Author name cluster

Jun Yu 0001

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11 papers

1 author row

ICRA Conference 2025 Conference Paper

Dualdiff: Dual-Branch Diffusion Model for Autonomous Driving with Semantic Fusion

Haoteng Li
Zhao Yang
Zezhong Qian
Gongpeng Zhao
Yuqi Huang
Jun Yu 0001
Huazheng Zhou
Longjun Liu

Accurate and high-fidelity driving scene reconstruction relies on fully leveraging scene information as conditioning. However, existing approaches, which primarily use 3D bounding boxes and binary maps for foreground and background control, fall short in capturing the complexity of the scene and integrating multi-modal information. In this paper, we propose DualDiff, a dual-branch conditional diffusion model designed to enhance multi-view driving scene generation. We introduce Occupancy Ray Sampling (ORS), a semantic-rich 3D representation, alongside numerical driving scene representation, for comprehensive foreground and background control. To improve cross-modal information integration, we propose a Semantic Fusion Attention (SFA) mechanism that aligns and fuses features across modalities. Furthermore, we design a foreground-aware masked (FGM) loss to enhance the generation of tiny objects. DualDiff achieves state-of-the-art performance in FID score, as well as consistently better results in downstream BEV segmentation and 3D object detection tasks.

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ICRA Conference 2025 Conference Paper

Towards Robust Autonomous Driving: Conditional Multimodal Large Language Models for Fine-Grained Perception

Fengzhao Sun
Jun Yu 0001
Yunxiang Zhang
Jiaming Hou
Xilong Lu
Heng Song
Fang Gao 0001

Multimodal large language models (MLLMs) have shown remarkable performance across various visual understanding tasks. However, most existing MLLMs still lack image detail perception, limiting their effectiveness in tasks that require detailed visual information. In this paper, we introduce Percept-DriveLM, a novel MLLM designed to tackle the fine-grained perception challenges in autonomous driving tasks. At the core of our model is the Visual Fusion Module, which integrates several innovative components: a dynamic resolution mechanism that combines both high and low resolution features, and an RoI conditional mechanism to incorporate object/region-level features identified by offline detectors, further refining the model's fine-grained perception abilities. Trained in a two-stage process, our model demonstrates exceptional performance, outperforming existing MLLMs with comparable parameter sizes and excelling in both autonomous driving perception and general vision-language tasks. The effectiveness of our approach is validated through extensive empirical studies. Code will be available at https://github.com/DebuggerSunfz/PerceptDriveLM.

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ICML Conference 2024 Conference Paper

Mitigating Label Noise on Graphs via Topological Sample Selection

Yuhao Wu
Jiangchao Yao
Xiaobo Xia
Jun Yu 0001
Ruxin Wang 0002
Bo Han 0003
Tongliang Liu

Despite the success of the carefully-annotated benchmarks, the effectiveness of existing graph neural networks (GNNs) can be considerably impaired in practice when the real-world graph data is noisily labeled. Previous explorations in sample selection have been demonstrated as an effective way for robust learning with noisy labels, however, the conventional studies focus on i. i. d data, and when moving to non-iid graph data and GNNs, two notable challenges remain: (1) nodes located near topological class boundaries are very informative for classification but cannot be successfully distinguished by the heuristic sample selection. (2) there is no available measure that considers the graph topological information to promote sample selection in a graph. To address this dilemma, we propose a $\textit{Topological Sample Selection}$ (TSS) method that boosts the informative sample selection process in a graph by utilising topological information. We theoretically prove that our procedure minimizes an upper bound of the expected risk under target clean distribution, and experimentally show the superiority of our method compared with state-of-the-art baselines.

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ICLR Conference 2024 Conference Paper

Neural Auto-designer for Enhanced Quantum Kernels

Cong Lei
Yuxuan Du
Peng Mi
Jun Yu 0001
Tongliang Liu

Quantum kernels hold great promise for offering computational advantages over classical learners, with the effectiveness of these kernels closely tied to the design of the feature map. However, the challenge of designing effective quantum feature maps for real-world datasets, particularly in the absence of sufficient prior information, remains a significant obstacle. In this study, we present a data-driven approach that automates the design of problem-specific quantum feature maps. Our approach leverages feature-selection techniques to handle high-dimensional data on near-term quantum machines with limited qubits, and incorporates a deep neural predictor to efficiently evaluate the performance of various candidate quantum kernels. Through extensive numerical simulations on different datasets, we demonstrate the superiority of our proposal over prior methods, especially for the capability of eliminating the kernel concentration issue and identifying the feature map with prediction advantages. Our work not only unlocks the potential of quantum kernels for enhancing real-world tasks, but also highlights the substantial role of deep learning in advancing quantum machine learning.

