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Junwen Chen

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2 papers

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NeurIPS Conference 2025 Conference Paper

MMMG: A Massive, Multidisciplinary, Multi-Tier Generation Benchmark for Text-to-Image Reasoning

Yuxuan Luo
Ryan Yuan
Junwen Chen
Haonan Cai
Ziyi Yue
Yuwei Yang
Fatima Zohra Daha
Ji Li

In this paper, we introduce knowledge image generation as a new task, alongside the Massive Multi-Discipline Multi-Tier Knowledge-Image Generation Benchmark (MMMG) to probe the reasoning capability of image generation models. Knowledge images have been central to human civilization and to the mechanisms of human learning—a fact underscored by dual-coding theory and the picture-superiority effect. Generating such images is challenging, demanding multimodal reasoning that fuses world knowledge with pixel-level grounding into clear explanatory visuals. To enable comprehensive evaluation, MMMG offers $4, 456$ expert-validated (knowledge) image-prompt pairs spanning $10$ disciplines, $6$ educational levels, and diverse knowledge formats such as charts, diagrams, and mind maps. To eliminate confounding complexity during evaluation, we adopt a unified Knowledge Graph (KG) representation. Each KG explicitly delineates a target image’s core entities and their dependencies. We further introduce MMMG-Score to evaluate generated knowledge images. This metric combines factual fidelity, measured by graph-edit distance between KGs, with visual clarity assessment. Comprehensive evaluations of $21$ state-of-the-art text-to-image generation models expose serious reasoning deficits—low entity fidelity, weak relations, and clutter—with GPT-4o achieving an MMMG-Score of only $50. 20$, underscoring the benchmark’s difficulty. To spur further progress, we release FLUX-Reason (MMMG-Score of $34. 45$), an effective and open baseline that combines a reasoning LLM with diffusion models and is trained on $16, 000$ curated knowledge image–prompt pairs.

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AAAI Conference 2018 Conference Paper

Adversarial Zero-shot Learning With Semantic Augmentation

Bin Tong
Martin Klinkigt
Junwen Chen
Xiankun Cui
Quan Kong
Tomokazu Murakami
Yoshiyuki Kobayashi

In situations in which labels are expensive or difﬁcult to obtain, deep neural networks for object recognition often suffer to achieve fair performance. Zero-shot learning is dedicated to this problem. It aims to recognize objects of unseen classes by transferring knowledge from seen classes via a shared intermediate representation. Using the manifold structure of seen training samples is widely regarded as important to learn a robust mapping between samples and the intermediate representation, which is crucial for transferring the knowledge. However, their irregular structures, such as the lack in variation of samples for certain classes and highly overlapping clusters of different classes, may result in an inappropriate mapping. Additionally, in a high dimensional mapping space, the hubness problem may arise, in which one of the unseen classes has a high possibility to be assigned to samples of different classes. To mitigate such problems, we use a generative adversarial network to synthesize samples with speciﬁed semantics to cover a higher diversity of given classes and interpolated semantics of pairs of classes. We propose a simple yet effective method for applying the augmented semantics to the hinge loss functions to learn a robust mapping. The proposed method was extensively evaluated on small- and largescale datasets, showing a signiﬁcant improvement over stateof-the-art methods.

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