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Xinhan Di

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3 papers

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AAAI Conference 2025 Conference Paper

HieraFashDiff: Hierarchical Fashion Design with Multi-stage Diffusion Models

Zhifeng Xie
Hao Li
Huiming Ding
Mengtian Li
Xinhan Di
Ying Cao

Fashion design is a challenging and complex process. Recent works on fashion generation and editing are all agnostic of the actual fashion design process, which limits their usage in practice. In this paper, we propose a novel hierarchical diffusion-based framework tailored for fashion design, coined as HieraFashDiff. Our model is designed to mimic the practical fashion design workflow, by unraveling the denosing process into two successive stages: 1) an ideation stage that generates design proposals given high-level concepts and 2) an iteration stage that continuously refines the proposals using low-level attributes. Our model supports fashion design generation and fine-grained local editing in a single framework. To train our model, we contribute a new dataset of full-body fashion images annotated with hierarchical text descriptions. Extensive evaluations show that, as compared to prior approaches, our method can generate fashion designs and edited results with higher fidelity and better prompt adherence, showing its promising potential to augment the practical fashion design workflow.

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PRL Workshop 2025 Workshop Paper

Intrinsic Self-Correction Enhancement in Monte Carlo Tree Search Boosted Reasoning via Iterative Preference Learning

Huchen Jiang
Yangyang Ma
CHAOFAN DING
Kexin Luan
Xinhan Di

With current state-of-the-art approaches aimed at enhancing the reasoning capabilities of Large Language Models(LLMs) through iterative preference learning inspired by AlphaZero, we propose to further enhance the step-wise reasoning capabilities through intrinsic self-correction to some extent. Our work leverages step-wise preference learning to enhance self-verification via reinforcement learning. We initially conduct our work through a two-stage training procedure. At the first stage, the self-correction reasoning ability of an LLM is enhanced through its own predictions, relying entirely on self-generated data within the intrinsic selfcorrection to some extent. At the second stage, the baseline step-wise preference learning is leveraged via the application of the enhanced self-correct policy achieved at the first stage. In the evaluation of arithmetic reasoning tasks, our approach outperforms OpenMath2-Llama3. 1-8B, dart-mathmistral-7b-uniform on MATH with increases in accuracy to 71. 34%(+4. 18%) and 48. 06%(+4. 94%) and LLama-3. 1-8BInstruct, Mistral-7B-Instruct-v0. 1 on GSM8K with increases in accuracy to 86. 76%(+2. 00%) and 38. 06%(+2. 28%).

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NeurIPS Conference 2018 Conference Paper

PointCNN: Convolution On X-Transformed Points

Yangyan Li
Rui Bu
Mingchao Sun
Wei Wu
Xinhan Di
Baoquan Chen

We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e. g. images). However, point cloud are irregular and unordered, thus a direct convolving of kernels against the features associated with the points will result in deserting the shape information while being variant to the orders. To address these problems, we propose to learn a X-transformation from the input points, which is used for simultaneously weighting the input features associated with the points and permuting them into latent potentially canonical order. Then element-wise product and sum operations of typical convolution operator are applied on the X-transformed features. The proposed method is a generalization of typical CNNs into learning features from point cloud, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.

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