arXiv subjects

Xinjie Li

Publications and source records attributed to Xinjie Li.

18 recordsLinked to original sources

arXiv2026

Low-Overhead Receiver Design for Data-Dependent Superimposed Training via Deep Learning

Superimposed pilot (SIP) transmission improves spectral efficiency by eliminating the dedicated pilot overhead required in orthogonal pilot (OP)-based schemes. However, SIP suffers from severe pilot-data coupling, which leads to a critical performance-complexity bottleneck at the receiver. To address this issue, this paper proposes a low-overhead transmission framework that revitalizes data-dependent superimposed training (DDST) with enhanced interference mitigation strategies. First, for quasi-static block-fading channels, an enhanced DDST receiver is developed to achieve non-iterative pilot-data decoupling by exploiting data-dependent algebraic structures. Second, to overcome the sensitivity of conventional DDST to channel variations and symbol misidentification in fast time-varying environments, a mix transmission scheme is developed. By strategically applying DDST to a subset of resource elements, the proposed scheme combines the interference-free transmission property of OP with the zero-pilot-overhead advantage of SIP, thereby improving demapping reliability and interference suppression. Furthermore, under the proposed mix scheme, a Vision Transformer-based neural receiver is designed to capture the orthogonal structure between pilots and perturbation-bearing data, as well as the underlying channel correlations, thereby relaxing the stringent quasi-static assumption required for interference disentanglement. Simulation results demonstrate that the proposed framework achieves significant performance gains in the low-to-medium SNR regime under time-varying channels while providing superior computational efficiency compared with state-of-the-art SIP receivers.

cs.IT↗

arXiv2026

Modeling Inverse Ellipsometry Problem via Flow Matching with a Large-Scale Dataset

Inverse ellipsometry, i.e., reconstructing optical constants and film thickness from the measured phase difference $Δ$ and amplitude ratio $Ψ$, is a fundamentally ill-posed problem. Traditional solutions rely on slow, expert-driven iterative fitting, while the development of machine learning approaches has been severely limited by the lack of large-scale, physically consistent datasets. To address this gap, we introduce \textbf{EllipBench}, a comprehensive benchmark comprising over 8 million high-precision samples spanning 98 thin-film materials and 5 substrates. Building upon this benchmark, we conduct a systematic evaluation of a broad spectrum of methods, including traditional machine learning models, deep neural networks, and Physics-Informed Neural Networks, and show that existing paradigms consistently struggle to fully resolve the inverse ellipsometry task. To better capture its inherent ambiguity, we further propose a novel \textbf{Decoupled Conditional Flow Matching (DCFM)} framework. Rather than formulating the problem as deterministic point-to-point regression, DCFM explicitly decouples geometric film thickness and incorporates it as a robust physical condition to guide a continuous vector field for modeling the inverse probability distribution of wavelength-dependent optical constants. Combined with a gradient detachment strategy and physics-based constraints, our joint architecture effectively mitigates intrinsic physical ambiguities and delivers a robust and accurate solution for inverse ellipsometry.

cs.LG↗

arXiv2025

Membership Inference Attack with Partial Features

Machine learning models are vulnerable to membership inference attack, which can be used to determine whether a given sample appears in the training data. Most existing methods assume the attacker has full access to the features of the target sample. This assumption, however, does not hold in many real-world scenarios where only partial features are available, thereby limiting the applicability of these methods. In this work, we introduce Partial Feature Membership Inference (PFMI), a scenario where the adversary observes only partial features of each sample and aims to infer whether this observed subset was present in the training set. To address this problem, we propose MRAD (Memory-guided Reconstruction and Anomaly Detection), a two-stage attack framework that works in both white-box and black-box settings. In the first stage, MRAD leverages the latent memory of the target model to reconstruct the unknown features of the sample. We observe that when the known features are absent from the training set, the reconstructed sample deviates significantly from the true data distribution. Consequently, in the second stage, we use anomaly detection algorithms to measure the deviation between the reconstructed sample and the training data distribution, thereby determining whether the known features belong to a member of the training set. Empirical results demonstrate that MRAD is effective across various datasets, and maintains compatibility with off-the-shelf anomaly detection techniques. For example, on STL-10, our attack exceeds an AUC of around 0.75 even with 60% of the missing features.

cs.LG↗

arXiv2025

What Happens Next? Next Scene Prediction with a Unified Video Model

Recent unified models for joint understanding and generation have significantly advanced visual generation capabilities. However, their focus on conventional tasks like text-to-video generation has left the temporal reasoning potential of unified models largely underexplored. To address this gap, we introduce Next Scene Prediction (NSP), a new task that pushes unified video models toward temporal and causal reasoning. Unlike text-to-video generation, NSP requires predicting plausible futures from preceding context, demanding deeper understanding and reasoning. To tackle this task, we propose a unified framework combining Qwen-VL for comprehension and LTX for synthesis, bridged by a latent query embedding and a connector module. This model is trained in three stages on our newly curated, large-scale NSP dataset: text-to-video pre-training, supervised fine-tuning, and reinforcement learning (via GRPO) with our proposed causal consistency reward. Experiments demonstrate our model achieves state-of-the-art performance on our benchmark, advancing the capability of generalist multimodal systems to anticipate what happens next.

