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Lai Jiang

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

Burst Image Quality Assessment: A New Benchmark and Unified Framework for Multiple Downstream Tasks

Xiaoye Liang
Lai Jiang
Minglang Qiao
Yichen Guo
Yue Zhang
Xin Deng
Shengxi Li
Yufan Liu

In recent years, the development of burst imaging technology has improved the capture and processing capabilities of visual data, enabling a wide range of applications. However, the redundancy in burst images leads to the increased storage and transmission demands, as well as reduced efficiency of downstream tasks. To address this, we propose a new task of Burst Image Quality Assessment (BuIQA), to evaluate the task-driven quality of each frame within a burst sequence, providing reasonable cues for burst image selection. Specifically, we establish the first benchmark dataset for BuIQA, consisting of 7,346 burst sequences with 45,827 images and 191,572 annotated quality scores for multiple downstream scenarios. Inspired by the data analysis, a unified BuIQA framework is proposed to achieve an efficient adaption for BuIQA under diverse downstream scenarios. Specifically, a task-driven prompt generation network is developed with heterogeneous knowledge distillation, to learn the priors of the downstream task. Then, the task-aware quality assessment network is introduced to assess the burst image quality based on the task prompt. Extensive experiments across 10 downstream scenarios demonstrate the impressive BuIQA performance of the proposed approach, outperforming the state-of-the-art. Furthermore, it can achieve 0.33 dB PSNR improvement in the downstream tasks of denoising and super-resolution, by applying our approach to select the high-quality burst frames.

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JBHI Journal 2026 Journal Article

Dual-Domain Visual Prompt Learning for Multi-Modal Medical Image Saliency Prediction

Ning Dai
Mai Xu
Xiaowan Hu
Ce Zheng
Lai Jiang

Medical image saliency prediction plays a pivotal role in emulating clinician visual attention to prioritize diagnostically critical regions. Current methods remain constrained by their spatial-domain dependency and limited cross-modality generalizability, neglecting frequency-domain patterns critical for subtle pathology detection while suffering from over-specialization in specific imaging modalities. Therefore, we propose a dual-domain visual prompt network (DVPNet) that integrates cross-modality generalization with spectral pattern awareness. On the one hand, DVPNet establishes a dataset prompt branch that dynamically modulates spatial feature encoding through modality-specific priors, allowing adaptive interpretation of heterogeneous medical imaging domains. On the other hand, a spatial-frequency hybrid prompt module employs learnable wavelet filters to decompose images into multi-scale spectral components, preserving low-frequency anatomical context while enhancing discriminative high-frequency biomarkers that are typically obscured in previous pixel-level analysis. By seamlessly integrating these complementary representations, DVPNet optimally synthesizes spatial and spectral evidence, enabling robust generalization across diverse medical imaging modalities while sustaining computational efficiency. Extensive experimental results on two distinct datasets demonstrate that the proposed method outperforms state-of-the-art approaches, showing superior saliency prediction performance and enhanced generalizability across medical contexts.

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AAAI Conference 2026 Conference Paper

SceneJailEval: A Scenario-Adaptive Multi-Dimensional Framework for Jailbreak Evaluation

Lai Jiang
Yuekang Li
Xiaohan Zhang
Youtao Ding
Li Pan

Accurate jailbreak evaluation is critical for LLM red team testing and jailbreak research. Mainstream methods rely on binary classification (string matching, toxic text classifiers, and LLM-based methods), outputting only "yes/no" labels without quantifying harm severity. Emerged multi-dimensional frameworks (e.g., Security Violation, Relative Truthfulness and Informativeness) use unified evaluation standards across scenarios, leading to scenario-specific mismatches (e.g., "Relative Truthfulness" is irrelevant to "hate speech"), undermining evaluation accuracy. To address these, we propose SceneJailEval, with key contributions: (1) A pioneering scenario-adaptive multi-dimensional framework for jailbreak evaluation, overcoming the critical "one-size-fits-all" limitation of existing multi-dimensional methods, and boasting robust extensibility to seamlessly adapt to customized or emerging scenarios. (2) A novel 14-scenario dataset featuring rich jailbreak variants and regional cases, addressing the long-standing gap in high-quality, comprehensive benchmarks for scenario-adaptive evaluation. (3) SceneJailEval delivers state-of-the-art performance with an F1 score of 0.917 on our full-scenario dataset (+6% over SOTA) and 0.995 on JBB (+3% over SOTA), breaking through the accuracy bottleneck of existing evaluation methods in heterogeneous scenarios and solidifying its superiority.

