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Qiong Cao

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3 papers

2 author rows

AAAI Conference 2025 Conference Paper

Beyond Human Data: Aligning Multimodal Large Language Models by Iterative Self-Evolution

Wentao Tan
Qiong Cao
Yibing Zhan
Chao Xue
Changxing Ding

Human preference alignment can significantly enhance the capabilities of Multimodal Large Language Models (MLLMs). However, collecting high-quality preference data remains costly. One promising solution is the self-evolution strategy, where models are iteratively trained on data they generate. Current multimodal self-evolution techniques, nevertheless, still need human- or GPT-annotated data. Some methods even require extra models or ground truth answers to construct preference data. To overcome these limitations, we propose a novel multimodal self-evolution framework that empowers the model to autonomously generate high-quality questions and answers using only unannotated images. First, in the question generation phase, we implement an image-driven self-questioning mechanism. This approach allows the model to create questions and evaluate their relevance and answerability based on the image content. If a question is deemed irrelevant or unanswerable, the model regenerates it to ensure alignment with the image. This process establishes a solid foundation for subsequent answer generation and optimization. Second, while generating answers, we design an answer self-enhancement technique to boost the discriminative power of answers. We begin by captioning the images and then use the descriptions to enhance the generated answers. Additionally, we utilize corrupted images to generate rejected answers, thereby forming distinct preference pairs for effective optimization. Finally, in the optimization step, we incorporate an image content alignment loss function alongside the Direct Preference Optimization (DPO) loss to mitigate hallucinations. This function maximizes the likelihood of the above generated descriptions in order to constrain the model's attention to the image content. As a result, model can generate more accurate and reliable outputs. Experiments demonstrate that our framework is competitively compared with previous methods that utilize external information, paving the way for more efficient and scalable MLLMs.

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IJCAI Conference 2024 Conference Paper

MuEP: A Multimodal Benchmark for Embodied Planning with Foundation Models

Kanxue Li
Baosheng Yu
Qi Zheng
Yibing Zhan
Yuhui Zhang
Tianle Zhang
Yijun Yang
Yue Chen

Foundation models have demonstrated significant emergent abilities, holding great promise for enhancing embodied agents' reasoning and planning capacities. However, the absence of a comprehensive benchmark for evaluating embodied agents with multimodal observations in complex environments remains a notable gap. In this paper, we present MuEP, a comprehensive Multimodal benchmark for Embodied Planning. MuEP facilitates the evaluation of multimodal and multi-turn interactions of embodied agents in complex scenes, incorporating fine-grained evaluation metrics that provide insights into the performance of embodied agents throughout each task. Furthermore, we evaluate embodied agents with recent state-of-the-art foundation models, including large language models (LLMs) and large multimodal models (LMMs), on the proposed benchmark. Experimental results show that foundation models based on textual representations of environments usually outperform their visual counterparts, suggesting a gap in embodied planning abilities with multimodal observations. We also find that control language generation is an indispensable ability beyond common-sense knowledge for accurate embodied task completion. We hope the proposed MuEP benchmark can contribute to the advancement of embodied AI with foundation models.

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ICLR Conference 2023 Conference Paper

Sharper Bounds for Uniformly Stable Algorithms with Stationary Mixing Process

Shi Fu
Yunwen Lei
Qiong Cao
Xinmei Tian 0001
Dacheng Tao

Generalization analysis of learning algorithms often builds on a critical assumption that training examples are independently and identically distributed, which is often violated in practical problems such as time series prediction. In this paper, we use algorithmic stability to study the generalization performance of learning algorithms with $\psi$-mixing data, where the dependency between observations weakens over time. We show uniformly stable algorithms guarantee high-probability generalization bounds of the order $O(1/\sqrt{n})$ (within a logarithmic factor), where $n$ is the sample size. We apply our general result to specific algorithms including regularization schemes, stochastic gradient descent and localized iterative regularization, and develop excess population risk bounds for learning with $\psi$-mixing data. Our analysis builds on a novel moment bound for weakly-dependent random variables on a $\varphi$-mixing sequence and a novel error decomposition of generalization error.

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