cs.CV↗

arXiv2025

Conditional Diffusion Model-Enabled Scenario-Specific Neural Receivers for Superimposed Pilot Schemes

Neural receivers have demonstrated strong performance in wireless communication systems. However, their effectiveness typically depends on access to large-scale, scenario-specific channel data for training, which is often difficult to obtain in practice. Recently, generative artificial intelligence (AI) models, particularly diffusion models (DMs), have emerged as effective tools for synthesizing high-dimensional data. This paper presents a scenario-specific channel generation method based on conditional DMs, which accurately model channel distributions conditioned on user location and velocity information. The generated synthetic channel data are then employed for data augmentation to improve the training of a neural receiver designed for superimposed pilot-based transmission. Experimental results show that the proposed method generates high-fidelity channel samples and significantly enhances neural receiver performance in the target scenarios, outperforming conventional data augmentation and generative adversarial network-based techniques.

cs.IT↗

arXiv2025

Light-Weight Cross-Modal Enhancement Method with Benchmark Construction for UAV-based Open-Vocabulary Object Detection

Open-Vocabulary Object Detection (OVD) faces severe performance degradation when applied to UAV imagery due to the domain gap from ground-level datasets. To address this challenge, we propose a complete UAV-oriented solution that combines both dataset construction and model innovation. First, we design a refined UAV-Label Engine, which efficiently resolves annotation redundancy, inconsistency, and ambiguity, enabling the generation of largescale UAV datasets. Based on this engine, we construct two new benchmarks: UAVDE-2M, with over 2.4M instances across 1,800+ categories, and UAVCAP-15K, providing rich image-text pairs for vision-language pretraining. Second, we introduce the Cross-Attention Gated Enhancement (CAGE) module, a lightweight dual-path fusion design that integrates cross-attention, adaptive gating, and global FiLM modulation for robust textvision alignment. By embedding CAGE into the YOLO-World-v2 framework, our method achieves significant gains in both accuracy and efficiency, notably improving zero-shot detection on VisDrone by +5.3 mAP while reducing parameters and GFLOPs, and demonstrating strong cross-domain generalization on SIMD. Extensive experiments and real-world UAV deployment confirm the effectiveness and practicality of our proposed solution for UAV-based OVD

cs.CV↗

arXiv2025

Learning-Aided Iterative Receiver for Superimposed Pilots: Design and Experimental Evaluation

The superimposed pilot transmission scheme offers substantial potential for improving spectral efficiency in MIMO-OFDM systems, but it presents significant challenges for receiver design due to pilot contamination and data interference. To address these issues, we propose an advanced iterative receiver based on joint channel estimation, detection, and decoding, which refines the receiver outputs through iterative feedback. The proposed receiver incorporates two adaptive channel estimation strategies to enhance robustness under time-varying and mismatched channel conditions. First, a variational message passing (VMP) method and its low-complexity variant (VMP-L) are introduced to perform inference without relying on time-domain correlation. Second, a deep learning (DL) based estimator is developed, featuring a convolutional neural network with a despreading module and an attention mechanism to extract and fuse relevant channel features. Extensive simulations under multi-stream and high-mobility scenarios demonstrate that the proposed receiver consistently outperforms conventional orthogonal pilot baselines in both throughput and block error rate. Moreover, over-the-air experiments validate the practical effectiveness of the proposed design. Among the methods, the DL based estimator achieves a favorable trade-off between performance and complexity, highlighting its suitability for real-world deployment in dynamic wireless environments.

cs.IT↗

arXiv2025

Partial-Wetting Phenomena in Active Matter

Abundant interfacial phenomena in nature, such as water droplets on lotus leaves and water transport in plant vessels, originate from partial-wetting phenomena, which can be well described by Young's equation. It remains an intriguing question whether similar behaviors exist in active matter. In this letter, we present a clear demonstration of the partial-wetting phenomenon in a ternary laning system, which is a typical active system. A phase diagram is constructed in which the relative drift velocities of different components govern the transitions among drying, partial wetting, and complete wetting states. The mechanical balance on the contact lines of the partial-wetting phase described by Young's equation is verified. A theoretical picture is proposed to explain the analogy of partial wetting in the laning system to that in the equilibrium system.

cond-mat.soft↗

arXiv2025

Joint Channel Estimation and Signal Detection for MIMO-OFDM: A Novel Data-Aided Approach with Reduced Computational Overhead

