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Xinrui Jiang

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4 papers

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NeurIPS Conference 2025 Conference Paper

4KAgent: Agentic Any Image to 4K Super-Resolution

Yushen Zuo
Qi Zheng
Mingyang Wu
Xinrui Jiang
Renjie Li
Jian Wang
Yide Zhang
Gengchen Mai

We present 4KAgent, a unified agentic super-resolution generalist system designed to universally upscale any image to 4K resolution (and even higher, if applied iteratively). Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs at $256\times 256$, into crystal-clear, photorealistic 4K outputs. 4KAgent comprises three core components: (1) Profiling, a module that customizes the 4KAgent pipeline based on bespoke use cases; (2) A Perception Agent, which leverages vision-language models alongside image quality assessment experts to analyze the input image and make a tailored restoration plan; and (3) A Restoration Agent, which executes the plan, following a recursive execution-reflection paradigm, guided by a quality-driven mixture-of-experts policy to select the optimal output for each step. Additionally, 4KAgent embeds a specialized face restoration pipeline, significantly enhancing facial details in portrait and selfie photos. We rigorously evaluate our 4KAgent across 11 distinct task categories encompassing a total of 26 diverse benchmarks, setting new state-of-the-art on a broad spectrum of imaging domains. Our evaluations cover natural images, portrait photos, AI-generated content, satellite imagery, fluorescence microscopy, and medical imaging like fundoscopy, ultrasound, and X-ray, demonstrating superior performance in terms of both perceptual (e. g. , NIQE, MUSIQ) and fidelity (e. g. , PSNR) metrics. By establishing a novel agentic paradigm for low-level vision tasks, we aim to catalyze broader interest and innovation within vision-centric autonomous agents across diverse research communities. We release all the code, models, and results at: https: //4kagent. github. io.

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NeurIPS Conference 2025 Conference Paper

UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systems

Tingzhu Bi
Yicheng Pan
Xinrui Jiang
Huize Sun
Meng Ma
Ping Wang

Uncovering cause-effect relationships from observational time series is fundamental to understanding complex systems. While many methods infer static causal graphs, real-world systems often exhibit dynamic causality —where relationships evolve over time. Accurately capturing these temporal dynamics requires time-resolved causal graphs. We propose UnCLe, a novel deep learning method for scalable dynamic causal discovery. UnCLe employs a pair of Uncoupler and Recoupler networks to disentangle input time series into semantic representations and learns inter-variable dependencies via auto-regressive Dependency Matrices. It estimates dynamic causal influences by analyzing datapoint-wise prediction errors induced by temporal perturbations. Extensive experiments demonstrate that UnCLe not only outperforms state-of-the-art baselines on static causal discovery benchmarks but, more importantly, exhibits a unique capability to accurately capture and represent evolving temporal causality in both synthetic and real-world dynamic systems (e. g. , human motion). UnCLe offers a promising approach for revealing the underlying, time-varying mechanisms of complex phenomena.

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AAAI Conference 2021 Conference Paper

Training Binary Neural Network without Batch Normalization for Image Super-Resolution

Xinrui Jiang
Nannan Wang
Jingwei Xin
Keyu Li
Xi Yang
Xinbo Gao

Recently, binary neural network (BNN) based superresolution (SR) methods have enjoyed initial success in the SR field. However, there is a noticeable performance gap between the binarized model and the full-precision one. Furthermore, the batch normalization (BN) in binary SR networks introduces floating-point calculations, which is unfriendly to low-precision hardwares. Therefore, there is still room for improvement in terms of model performance and efficiency. Focusing on this issue, in this paper, we first explore a novel binary training mechanism based on the feature distribution, allowing us to replace all BN layers with a simple training method. Then, we construct a strong baseline by combining the highlights of recent binarization methods, which already surpasses the state-of-the-arts. Next, to train highly accurate binarized SR model, we also develop a lightweight network architecture and a multi-stage knowledge distillation strategy to enhance the model representation ability. Extensive experiments demonstrate that the proposed method not only presents advantages of lower computation as compared to conventional floating-point networks but outperforms the state-of-the-art binary methods on the standard SR networks.

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AAAI Conference 2020 Conference Paper

Facial Attribute Capsules for Noise Face Super Resolution

Jingwei Xin
Nannan Wang
Xinrui Jiang
Jie Li
Xinbo Gao
Zhifeng Li

Existing face super-resolution (SR) methods mainly assume the input image to be noise-free. Their performance degrades drastically when applied to real-world scenarios where the input image is always contaminated by noise. In this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with the problem of high-scale super-resolution of noisy face image. Capsule is a group of neurons whose activity vector models different properties of the same entity. Inspired by the concept of capsule, we propose an integrated representation model of facial information, which named Facial Attribute Capsule (FAC). In the SR processing, we ﬁrst generated a group of FACs from the input LR face, and then reconstructed the HR face from this group of FACs. Aiming to effectively improve the robustness of FAC to noise, we generate FAC in semantic, probabilistic and facial attributes manners by means of integrated learning strategy. Each FAC can be divided into two sub-capsules: Semantic Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial attribute in detail from two aspects of semantic representation and probability distribution. The group of FACs model an image as a combination of facial attribute information in the semantic space and probabilistic space by an attributedisentangling way. The diverse FACs could better combine the face prior information to generate the face images with ﬁne-grained semantic attributes. Extensive benchmark experiments show that our method achieves superior hallucination results and outperforms state-of-the-art for very low resolution (LR) noise face image super resolution.

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