Author name cluster

Yonghao Dang

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

Exploring Position Encoding Mechanism in Diffusion U-Net for Training-free High-resolution Image Generation

Feng Zhou
Pu Cao
Yiyang Ma
Lu Yang
Yonghao Dang
Jianqin Yin

Denoising higher-resolution latents using a pre-trained U-Net often results in repetitive and disordered image patterns. In this work, we are motivated to reveal the intrinsic cause of such pattern disruption in high-resolution image generation. Through theoretical analysis and empirical studies, we reveal that the pre-trained U-Net fails to provide sufficient positional information for tokens at high-resolution. Specifically, 1) zero-padding serves as a critical mechanism for position encoding but lacks robustness across varying resolutions; and 2) tokens located farther from the feature map boundaries have increasing difficulty acquiring positional awareness, leading to pattern disruptions. Inspired by these findings, we propose a novel training-free approach for high-resolution generation, introducing a Progressive Boundary Complement (PBC) method. It creates dynamic virtual image boundaries inside the feature map to supplement position information at high resolution, enabling high-quality and rich-content high-resolution image synthesis. Extensive experiments show that our method significantly improves high-resolution image synthesis in terms of visual quality and content richness, achieving state-of-the-art performance.

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IROS Conference 2025 Conference Paper

3DWSNet: A Novel 3D Wavelet Spiking Neural Network for Event-based Action Recognition

Junkang Fang
Yonghao Dang
Wending Zhao
Bo Yu
Zehao Wang
Jianqin Yin

In robotics applications, event cameras provide low-latency and high-dynamic-range sensing by asynchronously detecting brightness changes, making them well-suited for capturing fast motions and subtle cues in dynamic environments. However, most existing Spiking Neural Network (SNN)-based methods enhance spatial information by stacking multiple frames of events, while neglecting the explicit modeling of high-and low-frequency components in the event stream. To address this limitation, we proposes a 3D Wavelet Spiking Neural Network (3DWSNet), which integrates a 3D wavelet transform with a cascaded Wavelet Spiking Convolution (WSC) module as its core. Specifically, the 3D wavelet transform decomposes input data into eight frequency sub-bands across spatial and temporal dimensions, enabling the model to preserve fine-grained high-frequency details while enriching low-frequency motion representations. The cascaded WSC architecture further improves the extraction of multi-scale spatio-temporal features by integrating information from feature maps at different resolutions. Extensive experiments show that our 3DWSNet significantly outperforms SOTA SNN performances on the CIFAR-10, CIFAR-100, DVS128 Gesture, and CIFAR10-DVS datasets. The source code will be publicly released soon.

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IROS Conference 2025 Conference Paper

MaskSem: Semantic-Guided Masking for Learning 3D Hybrid High-Order Motion Representation

Wei Wei
Shaojie Zhang
Yonghao Dang
Jianqin Yin

Human action recognition is a crucial task for intelligent robotics, particularly within the context of human-robot collaboration research. In self-supervised skeleton-based action recognition, the mask-based reconstruction paradigm learns the spatial structure and motion patterns of the skeleton by masking joints and reconstructing the target from unlabeled data. However, existing methods focus on a limited set of joints and low-order motion patterns, limiting the model’s ability to understand complex motion patterns. To address this issue, we introduce MaskSem, a novel semantic-guided masking method for learning 3D hybrid high-order motion representations. This novel framework leverages Grad-CAM based on relative motion to guide the masking of joints, which can be represented as the most semantically rich temporal orgions. The semantic-guided masking process can encourage the model to explore more discriminative features. Furthermore, we propose using hybrid high-order motion as the reconstruction target, enabling the model to learn multi-order motion patterns. Specifically, low-order motion velocity and high-order motion acceleration are used together as the reconstruction target. This approach offers a more comprehensive description of the dynamic motion process, enhancing the model’s understanding of motion patterns. Experiments on the NTU60, NTU120, and PKU-MMD datasets show that MaskSem, combined with a vanilla transformer, improves skeleton-based action recognition, making it more suitable for applications in human-robot interaction. The source code of our MaskSem is available at https://github.com/JayEason66/MaskSem.

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ICRA Conference 2025 Conference Paper

Quart-Online: Latency-Free Multimodal Large Language Model for Quadruped Robot Learning

Xinyang Tong
Pengxiang Ding
Yiguo Fan
Donglin Wang
Wenjie Zhang
Can Cui 0008
Mingyang Sun
Han Zhao 0008

This paper addresses the inherent inference latency challenges associated with deploying multimodal large language models (MLLM) in quadruped vision-language-action (QUAR-VLA) tasks. Our investigation reveals that conventional parameter reduction techniques ultimately impair the performance of the language foundation model during the action instruction tuning phase, making them unsuitable for this purpose. We introduce a novel latency-free quadruped MLLM model, dubbed QUARTOnline, designed to enhance inference efficiency without degrading the performance of the language foundation model. By incorporating Action Chunk Discretization (ACD), we compress the original action representation space, mapping continuous action values onto a smaller set of discrete representative vectors while preserving critical information. Subsequently, we fine-tune the MLLM to integrate vision, language, and compressed actions into a unified semantic space. Experimental results demonstrate that QUART-Online operates in tandem with the existing MLLM system, achieving real-time inference at 50 Hz in sync with the underlying controller frequency, significantly boosting the success rate across various tasks by 65 %. Our project page is https://quart-online.github.io.

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IROS Conference 2025 Conference Paper

Towards Physically Realizable Adversarial Attacks in Embodied Vision Navigation

Meng Chen
Jiawei Tu
Chao Qi
Yonghao Dang
Feng Zhou
Wei Wei
Jianqin Yin

The significant advancements in embodied vision navigation have raised concerns about its susceptibility to adversarial attacks exploiting deep neural networks. Investigating the adversarial robustness of embodied vision navigation is crucial, especially given the threat of 3D physical attacks that could pose risks to human safety. However, existing attack methods for embodied vision navigation often lack physical feasibility due to challenges in transferring digital perturbations into the physical world. Moreover, current physical attacks for object detection struggle to achieve both multi-view effectiveness and visual naturalness in navigation scenarios. To address this, we propose a practical attack method for embodied navigation by attaching adversarial patches to objects, where both opacity and textures are learnable. Specifically, to ensure effectiveness across varying viewpoints, we employ a multi-view optimization strategy based on object-aware sampling, which optimizes the patch’s texture based on feedback from the vision-based perception model used in navigation. To make the patch inconspicuous to human observers, we introduce a two-stage opacity optimization mechanism, in which opacity is fine-tuned after texture optimization. Experimental results demonstrate that our adversarial patches decrease the navigation success rate by an average of 22. 39%, outperforming previous methods in practicality, effectiveness, and naturalness. Code is available at: github.com/chen37058/Physical-Attacks-in-Embodied-Nav.

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