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Jin Tang

Publications and source records attributed to Jin Tang.

At least 19 recordsLinked to original sources
arXiv2026

MAC-RRG: Iterative Multi-Agent Collaboration for X-ray Radiology Report Generation

Despite the remarkable progress of LLM-based and knowledge graph-augmented Radiology Report Generation (RRG) methods, existing techniques still suffer from inherent defects. Conventional LLM-only models lack structured medical prior knowledge, resulting in frequent medical hallucinations and low diagnostic interpretability. Current knowledge graph-enhanced schemes adopt static one-round knowledge fusion with single-source knowledge, incapable of dynamic knowledge updating according to generation feedback. This paper proposes a novel Multi-Agent Collaborative iterative framework for X-ray Radiology Report Generation, termed MAC-RRG. Inspired by multi-agent technology, our framework constructs a closed-loop optimization paradigm based on task decoupling and collaborative reasoning. Specifically, the framework first generates a preliminary radiology report from input X-ray images via a vision encoder and a basic LLM. Subsequently, a multimodal knowledge graph (MM-KG) agent mines structured disease correlation and anatomical knowledge from medical knowledge graphs, while an auxiliary knowledge agent extracts unstructured domain knowledge from public medical databases. The multi-source knowledge acquired by dual agents is fused and embedded to guide the LLM in iteratively refining the initial report. Extensive quantitative and qualitative experiments on mainstream X-ray RRG datasets, including IU X-ray, MIMIC, and CheXpert Plus, fully verify the superiority of our proposed method. The source code and pre-trained models have been released on https://github.com/Event-AHU/Medical_Image_Analysis

cs.AI↗
arXiv2026

MGRL-RSCC: Multi-Granularity Reward Reinforcement Learning for Fine-Grained Remote Sensing Change Captioning

Remote Sensing Change Captioning (RSCC), which aims to generate accurate and detailed linguistic descriptions of ground object variations from bi-temporal remote sensing images, is a critical and challenging task in intelligent remote sensing interpretation. The mainstream autoregressive training paradigm faces severe exposure bias and train-test distribution mismatch, resulting in cumulative generation errors. They tend to produce conservative and template-fixed captions while ignoring subtle scene change details. To address these challenges, this paper proposes a novel multi-granularity reward reinforcement learning paradigm, termed MGRL-RSCC. Specifically, we first leverage a CNN and hierarchical self-attention module to extract and enhance visual features from bi-temporal remote sensing images. A Transformer decoder is then utilized to complete visual-to-linguistic translation. Different from existing methods, we design a dual-decoding strategy and a two-stage joint optimization scheme, which combines token-level supervised learning via greedy decoding and multi-granularity reward-driven self-critical reinforcement learning via sampling decoding. We further construct three complementary reward functions covering linguistic fluency, change state consistency, and structural-semantic relevance to comprehensively optimize caption quality and alleviate false and missing change descriptions. Extensive experiments on multiple public RSCC benchmark datasets demonstrate that the proposed MGRL-RSCC effectively mitigates exposure bias and conservative generation problems in traditional autoregressive methods. The source code and pre-trained models will be released on https://github.com/Event-AHU/MGRL-RSCC

cs.CV↗
arXiv2026

SafeDivertor: Faithful Divertor Heat Flux Reconstruction from Macroscopic Plasma State Signals via Time-Frequency Prior Exploitation

Divertor heat-flux analysis is essential for understanding plasma-wall interactions and protecting plasma-facing components in magnetic-confinement fusion devices, while conventional infrared-based inversion is usually performed after discharge and requires heat-conduction modeling with device-specific material properties, divertor geometry, and boundary conditions. Rather than accelerating this conventional infrared-based inversion paradigm, we introduce a new online-oriented signal-based reconstruction paradigm that directly reconstructs time-resolved radial heat-flux profiles from multi-source macroscopic plasma-state signals available during discharge. To enable systematic study of this task, we construct \textbf{DivMPS2HF}, a multi-source discharge dataset that provides the data foundation and benchmark for signal-based divertor heat-flux reconstruction. We further propose \textbf{SafeDivertor}, a task-driven framework designed to address the key challenges of signal-based heat-flux reconstruction. It employs physical prior-aware initialization to provide radial-distribution guidance for target channels, input perturbation to reduce over-reliance on specific heterogeneous signals, spectral-aware reconstruction optimization to exploit time-frequency priors and preserve transient dynamics, and progressive training to stabilize the optimization of these complementary objectives. Experiments on DivMPS2HF demonstrate that SafeDivertor achieves the best overall performance among the evaluated time-series baselines across all five metrics, establishing a new performance benchmark for signal-based divertor heat-flux reconstruction. The source code will be released on https://github.com/Event-AHU/OpenFusion