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ICML Conference 2024 Conference Paper

Towards Realistic Model Selection for Semi-supervised Learning

Muyang Li
Xiaobo Xia
Runze Wu
Fengming Huang
Jun Yu 0001
Bo Han 0003
Tongliang Liu

Semi-supervised Learning (SSL) has shown remarkable success in applications with limited supervision. However, due to the scarcity of labels in the training process, SSL algorithms are known to be impaired by the lack of proper model selection, as splitting a validation set will further reduce the limited labeled data, and the size of the validation set could be too small to provide a reliable indication to the generalization error. Therefore, we seek alternatives that do not rely on validation data to probe the generalization performance of SSL models. Specifically, we find that the distinct margin distribution in SSL can be effectively utilized in conjunction with the model’s spectral complexity, to provide a non-vacuous indication of the generalization error. Built upon this, we propose a novel model selection method, specifically tailored for SSL, known as S pectral-normalized La beled-margin M inimization (SLAM). We prove that the model selected by SLAM has upper-bounded differences w. r. t. the best model within the search space. In addition, comprehensive experiments showcase that SLAM can achieve significant improvements compared to its counterparts, verifying its efficacy from both theoretical and empirical standpoints.

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IROS Conference 2023 Conference Paper

Lightweight Neural Path Planning

Jinsong Li
Shaochen Wang
Ziyang Chen
Zhen Kan
Jun Yu 0001

Learning-based path planning is becoming a promising robot navigation methodology due to its adaptability to various environments. However, the expensive computing and storage associated with networks impose significant challenges for their deployment on low-cost robots. Motivated by this practical challenge, we develop a lightweight neural path planning architecture with a dual input network and a hybrid sampler for resource-constrained robotic systems. Our architecture is designed with efficient task feature extraction and fusion modules to translate the given planning instance into a guidance map. The hybrid sampler is then applied to restrict the planning within the prospective regions indicated by the guide map. To enable the network training, we further construct a publicly available dataset with various successful planning instances. Numerical simulations and physical experiments demonstrate that, compared with baseline approaches, our approach has nearly an order of magnitude fewer model size and five times lower computational while achieving promising performance. Besides, our approach can also accelerate the planning convergence process with fewer planning iterations compared to sample-based methods.

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ICLR Conference 2023 Conference Paper

Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning

Xiaobo Xia
Jiale Liu
Jun Yu 0001
Xu Shen 0001
Bo Han 0003
Tongliang Liu

Deep learning methods nowadays rely on massive data, resulting in substantial costs of data storage and model training. Data selection is a useful tool to alleviate such costs, where a coreset of massive data is extracted to practically perform on par with full data. Based on carefully-designed score criteria, existing methods first count the score of each data point and then select the data points whose scores lie in a certain range to construct a coreset. These methods work well in their respective preconceived scenarios but are not robust to the change of scenarios, since the optimal range of scores varies as the scenario changes. The issue limits the application of these methods, because realistic scenarios often mismatch preconceived ones, and it is inconvenient or unfeasible to tune the criteria and methods accordingly. In this paper, to address the issue, a concept of the moderate coreset is discussed. Specifically, given any score criterion of data selection, different scenarios prefer data points with scores in different intervals. As the score median is a proxy of the score distribution in statistics, the data points with scores close to the score median can be seen as a proxy of full data and generalize different scenarios, which are used to construct the moderate coreset. As a proof-of-concept, a universal method that inherits the moderate coreset and uses the distance of a data point to its class center as the score criterion, is proposed to meet complex realistic scenarios. Extensive experiments confirm the advance of our method over prior state-of-the-art methods, leading to a strong baseline for future research. The implementation is available at https://github.com/tmllab/Moderate-DS.

