Author name cluster

Fen Fang

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

Editing Is a Bargaining Game: Balanced Knowledge Editing in Large Language Models

Chenghao Xu
Jiexi Yan
Muli Yang
Fen Fang
Huilin Chen
Cheng Deng

Large Language Models (LLMs) are prone to generating incorrect or outdated information, thereby necessitating efficient and precise mechanisms for knowledge updates. Existing knowledge editing approaches, however, often encounter conflicts between two competing objectives: maintaining existing knowledge (preservation) and incorporating new information (editing). During gradient-based optimization, these conflicting objectives can lead to imbalanced update directions, where one gradient dominates, ultimately resulting in suboptimal learning dynamics. To address this challenge, we propose a balanced knowledge editing framework inspired by Nash bargaining theory. Our method guides the optimization process toward a Pareto stationary point, ensuring an equilibrium solution wherein any deviation from the final state would degrade the overall performance with respect to both objectives. This guarantees optimality in preserving prior knowledge while integrating new information. We empirically validate the effectiveness of our approach across a range of evaluation metrics on standard benchmark datasets. Extensive experiments show that our method consistently outperforms state-of-the-art techniques, achieving a superior balance between knowledge preservation and update accuracy.

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AAAI Conference 2026 System Paper

Next-Generation Metalens Vision System: Powered by AI and Applied to AI

Fen Fang
Muli Yang
Henan Wang
Xinan Liang
Tobias Mass
Xuewu Xu
Xulei Yang
Zhengguo Li

Metalenses have been widely recognized as a key building block of next-generation optical systems, offering unprecedented advantages in compactness, lightweight design, and scalable manufacturing compared to traditional refractive optics. Despite this promise, practical use is limited by optical aberrations, blur, and illumination sensitivity, which degrade both visual quality and machine perception. In this demonstration, we present an end-to-end metalens vision system—from hardware sensing with a custom-built RGB metalens camera, to physics-informed imaging and real-time restoration, and finally to downstream vision applications such as object detection and depth estimation. By integrating spatially-aware attention enhancement and reinforcement learning-based illumination control into a real-time system, our solution transforms degraded raw captures into high-fidelity images that are both visually interpretable and functionally reliable for machine vision. This AI-powered pipeline highlights metalenses as a cornerstone for next-generation imaging, where advances in optics and machine intelligence jointly drive the future of visual perception.

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AAAI Conference 2026 Conference Paper

Towards Illumination-Aware Restoration of Metalens-Captured Images: A New Dataset and a Strong Baseline

Fen Fang
Xinan Liang
Muli Yang
Jinghong Zheng
Tobias Mass
Ying Sun
Xulei Yang
Xuewu Xu

Metalenses offer compelling advantages such as lightweight and ultra-thin design, making them promising alternatives to conventional lenses. However, their widespread adoption is hindered by image quality degradation caused by chromatic and angular aberrations. To mitigate this, restoration processes are often necessary to recover high-quality RGB images from metalens-captured inputs. While recent deep learning-based restoration methods show promise, they typically (1) blur or distort peripheral regions, or (2) fail entirely under unseen illumination conditions. To advance metalens image restoration, we introduce IlluMeta---the first and largest real-world, illumination-aware metalens image dataset—captured across diverse lighting environments. In addition, we propose a novel end-to-end restoration framework that directs attention to challenging regions and adaptively adjusts to varying illuminations via reinforcement learning. Experiments show that our method can be applied in a plug-and-play manner to enhance existing models, significantly improving image restoration quality, especially under unseen lighting conditions, paving the way for broader real-world deployment of metalens technologies.