physics.plasm-ph↗
arXiv2026

FusionMMT: A Unified Multimodal and Multitask Learning Framework for Nuclear Fusion

With the growing global demand for energy, nuclear fusion has emerged as a promising direction for future clean energy. Tokamaks represent one of the leading approaches to magnetic-confinement fusion. Achieving high-performance, long-pulse, and steady-state operation requires effective diagnosis of plasma states. However, existing intelligent diagnostic methods are largely limited to either multimodal single-task or unimodal multitask learning, while a unified multimodal multitask learning framework remains underexplored. To address this gap, we construct EAST-VTD640, a multimodal multitask dataset that integrates vision and time-series diagnostics from 640 EAST shots for disruption prediction, edge-localized mode (ELM) recognition, and H98 regression. On this basis, we present FusionMMT, the first unified multimodal multitask framework for intelligent tokamak plasma diagnostics. FusionMMT employs multi-scale, time-aware, and variable-aware modeling to handle heterogeneous sampling rates and the high computational cost of high-frequency sequences. It further combines task-adaptive multimodal fusion with progressive multitask optimization to learn shared and task-specific representations while mitigating cross-task conflicts and optimization imbalance. Extensive experiments on EAST-VTD640 show that FusionMMT outperforms representative multimodal multitask methods across disruption prediction, ELM recognition, and H98 regression. The source code will be released on https://github.com/Event-AHU/OpenFusion

cs.AI↗
arXiv2026

Physics-Informed Neural Networks for Complex Eigenfrequency Identification and Mode Structure Reconstruction of the Ground-State ITG Branch

Physics-informed neural networks (PINNs) combine sparse observations with physical equations, providing an important approach for modeling complex plasma processes and inferring unknown physical quantities. The steep-gradient pedestal of high-confinement-mode tokamaks is closely linked to plasma confinement and edge transport. Analyzing ion-temperature-gradient (ITG) drift waves in this region requires jointly identifying complex eigenfrequencies and reconstructing two-dimensional complex-valued mode fields. Localized high-frequency oscillations, strong real-imaginary coupling, and nonlinear coupling between the mode field and eigenfrequency challenge PINN representation and joint optimization. To address these challenges, we propose a physics-informed neural framework combining Fourier feature encoding, complex-valued feature propagation, and three-stage training. Under sparse observations and physical constraints, it jointly solves for the complex eigenfrequency and mode field of a representative ground-state ITG branch. Experiments show that the framework accurately recovers the target complex eigenfrequency and two-dimensional complex-valued mode field and outperforms representative PINN baselines. It also provides a basis for analyzing higher-order and multiple-branch drift-wave modes.

cs.AI↗
arXiv2026

Spatio-Temporal Conditional Denoising Transformer for Modality-Missing RGBT Tracking

Missing modalities in RGBT tracking often lead to incomplete and unstable multimodal feature representations that greatly degrade the performance. Existing methods typically attempt to recover missing modalities from available ones, but the quality of data generated in challenging scenarios might be unsatisfactory. In addition, current approaches exhibit limited flexibility in processing both missing and complete data. To overcome these limitations, we propose a Spatio-temporal Conditional Denoising Transformer (SCDT), which integrates the spatial cues and the temporal context to adaptively perform information reconstruction of missing modalities and feature enhancement of weak modalities in a unified framework, for robust modality-missing RGBT tracking. In particular, SCDT leverages the short-term temporal cues from recent historical frames to capture the fine-grained temporal correlations and the long-term temporal cues encoding modality evolution to capture the global context. By jointly exploiting long short-term temporal contexts as the conditions, SCDT progressively guides noisy features of available modalities to learn reliable and temporally consistent multimodal representations. Furthermore, SCDT introduces a noisemodulated adaptation mechanism that dynamically adjusts its behavior according to the modal availability, enabling a single framework to unify feature learning under both modality-missing and complete scenarios without changing the architecture or parameters. Extensive experiments on three public benchmark datasets demonstrate that our method consistently outperforms state-of-the-art methods. The code is available here.

