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Yingjie Cai

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5 papers

2 author rows

ICLR Conference 2025 Conference Paper

DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation

Jing He
Haodong Li
Yongzhe Hu
Guibao Shen
Yingjie Cai
Weichao Qiu
Ying-Cong Chen

In the realm of image generation, creating customized images from visual prompt with additional textual instruction emerges as a promising endeavor. However, existing methods, both tuning-based and tuning-free, struggle with interpreting the subject-essential attributes from the visual prompt. This leads to subject-irrelevant attributes infiltrating the generation process, ultimately compromising the personalization quality in both editability and ID preservation. In this paper, we present $\textbf{DisEnvisioner}$, a novel approach for effectively extracting and enriching the subject-essential features while filtering out -irrelevant information, enabling exceptional customization performance, in a $\textbf{tuning-free}$ manner and using only $\textbf{a single image}$. Specifically, the feature of the subject and other irrelevant components are effectively separated into distinctive visual tokens, enabling a much more accurate customization. Aiming to further improving the ID consistency, we enrich the disentangled features, sculpting them into a more granular representation. Experiments demonstrate the superiority of our approach over existing methods in instruction response (editability), ID consistency, inference speed, and the overall image quality, highlighting the effectiveness and efficiency of DisEnvisioner.

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ICRA Conference 2025 Conference Paper

Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large Language Models

Tianshuo Xu
Hao Lu 0009
Xu Yan 0005
Yingjie Cai
Bingbing Liu
Ying-Cong Chen

Large Language Models (LLMs) have made substantial advancements in the field of robotic and autonomous driving. This study presents the first Occupancy-based Large Language Model (Occ-LLM), which represents a pioneering effort to integrate LLMs with an important representation. To effectively encode occupancy as input for the LLM and address the category imbalances associated with occupancy, we propose Motion Separation Variational Autoencoder (MS-VAE). This innovative approach utilizes prior knowledge to distinguish dynamic objects from static scenes before inputting them into a tailored Variational Autoencoder (VAE). This separation enhances the model's capacity to concentrate on dynamic trajectories while effectively reconstructing static scenes. The efficacy of Occ-LLM has been validated across key tasks, including 4D occupancy forecasting, self-ego planning, and occupancybased scene question answering. Comprehensive evaluations demonstrate that Occ-LLM significantly surpasses existing state-of-the-art methodologies, achieving gains of about 6% in Intersection over Union (IoU) and 4% in mean Intersection over Union (mIoU) for the task of 4D occupancy forecasting. These findings highlight the transformative potential of Occ-LLM in reshaping current paradigms within robotic and autonomous driving.

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NeurIPS Conference 2025 Conference Paper

SQS: Enhancing Sparse Perception Models via Query-based Splatting in Autonomous Driving

Haiming Zhang
Yiyao Zhu
Wending Zhou
Xu Yan
Yingjie Cai
Bingbing Liu
Shuguang Cui
Zhen Li

Sparse Perception Models (SPMs) adopt a query-driven paradigm that forgoes explicit dense BEV or volumetric construction, enabling highly efficient computation and accelerated inference. In this paper, we introduce SQS, a novel query-based splatting pre-training specifically designed to advance SPMs in autonomous driving. SQS introduces a plug-in module that predicts 3D Gaussian representations from sparse queries during pre-training, leveraging self-supervised splatting to learn fine-grained contextual features through the reconstruction of multi-view images and depth maps. During fine-tuning, the pre-trained Gaussian queries are seamlessly integrated into downstream networks via query interaction mechanisms that explicitly connect pre-trained queries with task-specific queries, effectively accommodating the diverse requirements of occupancy prediction and 3D object detection. Extensive experiments on autonomous driving benchmarks demonstrate that SQS delivers considerable performance gains across multiple query-based 3D perception tasks, notably in occupancy prediction and 3D object detection, outperforming prior state-of-the-art pre-training approaches by a significant margin (i. e. , +1. 3 mIoU on occupancy prediction and +1. 0 NDS on 3D detection).

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YNICL Journal 2023 Journal Article

A presurgical voxel-wise predictive model for cerebellar mutism syndrome in children with posterior fossa tumors

Wei Yang
Yiming Li
Zesheng Ying
Yingjie Cai
Xiaojiao Peng
HaiLang Sun
Jiashu Chen
Kaiyi Zhu

BACKGROUND: This study aimed to investigate cerebellar mutism syndrome (CMS)-related voxels and build a voxel-wise predictive model for CMS. METHODS: From July 2013 to January 2022, 188 pediatric patients diagnosed with posterior fossa tumor were included in this study, including 38 from a prospective cohort recruited between 2020 and January 2022, and the remaining from a retrospective cohort recruited in July 2013-Aug 2020. The retrospective cohort was divided into the training and validation sets; the prospective cohort served as a prospective validation set. Voxel-based lesion symptoms were assessed to identify voxels related to CMS, and a predictive model was constructed and tested in the validation and prospective validation sets. RESULTS: No significant differences were detected among these three data sets in CMS rate, gender, age, tumor size, tumor consistency, presence of hydrocephalus and paraventricular edema. Voxels related to CMS were mainly located in bilateral superior and inferior cerebellar peduncles and the superior part of the cerebellum. The areas under the curves for the model in the training, validation and prospective validation sets were 0.889, 0.784 and 0.791, respectively. CONCLUSIONS: Superior and inferior cerebellar peduncles and the superior part of the cerebellum were related to CMS, especially the right side, and voxel-based lesion-symptom analysis could provide valuable predictive information before surgery.

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AAAI Conference 2020 Conference Paper

Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation

Yingjie Cai
Buyu Li
Zeyu Jiao
Hongsheng Li
Xingyu Zeng
Xiaogang Wang

Monocular 3D object detection task aims to predict the 3D bounding boxes of objects based on monocular RGB images. Since the location recovery in 3D space is quite difﬁcult on account of absence of depth information, this paper proposes a novel uniﬁed framework which decomposes the detection problem into a structured polygon prediction task and a depth recovery task. Different from the widely studied 2D bounding boxes, the proposed novel structured polygon in the 2D image consists of several projected surfaces of the target object. Compared to the widely-used 3D bounding box proposals, it is shown to be a better representation for 3D detection. In order to inversely project the predicted 2D structured polygon to a cuboid in the 3D physical world, the following depth recovery task uses the object height prior to complete the inverse projection transformation with the given camera projection matrix. Moreover, a ﬁne-grained 3D box reﬁnement scheme is proposed to further rectify the 3D detection results. Experiments are conducted on the challenging KITTI benchmark, in which our method achieves state-of-the-art detection accuracy.

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