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Jianxin Lin

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

MedEyes: Learning Dynamic Visual Focus for Medical Progressive Diagnosis

Chunzheng Zhu
Yangfang Lin
Shen Chen
Yijun Wang
Jianxin Lin

Accurate medical diagnosis often involves progressive visual focusing and iterative reasoning, characteristics commonly observed in clinical workflows. While recent vision-language models demonstrate promising chain-of-thought (CoT) reasoning capabilities via reinforcement learning with verifiable rewards (RLVR), their purely on-policy learning paradigm tends to reinforce superficially coherent but clinically inaccurate reasoning paths. We propose MedEyes, a novel reinforcement learning framework that dynamically models clinician-style diagnostic reasoning by progressively attending to and interpreting relevant medical image regions. By incorporating off-policy expert guidance, MedEyes converts expert visual search trajectories into structured external behavioral signals, guiding the model toward clinically aligned visual reasoning. We design the Gaze-guided Reasoning Navigator (GRN) to emulate the diagnostic process through a dual-mode exploration strategy, scanning for systematic abnormality localization and drilling for detailed regional analysis. To balance expert imitation and autonomous discovery, we introduce the Confidence Value Sampler (CVS), which employs nucleus sampling and adaptive termination to create diverse yet credible exploration paths. Finally, the dual-stream GRPO optimization framework decouples on-policy and off-policy learning signals, mitigating reward assimilation and entropy collapse. Experiments demonstrate that MedEyes achieves an average performance improvement of +8.5% across multiple medical VQA benchmarks, validating MedEyes's potential in building trustworthy medical AI systems.

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ICLR Conference 2025 Conference Paper

HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting

Nian Ran
Peng Xiao
Yue Wang
Wesley Shi
Jianxin Lin
Qi Meng
Richard Allmendinger 0001

The application of large deep learning models in weather forecasting has led to significant advancements in the field, including higher-resolution forecasting and extended prediction periods exemplified by models such as Pangu and Fuxi. Despite these successes, previous research has largely been characterized by the neglect of extreme weather events, and the availability of datasets specifically curated for such events remains limited. Given the critical importance of accurately forecasting extreme weather, this study introduces a comprehensive dataset that incorporates high-resolution extreme weather cases derived from the High-Resolution Rapid Refresh (HRRR) data, a 3-km real-time dataset provided by NOAA. We also evaluate the current state-of-the-art deep learning models and Numerical Weather Prediction (NWP) systems on HR-Extreme, and provide a improved baseline deep learning model called HR-Heim which has superior performance on both general loss and HR-Extreme compared to others. Our results reveal that the errors of extreme weather cases are significantly larger than overall forecast error, highlighting them as an crucial source of loss in weather prediction. These findings underscore the necessity for future research to focus on improving the accuracy of extreme weather forecasts to enhance their practical utility

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EAAI Journal 2024 Journal Article

Graph-enhanced ensembles of multi-scale structure perception deep architecture for fetal ultrasound plane recognition

Zhan Gao
Guanghua Tan
Chunlian Wang
Jianxin Lin
Bin Pu
Shengli Li
Kenli Li

Ultrasound imaging plays a pivotal role in assessing fetal health and diagnosing diseases. However, the inherent challenges posed by the high similarity of certain fetal anatomical structures and noise in ultrasound images hinder the accurate identification of ultrasound planes. To address this challenge, we present a graph-enhanced ensembles of multi-scale structure perception architecture for more precise identification of standard ultrasound planes. Specifically, we introduce a local-to-global multi-granularity ensemble module for feature enhancement and noise suppression, coupled with a graph-based multi-view refinement module to perceive relationships within fetal anatomical structures. Additionally, we employ multiple classifiers with branch subnets to capture fine-grained structure representations and integrate a confidence evaluation loss to ensure alignment with the true distribution. Finally, the collaborative decision-making among classifiers is achieved through confidence matching. Extensive experiments demonstrate that our innovative framework achieves state-of-the-art classification performance on multiple datasets, and verify that the improvements benefit from the proposed modules. We also show that our method has outstanding capabilities in evaluating the quality of fetal ultrasound planes, a distinctive attribute absent in current methods. Code is available at: https: //github. com/201610006/graph-ensemble-net. git.

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AAAI Conference 2020 Conference Paper

Learning to Transfer: Unsupervised Domain Translation via Meta-Learning

Jianxin Lin
Yijun Wang
Zhibo Chen
Tianyu He

Unsupervised domain translation has recently achieved impressive performance with Generative Adversarial Network (GAN) and sufﬁcient (unpaired) training data. However, existing domain translation frameworks form in a disposable way where the learning experiences are ignored and the obtained model cannot be adapted to a new coming domain. In this work, we take on unsupervised domain translation problems from a meta-learning perspective. We propose a model called Meta-Translation GAN (MT-GAN) to ﬁnd good initialization of translation models. In the meta-training procedure, MT- GAN is explicitly trained with a primary translation task and a synthesized dual translation task. A cycle-consistency metaoptimization objective is designed to ensure the generalization ability. We demonstrate effectiveness of our model on ten diverse two-domain translation tasks and multiple face identity translation tasks. We show that our proposed approach signiﬁcantly outperforms the existing domain translation methods when each domain contains no more than ten training samples.