cs.CV↗
arXiv2026

Ferrimagnetic Skyrmions in a Tetragonal Mn1.9Co0.1Sb Single Crystal at Room Temperature

The development of room temperature small-sized ferrimagnetic skyrmion materials is significant for topological spintronic device applications. As a room temperature ferrimagnetic material, the tetragonal Mn1.9Co0.1Sb crystal exhibits multiple phase transitions, including spin reorientation transitions. However, the magnetic spin textures and their evolution mechanisms during magnetic phase transitions in Mn1.9Co0.1Sb crystals remain unexplored. Using Lorentz transmission electron microscopy, we discovered and verified dipolar skyrmion behavior and its magnetic evolution at room temperature. We established a stable phase diagram of magnetic textures as functions of temperature and magnetic field, while also investigating the evolution mechanisms of spin textures across multiple temperature-induced magnetic phase transitions. Through micromagnetic simulations, a ferrimagnetic configuration with in-plane ferromagnetic coupling and interlayer antiferromagnetic arrangement was established, which stands in contrast to synthetic ferrimagnetic/antiferromagnetic systems that exhibit interlayer antiferromagnetic coupling via the Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction. We determined that the intrinsic frequency of ferrimagnetic skyrmions can reach the THz regime due to strong interlayer antiparallel exchange interactions. These findings highlight the diversity of room temperature ferrimagnetic skyrmion regulation behaviors in Mn1.9Co0.1Sb and their dynamic evolution characteristics, opening new avenues for developing novel spintronic devices with enhanced functionalities capable of operating under ambient conditions.

cond-mat.mtrl-sci↗
arXiv2026

Enhanced Curie temperature and room-temperature 50-nm skyrmions achieved in hexagonal ferromagnet Mn5Ge3+x synthesized via a high-pressure method

The development of new high-temperature ultrasmall-size skyrmion materials holds immense significance for the promising applications of topological spintronic devices. In this study, we demonstrate that a high-pressure synthesis technique can significantly elevate the Curie temperature of Mn5Ge3+x crystals, from 294 K to 350 K. This enhancement is attributed to the combined effects of lattice contraction and increased Ge content, the conclusion supported by Density Functional Theory calculations. Additionally, our real-space magnetic imaging reveals the stability of dipolar skyrmions with diameters of approximately 50 nm at room temperature. Our micromagnetic simulations closely replicate the diverse experimental topological magnetic textures observed. Furthermore, magnetotransport measurements indicate the potential for the electrical distinction between various topological magnetic textures in skyrmion-based devices. We also report deterministic manipulations on single dipolar skyrmions in confined nanostructures by using in-plane currents. The observation, electrical manipulation, and electrical detection of room-temperature ultrasmall topological magnetic textures underscore the potential of Mn5Ge3+x as a promising platform for spintronic device applications.

cond-mat.mtrl-sci↗
arXiv2026

Current-induced creation and dynamics of embedded magnetic skyrmion bags

Magnetic skyrmion bags-vortex-like structures hosting multiple skyrmions with tunable topological charge (Q)-hold significant promise for next-generation spintronic computing. However, while their creation using magnetic fields has been demonstrated, their direct electrical generation remains an outstanding challenge. Here, we report the direct current-induced formation and manipulation of embedded skyrmion bags in a FeGe nanoplate under zero magnetic field. Using in-situ Lorentz transmission electron microscopy, we capture the transformation of a distorted helical ground state into embedded skyrmion bags with diverse configurations, driven by nanosecond current pulses. Theoretical analysis indicates that this process is driven by the spin-transfer-torque-induced fracture of the helical state. Furthermore, we demonstrate electrically-induced transitions between skyrmion bags of different Q, leading to the stabilization of complex three-dimensional topological structures, including experimental signatures of magnetic monopoles and bobbers. Our work establishes a foundation for all-electrical control of high-Q topological spin textures and topological defects, paving the way for their application in functional spintronic devices.

