge. In this paper, we propose an innovative training-free pipeline that extends existing text-to-image generation models to incorporate a sketch as an additional condition. To generate new images with a layout and structure closely resembling the input sketch, we find that these core features of a sketch can be tracked with the cross-attention maps of diffusion models. We introduce latent optimization, a method that refines the noisy latent at each intermediate step of the generation process using cross-attention maps to ensure that the generated images closely adhere to the desired structure outlined in the reference sketch. Through latent optimization, our method enhances the fidelity and accuracy of image generation, offering users greater control and customization options in content creation.

From Obstacle to Opportunity: Enhancing Semi-supervised Learning with Synthetic Data

Semi-supervised learning (SSL) can utilize unlabeled data to enhance model performance. In recent years, with increasingly powerful generative models becoming available, a large number of synthetic images have been uploaded to public image sets. Therefore, when collecting unlabeled data from these sources, the inclusion of synthetic images is inevitable. This prompts us to consider the impact of unlabeled data mixed with real and synthetic images on SSL. In this paper, we set up a new task, Real and Synthetic hybrid SSL (RS-SSL), to investigate this problem. We discover that current SSL methods are unable to fully utilize synthetic data and are sometimes negatively affected. Then, by analyzing the issues caused by synthetic images, we propose a new SSL method, RSMatch, to tackle the RS-SSL problem. Extensive experimental results show that RSMatch can better utilize the synthetic data in unlabeled images to improve the SSL performance. The effectiveness is further verified through ablation studies and visualization.

Semantic-Driven Initial Image Construction for Guided Image Synthesis in Diffusion Model

The initial noise image has demonstrated a significant influence on image generation, and manipulating the initial noise image can effectively increase control over the generation. All of the current generation is based only on a single initial noise drawn from a normal distribution, which may not be suited to the desired content specified by the prompt. In this research, we propose a novel approach using pre-collected, semantically-informed pixel blocks from multiple initial noises for the initial image construction to enhance control over the image generation. The inherent tendencies of these pixel blocks can easily generate specific content, thus effectively guiding the generation process towards the desired content. The pursuit of tailored initial image construction inevitably leads to deviations from the normal distribution, and our experimental results show that the diffusion model exhibits a certain degree of tolerance towards the distribution of initial images. Our approach achieves state-of-the-art performance in the training-free layout-to-image synthesis task, demonstrating the adaptability of the initial image construction in guiding the content of the generated image. Our code will be made publicly available.

Guided Image Synthesis via Initial Image Editing in Diffusion Model

Diffusion models have the ability to generate high quality images by denoising pure Gaussian noise images. While previous research has primarily focused on improving the control of image generation through adjusting the denoising process, we propose a novel direction of manipulating the initial noise to control the generated image. Through experiments on stable diffusion, we show that blocks of pixels in the initial latent images have a preference for generating specific content, and that modifying these blocks can significantly influence the generated image. In particular, we show that modifying a part of the initial image affects the corresponding region of the generated image while leaving other regions unaffected, which is useful for repainting tasks. Furthermore, we find that the generation preferences of pixel blocks are primarily determined by their values, rather than their position. By moving pixel blocks with a tendency to generate user-desired content to user-specified regions, our approach achieves state-of-the-art performance in layout-to-image generation. Our results highlight the flexibility and power of initial image manipulation in controlling the generated image.

Training-Free Location-Aware Text-to-Image Synthesis

Current large-scale generative models have impressive efficiency in generating high-quality images based on text prompts. However, they lack the ability to precisely control the size and position of objects in the generated image. In this study, we analyze the generative mechanism of the stable diffusion model and propose a new interactive generation paradigm that allows users to specify the position of generated objects without additional training. Moreover, we propose an object detection-based evaluation metric to assess the control capability of location aware generation task. Our experimental results show that our method outperforms state-of-the-art methods on both control capacity and image quality.

Noisy Annotation Refinement for Object Detection

Supervised training of object detectors requires well-annotated large-scale datasets, whose production is costly. Therefore, some efforts have been made to obtain annotations in economical ways, such as cloud sourcing. However, datasets obtained by these methods tend to contain noisy annotations such as inaccurate bounding boxes and incorrect class labels. In this study, we propose a new problem setting of training object detectors on datasets with entangled noises of annotations of class labels and bounding boxes. Our proposed method efficiently decouples the entangled noises, corrects the noisy annotations, and subsequently trains the detector using the corrected annotations. We verified the effectiveness of our proposed method and compared it with the baseline on noisy datasets with different noise levels. The experimental results show that our proposed method significantly outperforms the baseline.

FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding

Few-learn learning (FSL) is one of the key future steps in machine learning and has raised a lot of attention. However, in contrast to the rapid development in other domains, such as Computer Vision, the progress of FSL in Nature Language Processing (NLP) is much slower. One of the key reasons for this is the lacking of public benchmarks. NLP FSL researches always report new results on their own constructed few-shot datasets, which is pretty inefficient in results comparison and thus impedes cumulative progress. In this paper, we present FewJoint, a novel Few-Shot Learning benchmark for NLP. Different from most NLP FSL research that only focus on simple N-classification problems, our benchmark introduces few-shot joint dialogue language understanding, which additionally covers the structure prediction and multi-task reliance problems. This allows our benchmark to reflect the real-word NLP complexity beyond simple N-classification. Our benchmark is used in the few-shot learning contest of SMP2020-ECDT task-1. We also provide a compatible FSL platform to ease experiment set-up.