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IJCAI Conference 2019 Conference Paper

Deliberation Learning for Image-to-Image Translation

Tianyu He
Yingce Xia
Jianxin Lin
Xu Tan
Di He
Tao Qin
Zhibo Chen

Image-to-image translation, which transfers an image from a source domain to a target one, has attracted much attention in both academia and industry. The major approach is to adopt an encoder-decoder based framework, where the encoder extracts features from the input image and then the decoder decodes the features and generates an image in the target domain as the output. In this paper, we go beyond this learning framework by considering an additional polishing step on the output image. Polishing an image is very common in human's daily life, such as editing and beautifying a photo in Photoshop after taking/generating it by a digital camera. Such a deliberation process is shown to be very helpful and important in practice and thus we believe it will also be helpful for image translation. Inspired by the success of deliberation network in natural language processing, we extend deliberation process to the field of image translation. We verify our proposed method on four two-domain translation tasks and one multi-domain translation task. Both the qualitative and quantitative results demonstrate the effectiveness of our method.

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IJCAI Conference 2019 Conference Paper

Image-to-Image Translation with Multi-Path Consistency Regularization

Jianxin Lin
Yingce Xia
Yijun Wang
Tao Qin
Zhibo Chen

Image translation across different domains has attracted much attention in both machine learning and computer vision communities. Taking the translation from a source domain to a target domain as an example, existing algorithms mainly rely on two kinds of loss for training: One is the discrimination loss, which is used to differentiate images generated by the models and natural images; the other is the reconstruction loss, which measures the difference between an original image and the reconstructed version. In this work, we introduce a new kind of loss, multi-path consistency loss, which evaluates the differences between direct translation from source domain to target domain and indirect translation from source domain to an auxiliary domain to target domain, to regularize training. For multi-domain translation (at least, three) which focuses on building translation models between any two domains, at each training iteration, we randomly select three domains, set them respectively as the source, auxiliary and target domains, build the multi-path consistency loss and optimize the network. For two-domain translation, we need to introduce an additional auxiliary domain and construct the multi-path consistency loss. We conduct various experiments to demonstrate the effectiveness of our proposed methods, including face-to-face translation, paint-to-photo translation, and de-raining/de-noising translation.

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AAAI Conference 2018 Conference Paper

Multi-Scale Face Restoration With Sequential Gating Ensemble Network

Jianxin Lin
Tiankuang Zhou
Zhibo Chen

Restoring face images from distortions is important in face recognition applications and is challenged by multiple scale issues, which is still not well-solved in research area. In this paper, we present a Sequential Gating Ensemble Network (SGEN) for multi-scale face restoration issue. We ﬁrst employ the principle of ensemble learning into SGEN architecture design to reinforce predictive performance of the network. The SGEN aggregates multi-level base-encoders and base-decoders into the network, which enables the network to contain multiple scales of receptive ﬁeld. Instead of combining these base-en/decoders directly with non-sequential operations, the SGEN takes base-en/decoders from different levels as sequential data. Speciﬁcally, the SGEN learns to sequentially extract high level information from base-encoders in bottom-up manner and restore low level information from base-decoders in top-down manner. Besides, we propose to realize bottom-up and top-down information combination and selection with Sequential Gating Unit (SGU). The SGU sequentially takes two inputs from different levels and decides the output based on one active input. Experiment results demonstrate that our SGEN is more effective at multiscale human face restoration with more image details and less noise than state-of-the-art image restoration models. By using adversarial training, SGEN also produces more visually preferred results than other models through subjective evaluation.

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NeurIPS Conference 2017 Conference Paper

Deliberation Networks: Sequence Generation Beyond One-Pass Decoding

Yingce Xia
Fei Tian
Lijun Wu
Jianxin Lin
Tao Qin
Nenghai Yu
Tie-Yan Liu

The encoder-decoder framework has achieved promising progress for many sequence generation tasks, including machine translation, text summarization, dialog system, image captioning, etc. Such a framework adopts an one-pass forward process while decoding and generating a sequence, but lacks the deliberation process: A generated sequence is directly used as final output without further polishing. However, deliberation is a common behavior in human's daily life like reading news and writing papers/articles/books. In this work, we introduce the deliberation process into the encoder-decoder framework and propose deliberation networks for sequence generation. A deliberation network has two levels of decoders, where the first-pass decoder generates a raw sequence and the second-pass decoder polishes and refines the raw sentence with deliberation. Since the second-pass deliberation decoder has global information about what the sequence to be generated might be, it has the potential to generate a better sequence by looking into future words in the raw sentence. Experiments on neural machine translation and text summarization demonstrate the effectiveness of the proposed deliberation networks. On the WMT 2014 English-to-French translation task, our model establishes a new state-of-the-art BLEU score of 41. 5.

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