Image editing in our daily lives often requires models to first understand user’s intention and then proceed with the editing. Despite significant advancements in image editing technology, understanding and executing complex instructions remains a substantial challenge. Existing image editing models either fail to comprehend complex intentions or make errors when dealing with multiple objects. To address these challenges, we present an innovative image editing framework that employs Chain-of-Thought (CoT) reasoning and localizing capabilities of multimodal Large Language Models (LLMs) to assist diffusion models in generating more refined images. We meticulously design a CoT process comprising instruction decomposition, region localization, and detailed description. We train our model to learn the CoT process and the mask of the edited image. By providing diffusion models with generated prompts and generated masks, our model edits images with a superior understanding of instructions. Extensive experiments demonstrate that our model outperforms existing state-of-the-art models in image generation both qualitatively and quantitatively. Notably, our model exhibits an enhanced ability of understanding complex prompts and generating corresponding images.

Patient data from real-world clinical practice often suffers from data scarcity and long-tail imbalances, leading to biased outcomes or algorithmic unfairness. This study addresses these challenges by generating lesion-containing image-segmentation pairs from lesion-free images. Previous efforts in medical imaging synthesis have struggled with separating lesion information from background, resulting in low-quality backgrounds and limited control over the synthetic output. Inspired by diffusion-based image inpainting, we propose LeFusion, a lesion-focused diffusion model. By redesigning the diffusion learning objectives to focus on lesion areas, we simplify the learning process and improve control over the output while preserving high-fidelity backgrounds by integrating forward-diffused background contexts into the reverse diffusion process. Additionally, we tackle two major challenges in lesion texture synthesis: 1) multi-peak and 2) multi-class lesions. We introduce two effective strategies: histogram-based texture control and multi-channel decomposition, enabling the controlled generation of high-quality lesions in difficult scenarios. Furthermore, we incorporate lesion mask diffusion, allowing control over lesion size, location, and boundary, thus increasing lesion diversity. Validated on 3D cardiac lesion MRI and lung nodule CT datasets, LeFusion-generated data significantly improves the performance of state-of-the-art segmentation models, including nnUNet and SwinUNETR.

Technical support question-answering (QA) systems assist users in diagnosing and resolving technical issues, but ensuring their reliability remains a challenge. Existing QA systems may generate inaccurate responses due to LLM hallucinations and retrieval errors, which can lead to misleading guidance. A reliable evaluation framework is essential for systematically improving technical support QA systems, ensuring they generate accurate guidance. However, existing evaluation methods for QA systems struggle to precisely match key terms, and verify step order and completeness.To address these challenges, we propose TechSupportEval, an automated evaluation framework for technical support QA. Our framework introduces two novel techniques: (1) ClozeFact, which formulates fact verification as a cloze test and uses an LLM to fill in missing key terms to ensure precise key term matching, and (2) StepRestore, which shuffles ground truth steps and uses an LLM to reconstruct the actionable instructions in the correct order, verifying step order and completeness.To support comprehensive evaluation, we propose a benchmark dataset built upon the publicly available TechQA dataset, containing responses generated by different levels of QA systems. TechSupportEval achieves an AUC of 0.91, outperforming the state-of-the-art method by 7.6%. The code and dataset are available at https://github.com/NetManAIOps/TechSupportEval.

Semantic segmentation is a computer vision task that associates a label with each pixel in an image. Modern approaches tend to introduce class embeddings into semantic segmentation for deeply utilizing category semantics, and regard supervised class masks as final predictions. In this paper, we explore the mechanism of class embeddings and have an insight that more explicit and meaningful class embeddings can be generated based on class masks purposely. Following this observation, we propose ECENet, a new segmentation paradigm, in which class embeddings are obtained and enhanced explicitly during interacting with multi-stage image features. Based on this, we revisit the traditional decoding process and explore inverted information flow between segmentation masks and class embeddings. Furthermore, to ensure the discriminability and informativity of features from backbone, we propose a Feature Reconstruction module, which combines intrinsic and diverse branches together to ensure the concurrence of diversity and redundancy in features. Experiments show that our ECENet outperforms its counterparts on the ADE20K dataset with much less computational cost and achieves new state-of-the-art results on PASCALContext dataset. The code will be released at https://gitee.com/mindspore/models and https://github.com/Carol-lyh/ECENet.

We present Skeleton-CutMix, a simple and effective skeleton augmentation framework for supervised domain adaptation and show its advantage in skeleton-based action recognition tasks. Existing approaches usually perform domain adaptation for action recognition with elaborate loss functions that aim to achieve domain alignment. However, they fail to capture the intrinsic characteristics of skeleton representation. Benefiting from the well-defined correspondence between bones of a pair of skeletons, we instead mitigate domain shift by fabricating skeleton data in a mixed domain, which mixes up bones from the source domain and the target domain. The fabricated skeletons in the mixed domain can be used to augment training data and train a more general and robust model for action recognition. Specifically, we hallucinate new skeletons by using pairs of skeletons from the source and target domains; a new skeleton is generated by exchanging some bones from the skeleton in the source domain with corresponding bones from the skeleton in the target domain, which resembles a cut-and-mix operation. When exchanging bones from different domains, we introduce a class-specific bone sampling strategy so that bones that are more important for an action class are exchanged with higher probability when generating augmentation samples for that class. We show experimentally that the simple bone exchange strategy for augmentation is efficient and effective and that distinctive motion features are preserved while mixing both action and style across domains. We validate our method in cross-dataset and cross-age settings on NTU-60 and ETRI-Activity3D datasets with an average gain of over 3% in terms of action recognition accuracy, and demonstrate its superior performance over previous domain adaptation approaches as well as other skeleton augmentation strategies.

Point set registration plays an important role in computer vision and pattern recognition. In this article, we propose an adaptive hierarchical probabilistic model (HPM) under a variational Bayesian (VB) framework for point set registration problem. The main contributions of this article are given as follows. First, a dynamic putative inlier estimation strategy is proposed through the hesitant fuzzy Einstein weighted averaging based membership calculation and component estimation using symmetric cross entropy. Second, a student-t mixture model based HPM is designed to solve outlier and occlusion problems during registration. Third, a VB-based transformation updating is proposed to construct a robust and adjustable transformation for effectively fitting target point set while further resisting outliers. The performances of the proposed method in point set and image registrations against 11 state-of-the-art methods are evaluated, in which our method gives the best performance in most scenarios.