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AAAI Conference 2026 Conference Paper

Your AI-Generated Image Detector Can Secretly Achieve SOTA Accuracy, If Calibrated

Muli Yang
Gabriel James Goenawan
Henan Wang
Huaiyuan Qin
Chenghao Xu
Yanhua Yang
Fen Fang
Ying Sun

Despite being trained on balanced datasets, existing AI-generated image detectors often exhibit systematic bias at test time, frequently misclassifying fake images as real. We hypothesize that this behavior stems from distributional shift in fake samples and implicit priors learned during training. Specifically, models tend to overfit to superficial artifacts that do not generalize well across different generation methods, leading to a misaligned decision threshold when faced with test-time distribution shift. To address this, we propose a theoretically grounded post-hoc calibration framework based on Bayesian decision theory. In particular, we introduce a learnable scalar correction to the model’s logits, optimized on a small validation set from the target distribution while keeping the backbone frozen. This parametric adjustment compensates for distributional shift in model output, realigning the decision boundary even without requiring ground-truth labels. Experiments on challenging benchmarks show that our approach significantly improves robustness without retraining, offering a lightweight and principled solution for reliable and adaptive AI-generated image detection in the open world.

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NeurIPS Conference 2021 Conference Paper

Predicting Event Memorability from Contextual Visual Semantics

Qianli Xu
Fen Fang
Ana Molino
Vigneshwaran Subbaraju
Joo-Hwee Lim

Episodic event memory is a key component of human cognition. Predicting event memorability, i. e. , to what extent an event is recalled, is a tough challenge in memory research and has profound implications for artificial intelligence. In this study, we investigate factors that affect event memorability according to a cued recall process. Specifically, we explore whether event memorability is contingent on the event context, as well as the intrinsic visual attributes of image cues. We design a novel experiment protocol and conduct a large-scale experiment with 47 elder subjects over 3 months. Subjects’ memory of life events is tested in a cued recall process. Using advanced visual analytics methods, we build a first-of-its-kind event memorability dataset (called R3) with rich information about event context and visual semantic features. Furthermore, we propose a contextual event memory network (CEMNet) that tackles multi-modal input to predict item-wise event memorability, which outperforms competitive benchmarks. The findings inform deeper understanding of episodic event memory, and open up a new avenue for prediction of human episodic memory. Source code is available at https: //github. com/ffzzy840304/Predicting-Event-Memorability.

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AAAI Conference 2021 System Paper

TAILOR: Teaching with Active and Incremental Learning for Object Registration

Qianli Xu
Nicolas Gauthier
Wenyu Liang
Fen Fang
Hui Li Tan
Ying Sun
Yan Wu
Liyuan Li

When deploying a robot to a new task, one often has to train it to detect novel objects, which is time-consuming and laborintensive. We present TAILOR - a method and system for object registration with active and incremental learning. When instructed by a human teacher to register an object, TAILOR is able to automatically select viewpoints to capture informative images by actively exploring viewpoints, and employs a fast incremental learning algorithm to learn new objects without potential forgetting of previously learned objects. We demonstrate the effectiveness of our method with a KUKA robot to learn novel objects used in a real-world gearbox assembly task through natural interactions.

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ICRA Conference 2021 Conference Paper

Towards Efficient Multiview Object Detection with Adaptive Action Prediction

Qianli Xu
Fen Fang
Nicolas Gauthier
Wenyu Liang
Yan Wu 0002
Liyuan Li
Joo Hwee Lim

Active vision is a desirable perceptual feature for robots. Existing approaches usually make strong assumptions about the task and environment, thus are less robust and efficient. This study proposes an adaptive view planning approach to boost the efficiency and robustness of active object detection. We formulate the multi-object detection task as an active multiview object detection problem given the initial location of the objects. Next, we propose a novel adaptive action prediction (A2P) method built on a deep Q-learning network with a dueling architecture. The A2P method is able to perform view planning based on visual information of multiple objects; and adjust action ranges according to the task status. Evaluated on the AVD dataset, A2P leads to 21. 9% increase in detection accuracy in unfamiliar environments, while improving efficiency by 22. 7%. On the T-LESS dataset, multi-object detection boosts efficiency by more than 30% while achieving equivalent detection accuracy.

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