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ICLR Conference 2023 Conference Paper

Mosaic Representation Learning for Self-supervised Visual Pre-training

Zhaoqing Wang
Ziyu Chen
Yaqian Li
Yandong Guo
Jun Yu 0001
Mingming Gong
Tongliang Liu

Self-supervised learning has achieved significant success in learning visual representations without the need for manual annotation. To obtain generalizable representations, a meticulously designed data augmentation strategy is one of the most crucial parts. Recently, multi-crop strategies utilizing a set of small crops as positive samples have been shown to learn spatially structured features. However, it overlooks the diverse contextual backgrounds, which reduces the variance of the input views and degenerates the performance. To address this problem, we propose a mosaic representation learning framework (MosRep), consisting of a new data augmentation strategy that enriches the backgrounds of each small crop and improves the quality of visual representations. Specifically, we randomly sample numbers of small crops from different input images and compose them into a mosaic view, which is equivalent to introducing different background information for each small crop. Additionally, we further jitter the mosaic view to prevent memorizing the spatial locations of each crop. Along with optimization, our MosRep gradually extracts more discriminative features. Extensive experimental results demonstrate that our method improves the performance far greater than the multi-crop strategy on a series of downstream tasks, e.g., +7.4% and +4.9% than the multi-crop strategy on ImageNet-1K with 1% label and 10% label, respectively. Code is available at https://github.com/DerrickWang005/MosRep.git.

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ICML Conference 2023 Conference Paper

Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?

Yu Yao 0005
Mingming Gong
Yuxuan Du
Jun Yu 0001
Bo Han 0003
Kun Zhang 0001
Tongliang Liu

In real life, accurately annotating large-scale datasets is sometimes difficult. Datasets used for training deep learning models are likely to contain label noise. To make use of the dataset containing label noise, two typical methods have been proposed. One is to employ the semi-supervised method by exploiting labeled confident examples and unlabeled unconfident examples. The other one is to model label noise and design statistically consistent classifiers. A natural question remains unsolved: which one should be used for a specific real-world application? In this paper, we answer the question from the perspective of causal data generative process. Specifically, the performance of the semi-supervised based method depends heavily on the data generative process while the method modeling label-noise is not influenced by the generation process. For example, for a given dataset, if it has a causal generative structure that the features cause the label, the semi-supervised based method would not be helpful. When the causal structure is unknown, we provide an intuitive method to discover the causal structure for a given dataset containing label noise.

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ICLR Conference 2022 Conference Paper

Sample Selection with Uncertainty of Losses for Learning with Noisy Labels

Xiaobo Xia
Tongliang Liu
Bo Han 0003
Mingming Gong
Jun Yu 0001
Gang Niu 0001
Masashi Sugiyama

In learning with noisy labels, the sample selection approach is very popular, which regards small-loss data as correctly labeled data during training. However, losses are generated on-the-ﬂy based on the model being trained with noisy labels, and thus large-loss data are likely but not certain to be incorrect. There are actually two possibilities of a large-loss data point: (a) it is mislabeled, and then its loss decreases slower than other data, since deep neural networks learn patterns ﬁrst; (b) it belongs to an underrepresented group of data and has not been selected yet. In this paper, we incorporate the uncertainty of losses by adopting interval estimation instead of point estimation of losses, where lower bounds of the conﬁdence intervals of losses derived from distribution-free concentration inequalities, but not losses themselves, are used for sample selection. In this way, we also give large-loss but less selected data a try; then, we can better distinguish between the cases (a) and (b) by seeing if the losses effectively decrease with the uncertainty after the try. As a result, we can better explore underrepresented data that are correctly labeled but seem to be mislabeled at ﬁrst glance. Experiments demonstrate that the proposed method is superior to baselines and robust to a broad range of label noise types.

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ICML Conference 2022 Conference Paper

Understanding Robust Overfitting of Adversarial Training and Beyond

Chaojian Yu
Bo Han 0003
Li Shen 0008
Jun Yu 0001
Chen Gong 0002
Mingming Gong
Tongliang Liu

Robust overfitting widely exists in adversarial training of deep networks. The exact underlying reasons for this are still not completely understood. Here, we explore the causes of robust overfitting by comparing the data distribution of non-overfit (weak adversary) and overfitted (strong adversary) adversarial training, and observe that the distribution of the adversarial data generated by weak adversary mainly contain small-loss data. However, the adversarial data generated by strong adversary is more diversely distributed on the large-loss data and the small-loss data. Given these observations, we further designed data ablation adversarial training and identify that some small-loss data which are not worthy of the adversary strength cause robust overfitting in the strong adversary mode. To relieve this issue, we propose minimum loss constrained adversarial training (MLCAT): in a minibatch, we learn large-loss data as usual, and adopt additional measures to increase the loss of the small-loss data. Technically, MLCAT hinders data fitting when they become easy to learn to prevent robust overfitting; philosophically, MLCAT reflects the spirit of turning waste into treasure and making the best use of each adversarial data; algorithmically, we designed two realizations of MLCAT, and extensive experiments demonstrate that MLCAT can eliminate robust overfitting and further boost adversarial robustness.

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