cond-mat.mes-hall↗
arXiv2026

DRGBT-1K: A Large-scale High-quality Benchmark for Dynamic RGBT Tracking

Dynamic RGBT (DRGBT) tracking aims to continuously localize a target when the available sensing modalities and observation platforms vary over time. Compared with conventional RGBT tracking with fixed RGBT inputs and a fixed observation platform, DRGBT tracking is more consistent with real-world collaborative perception systems, where targets may be observed by heterogeneous sensors from different viewpoints. However, existing benchmarks are still insufficient for systematically evaluating tracker robustness under real dynamic modality variations and cross-platform transitions. To address this limitation, we make the following contributions. 1) We construct DRGBT-1K, a large-scale high-quality benchmark for DRGBT tracking. It contains 1,045 sequences captured entirely in real-world scenarios and 795K RGBT frame pairs collected using UAVs and handheld RGBT devices, encompassing diverse real-world scenes, pronounced viewpoint changes, modality variations, and target appearance discontinuities. 2) We provide comprehensive annotations for fine-grained evaluation, including dense bounding boxes, target category labels, challenge attributes, frame-level modality labels and platform labels. DRGBT-1K covers 24 target categories, more than 15 scene types and 15 challenge attributes. 3) We establish a comprehensive benchmark by evaluating 20 representative multimodal tracking methods, including conventional RGBT trackers, modality-missing RGBT trackers, and DRGBT trackers under a unified evaluation protocol. 4) We release an unaligned version of DRGBT-1K and derive UGVT-1K to support broader research on unaligned multimodal tracking and UAV-ground collaborative tracking. 5) We develop an online evaluation platform for DRGBT-1K and provide a leaderboard that collects all methods evaluated on this benchmark.

cs.CV↗
arXiv2026

Cross-Modal UAV Object Tracking: State-Aware Representation Learning and A Unified Benchmark

Unmanned Aerial Vehicle (UAV) object tracking has emerged as a popular research field with broad practical applications. Modern UAVs are increasingly equipped with both visible light and thermal infrared sensors. However, due to constraints in communication bandwidth, computational resources and power consumption, current systems often activate one modality and switch between modalities to maintain robust tracking in complex scenarios. Such modality switch inevitably leads to significant appearance change and sudden spatial shift, posing great challenges for existing tracking algorithms. To handle this problem, we propose a novel State-Aware Representation Learning Approach called SARLA, which perceives the inconsistent modality states of current frame with template and last frame in the target representations to adapt to the sudden changes in both appearance and position, for robust cross-modal object tracking. In particular, we propose the Modality State Aware Representation Module (MSARM) and Spatial State Aware Representation Module (SSARM). MSARM guides the model to learn appearance correlation, bridging the modality gap, while SSARM models cross-frame spatial correlation to mitigate sudden spatial shift impacts. In addition, we design a spatial shift prediction loss to further handle the effects of spatial variation caused by modality switch. To promote the development of this research field, we establish a large-scale video benchmark called CM-UOT, which consists of 1079 cross-modal sequences with an average video length greater than 621 frames and encompasses over 671K frames in total. Extensive experiments on CM-UOT dataset demonstrate the superior performance of the proposed SARLA against 20 excellent tracking methods. The source code, datasets, and evaluation protocols associated with this work are publicly available at: https://github.com/hongsmile365/sarla-.

cs.CV↗
arXiv2026

Visible-Light Imaging Diagnosis of Neutral Particle Emission Tomography in the Tokamak Divertor: An Efficient Transformer-based Surrogate Model