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ICML Conference 2025 Conference Paper

Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Fangxu Yu
Lai Jiang
Haoqiang Kang
Shibo Hao
Lianhui Qin

The ability to generate diverse solutions to a given problem is a hallmark of human creativity. This divergent reasoning is also crucial for machines, enhancing their robustness and enabling them to assist humans in many applications such as scientific discovery. However, existing approaches to multi-step reasoning with large language models (LLMs) have mostly focused only on reasoning accuracy, without further discovering more diverse valid solutions. For example, supervised fine-tuning improves reasoning quality but requires vast labeled data, while reward-maximizing reinforcement learning finds top-reward solutions while neglecting the solution diversity. To fill this gap, we propose Flow of Reasoning (FoR), an efficient diversity-seeking LLM finetuning method aimed at improving reasoning quality and diversity with minimal data. FoR formulates multi-step LLM reasoning as a Markovian flow on a DAG-structured reasoning graph. This formulation allows us to incorporate and adapt principled GFlowNet approaches, for finetuning LLMs to sample divergent paths with probabilities proportional to the (unnormalized) reward of target problems. Extensive experiments show that, with limited training examples (e. g. , 15 examples), FoR enables the discovery of diverse, creative, high-quality solutions, greatly outperforming a wide range of existing inference and training methods across six challenging reasoning tasks, including BlocksWorld (embodied reasoning), Game24 (math puzzle solving), Rubik’s Cube (spatial reasoning), 1D-ARC (abstraction reasoning), GSM8k (math reasoning), and ProntoQA (logical reasoning). Code is available at https: //github. com/Yu-Fangxu/FoR.

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JBHI Journal 2022 Journal Article

Joint Learning of Multi-Level Tasks for Diabetic Retinopathy Grading on Low-Resolution Fundus Images

Xiaofei Wang
Mai Xu
Jicong Zhang
Lai Jiang
Liu Li
Mengxian He
Ningli Wang
Hanruo Liu

Diabetic retinopathy (DR) is a leading cause of permanent blindness among the working-age people. Automatic DR grading can help ophthalmologists make timely treatment for patients. However, the existing grading methods are usually trained with high resolution (HR) fundus images, such that the grading performance decreases a lot given low resolution (LR) images, which are common in clinic. In this paper, we mainly focus on DR grading with LR fundus images. According to our analysis on the DR task, we find that: 1) image super-resolution (ISR) can boost the performance of both DR grading and lesion segmentation; 2) the lesion segmentation regions of fundus images are highly consistent with pathological regions for DR grading. Based on our findings, we propose a convolutional neural network (CNN)-based method for joint learning of multi-level tasks for DR grading, called DeepMT-DR, which can simultaneously handle the low-level task of ISR, the mid-level task of lesion segmentation and the high-level task of disease severity classification on LR fundus images. Moreover, a novel task-aware loss is developed to encourage ISR to focus on the pathological regions for its subsequent tasks: lesion segmentation and DR grading. Extensive experimental results show that our DeepMT-DR method significantly outperforms other state-of-the-art methods for DR grading over three datasets. In addition, our method achieves comparable performance in two auxiliary tasks of ISR and lesion segmentation.

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AAAI Conference 2021 Conference Paper

Deep Multi-Task Learning for Diabetic Retinopathy Grading in Fundus Images

Xiaofei Wang
Mai Xu
Jicong Zhang
Lai Jiang
Liu Li

Recent years have witnessed the growing interest in disease severity grading, especially for ocular diseases based on fundus images. The existing grading methods are usually trained with high resolution (HR) images. However, the grading performance decreases a lot given low resolution (LR) images, which are common in practice. In this paper, we mainly focus on diabetic retinopathy (DR) grading with LR fundus images. According to our analysis on the DR task, we find that: 1) image super-resolution (ISR) can boost the performance of DR grading and lesion segmentation; 2) the lesion segmentation regions of fundus images are highly consistent with pathological regions for DR grading. Thus, we propose a deep multi-task learning based DR grading (DeepMT-DR) method for LR fundus images, which simultaneously handles the auxiliary tasks of ISR and lesion segmentation. Specifically, based on our findings, we propose a hierarchical deep learning structure that simultaneously processes the low-level task of ISR, the mid-level task of lesion segmentation and the high-level task of DR grading. Moreover, a novel task-aware loss is developed to encourage ISR to focus on the pathological regions for its subsequent tasks: lesion segmentation and DR grading. Extensive experimental results show that our DeepMT-DR method significantly outperforms other stateof-the-art methods for DR grading over two public datasets. In addition, our method achieves comparable performance in two auxiliary tasks of ISR and lesion segmentation.

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AAAI Conference 2019 Conference Paper

Image Saliency Prediction in Transformed Domain: A Deep Complex Neural Network Method

Lai Jiang
Zhe Wang
Mai Xu
Zulin Wang

The transformed domain fearures of images show effectiveness in distinguishing salient and non-salient regions. In this paper, we propose a novel deep complex neural network, named Sal- DCNN, to predict image saliency by learning features in both pixel and transformed domains. Before proposing Sal-DCNN, we analyze the saliency cues encoded in discrete Fourier transform (DFT) domain. Consequently, we have the following findings: 1) the phase spectrum encodes most saliency cues; 2) a certain pattern of the amplitude spectrum is important for saliency prediction; 3) the transformed domain spectrum is robust to noise and down-sampling for saliency prediction. According to these findings, we develop the structure of Sal- DCNN, including two main stages: the complex dense encoder and three-stream multi-domain decoder. Given the new Sal- DCNN structure, the saliency maps can be predicted under the supervision of ground-truth fixation maps in both pixel and transformed domains. Finally, the experimental results show that our Sal-DCNN method outperforms other 8 state-of-theart methods for image saliency prediction on 3 databases.

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