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Haoxing Chen

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4 papers

2 author rows

ICML Conference 2025 Conference Paper

Stochastic Layer-Wise Shuffle for Improving Vision Mamba Training

Zizheng Huang
Haoxing Chen
Jiaqi Li
Jun Lan 0001
Huijia Zhu
Weiqiang Wang 0002
Limin Wang 0002

Recent Vision Mamba (Vim) models exhibit nearly linear complexity in sequence length, making them highly attractive for processing visual data. However, the training methodologies and their potential are still not sufficiently explored. In this paper, we investigate strategies for Vim and propose Stochastic Layer-Wise Shuffle (SLWS), a novel regularization method that can effectively improve the Vim training. Without architectural modifications, this approach enables the non-hierarchical Vim to get leading performance on ImageNet-1K compared with the similar type counterparts. Our method operates through four simple steps per layer: probability allocation to assign layer-dependent shuffle rates, operation sampling via Bernoulli trials, sequence shuffling of input tokens, and order restoration of outputs. SLWS distinguishes itself through three principles: (1) Plug-and-play: No architectural modifications are needed, and it is deactivated during inference. (2) Simple but effective: The four-step process introduces only random permutations and negligible overhead. (3) Intuitive design: Shuffling probabilities grow linearly with layer depth, aligning with the hierarchical semantic abstraction in vision models. Our work underscores the importance of tailored training strategies for Vim models and provides a helpful way to explore their scalability. Code and models are available at https: //github. com/huangzizheng01/ShuffleMamba

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AAAI Conference 2025 Conference Paper

WildFake: A Large-Scale and Hierarchical Dataset for AI-Generated Images Detection

Yan Hong
Jianming Feng
Haoxing Chen
Jun Lan
Huijia Zhu
Weiqiang Wang
Jianfu Zhang

The development of text-to-image generative models has enabled the creation of images so realistic that distinguishing between AI-generated images and real photos is becoming a challenge. This progress offers new possibilities but also raises concerns over privacy, authenticity, and security. Detecting AI-generated images is crucial to prevent misuse. To assess the generalizability and robustness of AI-generated image detection, we present a large-scale dataset, referred to as WildFake. This dataset features cutting-edge image generators, a wide variety of generator categories, and generators for various applications, organized in a hierarchical framework. WildFake collects fake images from the open-source community, enriching its diversity with a broad range of image classes and image styles. Its design significantly improves the effectiveness of detection algorithms, making it a valuable resource for enhancing AI-generated image detection in practical applications. Our evaluations offer insights into the performance of generative models at various levels, showcasing WildFake's unique hierarchical structure's benefits.

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NeurIPS Conference 2023 Conference Paper

DiffUTE: Universal Text Editing Diffusion Model

Haoxing Chen
Zhuoer Xu
Zhangxuan Gu
Jun Lan
行郑
Yaohui Li
Changhua Meng
Huijia Zhu

Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text and text style during generation. To tackle this problem, we propose a universal self-supervised text editing diffusion model (DiffUTE), which aims to replace or modify words in the source image with another one while maintaining its realistic appearance. Specifically, we build our model on a diffusion model and carefully modify the network structure to enable the model for drawing multilingual characters with the help of glyph and position information. Moreover, we design a self-supervised learning framework to leverage large amounts of web data to improve the representation ability of the model. Experimental results show that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity. Our code will be avaliable in \url{https: //github. com/chenhaoxing/DiffUTE}.

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ICML Conference 2023 Conference Paper

Model-Aware Contrastive Learning: Towards Escaping the Dilemmas

Zizheng Huang
Haoxing Chen
Ziqi Wen
Chao Zhang 0078
Huaxiong Li
Bo Wang 0027
Chunlin Chen 0001

Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains. However, the most common InfoNCE-based methods suffer from some dilemmas, such as uniformity-tolerance dilemma (UTD) and gradient reduction, both of which are related to a $\mathcal{P}_{ij}$ term. It has been identified that UTD can lead to unexpected performance degradation. We argue that the fixity of temperature is to blame for UTD. To tackle this challenge, we enrich the CL loss family by presenting a Model-Aware Contrastive Learning (MACL) strategy, whose temperature is adaptive to the magnitude of alignment that reflects the basic confidence of the instance discrimination task, then enables CL loss to adjust the penalty strength for hard negatives adaptively. Regarding another dilemma, the gradient reduction issue, we derive the limits of an involved gradient scaling factor, which allows us to explain from a unified perspective why some recent approaches are effective with fewer negative samples, and summarily present a gradient reweighting to escape this dilemma. Extensive remarkable empirical results in vision, sentence, and graph modality validate our approach’s general improvement for representation learning and downstream tasks.

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