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Yongxin Wang

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5 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Evolving and Regularizing Meta-Environment Learner for Fine-Grained Few-Shot Class-Incremental Learning

Li-Jun Zhao
Zhen-Duo Chen
Yongxin Wang
Xin Luo
Xin-Shun Xu

Recently proposed Fine-Grained Few-Shot Class-Incremental Learning (FG-FSCIL) offers a practical and efficient solution for enabling models to incrementally learn new fine-grained categories under limited data conditions. However, existing methods still settle for the fine-grained feature extraction capabilities learned from the base classes. Unlike conventional datasets, fine-grained categories exhibit subtle inter-class variations, naturally fostering latent synergy among sub-categories. Meanwhile, the incremental learning framework offers an opportunity to progressively strengthen this synergy by incorporating new sub-category data over time. Motivated by this, we theoretically formulate the FSCIL problem and derive a generalization error bound within a shared fine-grained meta-category environment. Guided by our theoretical insights, we design a novel Meta-Environment Learner (MEL) for FG-FSCIL, which evolves fine-grained feature extraction to enhance meta-environment understanding and simultaneously regularizes hypothesis space complexity. Extensive experiments demonstrate that our method consistently and significantly outperforms existing approaches.

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NeurIPS Conference 2025 Conference Paper

PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly

Liang Ma
Jiajun Wen
Min Lin
Rongtao Xu
Xiwen Liang
Bingqian Lin
Jun Ma
Yongxin Wang

While vision-language models (VLMs) have demonstrated promising capabilities in reasoning and planning for embodied agents, their ability to comprehend physical phenomena, particularly within structured 3D environments, remains severely limited. To close this gap, we introduce PhyBlock, a progressive benchmark designed to assess VLMs on physical understanding and planning through robotic 3D block assembly tasks. PhyBlock integrates a novel four-level cognitive hierarchy assembly task alongside targeted Visual Question Answering (VQA) samples, collectively aimed at evaluating progressive spatial reasoning and fundamental physical comprehension, including object properties, spatial relationships, and holistic scene understanding. PhyBlock includes 2600 block tasks (400 assembly tasks, 2200 VQA tasks) and evaluates models across three key dimensions: partial completion, failure diagnosis, and planning robustness. We benchmark 23 state-of-the-art VLMs, highlighting their strengths and limitations in physically grounded, multi-step planning. Our empirical findings indicate that the performance of VLMs exhibits pronounced limitations in high-level planning and reasoning capabilities, leading to a notable decline in performance for the growing complexity of the tasks. Error analysis reveals persistent difficulties in spatial orientation and dependency reasoning. We position PhyBlock as a unified testbed to advance embodied reasoning, bridging vision-language understanding and real-world physical problem-solving.

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NeurIPS Conference 2024 Conference Paper

Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs

Sukmin Yun
haokun lin
Rusiru Thushara
Mohammad Q. Bhat
Yongxin Wang
Zutao Jiang
Mingkai Deng
Jinhong Wang

Multimodal large language models (MLLMs) have shown impressive success across modalities such as image, video, and audio in a variety of understanding and generation tasks. However, current MLLMs are surprisingly poor at understanding webpage screenshots and generating their corresponding HTML code. To address this problem, we propose Web2Code, a benchmark consisting of a new large-scale webpage-to-code dataset for instruction tuning and an evaluation framework for the webpage understanding and HTML code translation abilities of MLLMs. For dataset construction, we leverage pretrained LLMs to enhance existing webpage-to-code datasets as well as generate a diverse pool of new webpages rendered into images. Specifically, the inputs are webpage images and instructions, while the responses are the webpage's HTML code. We further include diverse natural language QA pairs about the webpage content in the responses to enable a more comprehensive understanding of the web content. To evaluate model performance in these tasks, we develop an evaluation framework for testing MLLMs' abilities in webpage understanding and web-to-code generation. Extensive experiments show that our proposed dataset is beneficial not only to our proposed tasks but also in the general visual domain. We hope our work will contribute to the development of general MLLMs suitable for web-based content generation and task automation. Our data and code are available at https: //github. com/MBZUAI-LLM/web2code.

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TIST Journal 2023 Journal Article

Diagnose Like Doctors: Weakly Supervised Fine-Grained Classification of Breast Cancer

Jieru Tian
Yongxin Wang
Zhenduo Chen
Xin Luo
Xinshun Xu

Breast cancer is the most common type of cancers in women. Therefore, how to accurately and timely diagnose it becomes very important. Some computer-aided diagnosis models based on pathological images have been proposed for this task. However, there are still some issues that need to be further addressed. For example, most deep learning based models suffer from a lack of interpretability. In addition, some of them cannot fully exploit the information in medical data, e.g., hierarchical label structure and scattered distribution of target objects. To address these issues, we propose a weakly supervised fine-grained medical image classification method for breast cancer diagnosis, i.e., DLD-Net for short. It simulates the diagnostic procedures of pathologists by multiple attention-guided cropping and dropping operations, making it have good clinical interpretability. Moreover, it cannot only exploit the global information of a whole image, but also further mine the critical local information by generating and selecting critical regions from the image. In light of this, those subtle discriminating information hidden in scattered regions can be exploited. In addition, we also design a novel hierarchical cross-entropy loss to utilize the hierarchical label information in medical images, making the classification results more discriminative. Furthermore, DLD-Net is a weakly supervised network, which can be trained end-to-end without any additional region annotations. Extensive experimental results on three benchmark datasets demonstrate that DLD-Net is able to achieve good results and outperforms some state-of-the-art methods.

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IJCAI Conference 2018 Conference Paper

SDMCH: Supervised Discrete Manifold-Embedded Cross-Modal Hashing

Xin Luo
Xiao-Ya Yin
Liqiang Nie
Xuemeng Song
Yongxin Wang
Xin-Shun Xu

Cross-modal hashing methods have attracted considerable attention. Most pioneer approaches only preserve the neighborhood relationship by constructing the correlations among heterogeneous modalities. However, they neglect the fact that the high-dimensional data often exists on a low-dimensional manifold embedded in the ambient space and the relative proximity between the neighbors is also important. Although some methods leverage the manifold learning to generate the hash codes, most of them fail to explicitly explore the discriminative information in the class labels and discard the binary constraints during optimization, generating large quantization errors. To address these issues, in this paper, we present a novel cross-modal hashing method, named Supervised Discrete Manifold-Embedded Cross-Modal Hashing (SDMCH). It can not only exploit the non-linear manifold structure of data and construct the correlation among heterogeneous multiple modalities, but also fully utilize the semantic information. Moreover, the hash codes can be generated discretely by an iterative optimization algorithm, which can avoid the large quantization errors. Extensive experimental results on three benchmark datasets demonstrate that SDMCH outperforms ten state-of-the-art cross-modal hashing methods.

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