Nuclear fusion has made significant progress in recent years and is expected to become one of the most important pathways to addressing global energy challenges. This paper focuses on observing plasma using visible-light cameras, analyzing its spatio-temporal motion cues, and predicting the two-dimensional spatial distribution of light intensity, aiming to provide a foundational basis for future scientific experiments using deep neural networks. Specifically, we propose Delta-InvFormer, a novel backbone network centered on a differential Transformer. The key insight is that by taking consecutive video frames as input, we can better capture the dynamics of the plasma. Moreover, spatial and temporal differential self-attention effectively mitigates interference from noisy signals, ensuring high-quality feature extraction. These features are then fused into a compact and informative representation, which is fed into a decoder network to predict the distribution. Based on real experimental data collected from the Experimental Advanced Superconducting Tokamak (EAST) large-scale scientific facility, our results demonstrate that the proposed model not only significantly accelerates traditional methods for distribution prediction but also achieves competitive reconstruction accuracy. The source code of this paper will be released on https://github.com/Event-AHU/OpenFusion

cs.CV↗
arXiv2026

MQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning

Large-scale Vision-Language Models have demonstrated impressive transfer learning capabilities across a wide range of tasks. For few-shot classification, we observe that VLMs exhibit a notable ability to filter candidate categories and thus achieve high Top-K accuracy. However, they often struggle with fine-grained discrimination among visually similar categories, resulting in unsatisfactory Top-1 performance, as shown in Figure 1. Existing studies on VLM adapters generally focus on global alignment between visual and textual representations in the feature space, but fail to exploit semantically similar categories to refine fine-grained visual representations. Based on these observations, we propose a novel coarse-to-fine VLM fine-tuning approach for few-shot learning that leverages quantum computation, termed the Multi-Modal Quantum Adapter (MQAdapter). Specifically, MQAdapter first retrieves the Top-K category candidates most similar to the input image and uses them as semantic anchors. It then employs a cross-modal quantum learning mechanism to refine visual features under the guidance of these anchors. The core of this mechanism is the encoding of visual and textual features into quantum states. By leveraging quantum entanglement and superposition in a high-dimensional Hilbert space, MQAdapter effectively models higher-order cross-modal interactions, producing more discriminative representations than traditional Euclidean adapters. MQAdapter is parameter-efficient and can be integrated with various existing fine-tuning algorithms to achieve further performance gains. Evaluations on 15 datasets demonstrate the effectiveness of MQAdapter while requiring fewer trainable parameters.

cs.CV↗
arXiv2026

Mixture of Enhanced-View Experts for Multi-Query Vehicle ReID and A Large-Scale Benchmark

Multi-query vehicle ReID aims to leverage complementary information from diverse views for robust feature learning. However, current methods suffer from simplistic feature fusion and thus easily ignores some important view information and cross-view relationships. To handle these problems, this work presents a novel approach called Mixture of Enhanced-View Experts (EV-MoE), which enhances the feature representation of each view and efficiently integrate the view-specific enhanced features by MoE, for robust multi-query ReID. In particular, we design a mixture of enhanced-view experts module, which consists of two parts including view-specific feature enhancement sub-Module (VFEM) and dynamic multi-view fusion sub-Module (DMFM). Moreover, we further introduce Multi-view Alignment Loss (MAL), which aligns features through bidirectional crossview contrastive learning and reconstruction constraints, addressing the challenges of consistency between multi-query features and single-image features. In addition, to evaluate multi-query ReID in real-world environments, we collect LCRI-1K, a largescale vehicle ReID dataset with 1,090 identities, 107,805 images, across 23,637 cameras, where each vehicle appears in an average of 67.5 cameras, providing a comprehensive benchmark to test the robustness in complex environments. Extensive experiments demonstrate the robustness of CAFNet in addressing the multiquery vehicle ReID problem. The code is available at https: //github.com/xiaozhen28/CAFNet.

cs.CV↗
arXiv2026

ASFR-Net: Adversarial Alignment and Spatio-Frequency Refinement Network for Heterogeneous Remote Sensing Image Change Detection

The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significant modal disparities caused by distinct imaging mechanisms. These intrinsic inconsistencies are prone to introducing pseudo-changes, thereby constraining detection accuracy. To address this, we propose a novel, end-to-end adversarial spatio-frequency refinement network (ASFR-Net). Initially, a modality-invariant representation learner (MIR-Learner) guides the backbone to extract modality-invariant features, effectively bridging the primary domain gap. Subsequently, to address persistent residual modal differences, we design an innovative spatio-frequency synergistic enhancement module (SFEM), which identifies and suppresses sensor-specific noise and artifacts that are difficult to discern in the spatial domain by leveraging frequency-domain processing. Multi-level difference features are then computed from these refined representations and fed into a decoder equipped with cascaded hierarchical guided fusion module (HGFM) blocks to generate precise change maps. To alleviate the data scarcity in heterogeneous tasks, we construct and release a new high-resolution benchmark specifically focused on building changes: the visible-near-infrared heterogeneous change detection (VisNIR-HCD) dataset. It presents unique scientific challenges arising from deceptive visual similarity and non-linear spectral inversions, providing a robust platform for evaluating model generalization. Extensive experiments on VisNIR-HCD and public datasets demonstrate that ASFR-Net achieves state-of-the-art (SOTA) performance, significantly outperforming existing methods. The source code and the VisNIR-HCD dataset are publicly available at https://github.com/LuoYang2024/ASFR-Net.

cs.CV↗
arXiv2026

Hierarchical Multi-to-Single-Modal Knowledge Distillation for Disruption Prediction in EAST

Plasma disruption is a critical threat to tokamak safety. Existing data-driven predictors mainly rely on time-series diagnostic signals, while visible images provide complementary spatial cues including plasma deformation, local brightening, and radiation-structure evolution. Although the image modality improves the model's discriminative capability, it also substantially increases the computational cost during inference. To address this issue, we propose a hierarchical multi-to-single-modal knowledge distillation framework for disruption prediction on a synchronized EAST multimodal dataset. During training, visible images and time-series signals are used to train a multimodal teacher, which learns disruption precursor representations through Transformer-based encoders and a prototype-guided spatiotemporal hypergraph module. During inference, only the time-series student is retained, with multimodal knowledge transferred through graph-structure-level, representation-level, and decision-level distillation. On the 640-discharge EAST dataset, the results demonstrate that the proposed framework can preserve the discriminative advantages of multimodal learning while substantially reducing inference cost, and providing an effective route for efficient disruption prediction in EAST. The source code of this paper will be released on https://github.com/Event-AHU/OpenFusion.

cs.CV↗
arXiv2026

Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer

Accurate modeling of the divertor temperature field is essential for preventing material melting and damage and for extending the service life of fusion devices. However, conventional numerical methods, such as the Finite Element Method (FEM), are computationally expensive and therefore unsuitable for real-time applications. Therefore, a fast and generalizable method is required for real-time reconstruction of the divertor temperature field and subsequent real-time control. To address the above issue, we propose a Physics-aware Neural Operator Transformer (PNOT) to characterize the spatiotemporal evolution of the divertor temperature field. It models boundary heat-flux relations as a structured graph and employs graph attention to explicitly capture spatial physical dependencies. Inspired by physics-aware attention, we further develop a physics-aware neural operator module to aggregate query points with similar physical conditions via slicing and model heat diffusion, while a gradient-constrained Sobolev regularization loss enforces consistency between function values and their derivatives. Experimental results show that these physical constraints improve prediction accuracy while preserving physical consistency. The source code of this paper will be released on https://github.com/Event-AHU/OpenFusion

cs.CV↗
arXiv2026

Dynamic Parsing and Updating Natural Language Specification using VLMs for Robust Vision-Language Tracking

Vision-language tracking guided by natural language specifications leverages high-level semantic cues of target objects to substantially boost tracking accuracy and robustness. Existing studies have verified that adaptively optimizing textual descriptions throughout the tracking process can effectively mitigate the semantic-visual mismatch induced by dynamic variations in target appearance, position, and other inherent attributes. Nevertheless, mainstream methods that directly generate textual information via sequence models or large language models inevitably suffer from inherent defects, including erroneous target updating, excessive background distraction, and pervasive hallucination artifacts. To address the aforementioned limitations, this paper proposes a novel language dependency parsing mechanism to precisely distill core tracking principal components, encompassing target objects, semantic concepts, and background contextual information. On this basis, we perform component-aware adaptive textual description updates by exploiting the powerful cross-modal understanding capability of the pre-trained vision-language model Qwen-VL. By integrating the proposed elaborately designed modules into the baseline framework, our method achieves consistent and superior tracking performance on multiple large-scale vision-language tracking benchmarks, including TNL2K, LaSOT, TNLLT, and OTB-LANG. The source code and pre-trained models will be released at https://github.com/Event-AHU/Open_VLTrack.

cs.CV↗