The acquisition of channel state information (CSI) is essential in MIMO-OFDM communication systems. Data-aided enhanced receivers, by incorporating domain knowledge, effectively mitigate performance degradation caused by imperfect CSI, particularly in dynamic wireless environments. However, existing methodologies face notable challenges: they either refine channel estimates within MIMO subsystems separately, which proves ineffective due to deviations from assumptions regarding the time-varying nature of channels, or fully exploit the time-frequency characteristics but incur significantly high computational overhead due to dimensional concatenation. To address these issues, this study introduces a novel data-aided method aimed at reducing complexity, particularly suited for fast-fading scenarios in fifth-generation (5G) and beyond networks. We derive a general form of a data-aided linear minimum mean-square error (LMMSE)-based algorithm, optimized for iterative joint channel estimation and signal detection. Additionally, we propose a computationally efficient alternative to this algorithm, which achieves comparable performance with significantly reduced complexity. Empirical evaluations reveal that our proposed algorithms outperform several state-of-the-art approaches across various MIMO-OFDM configurations, pilot sequence lengths, and in the presence of time variability. Comparative analysis with basis expansion model-based iterative receivers highlights the superiority of our algorithms in achieving an effective trade-off between accuracy and computational complexity.

cs.IT↗

arXiv2025

Sub-Clustering for Class Distance Recalculation in Long-Tailed Drug Classification

In the real world, long-tailed data distributions are prevalent, making it challenging for models to effectively learn and classify tail classes. However, we discover that in the field of drug chemistry, certain tail classes exhibit higher identifiability during training due to their unique molecular structural features, a finding that significantly contrasts with the conventional understanding that tail classes are generally difficult to identify. Existing imbalance learning methods, such as resampling and cost-sensitive reweighting, overly rely on sample quantity priors, causing models to excessively focus on tail classes at the expense of head class performance. To address this issue, we propose a novel method that breaks away from the traditional static evaluation paradigm based on sample size. Instead, we establish a dynamical inter-class separability metric using feature distances between different classes. Specifically, we employ a sub-clustering contrastive learning approach to thoroughly learn the embedding features of each class, and we dynamically compute the distances between class embeddings to capture the relative positional evolution of samples from different classes in the feature space, thereby rebalancing the weights of the classification loss function. We conducted experiments on multiple existing long-tailed drug datasets and achieved competitive results by improving the accuracy of tail classes without compromising the performance of dominant classes.

cs.LG↗

arXiv2024

Are Conditional Latent Diffusion Models Effective for Image Restoration?

Recent advancements in image restoration increasingly employ conditional latent diffusion models (CLDMs). While these models have demonstrated notable performance improvements in recent years, this work questions their suitability for IR tasks. CLDMs excel in capturing high-level semantic correlations, making them effective for tasks like text-to-image generation with spatial conditioning. However, in IR, where the goal is to enhance image perceptual quality, these models face difficulty of modeling the relationship between degraded images and ground truth images using a low-level representation. To support our claims, we compare state-of-the-art CLDMs with traditional image restoration models through extensive experiments. Results reveal that despite the scaling advantages of CLDMs, they suffer from high distortion and semantic deviation, especially in cases with minimal degradation, where traditional methods outperform them. Additionally, we perform empirical studies to examine the impact of various CLDM design elements on their restoration performance. We hope this finding inspires a reexamination of current CLDM-based IR solutions, opening up more opportunities in this field.

cs.CV↗

arXiv2024

Controlling the Latent Diffusion Model for Generative Image Shadow Removal via Residual Generation

Large-scale generative models have achieved remarkable advancements in various visual tasks, yet their application to shadow removal in images remains challenging. These models often generate diverse, realistic details without adequate focus on fidelity, failing to meet the crucial requirements of shadow removal, which necessitates precise preservation of image content. In contrast to prior approaches that aimed to regenerate shadow-free images from scratch, this paper utilizes diffusion models to generate and refine image residuals. This strategy fully uses the inherent detailed information within shadowed images, resulting in a more efficient and faithful reconstruction of shadow-free content. Additionally, to revent the accumulation of errors during the generation process, a crosstimestep self-enhancement training strategy is proposed. This strategy leverages the network itself to augment the training data, not only increasing the volume of data but also enabling the network to dynamically correct its generation trajectory, ensuring a more accurate and robust output. In addition, to address the loss of original details in the process of image encoding and decoding of large generative models, a content-preserved encoder-decoder structure is designed with a control mechanism and multi-scale skip connections to achieve high-fidelity shadow-free image reconstruction. Experimental results demonstrate that the proposed method can reproduce high-quality results based on a large latent diffusion prior and faithfully preserve the original contents in shadow regions.

cs.CV↗

arXiv2024

DiffBody: Human Body Restoration by Imagining with Generative Diffusion Prior

Human body restoration plays a vital role in various applications related to the human body. Despite recent advances in general image restoration using generative models, their performance in human body restoration remains mediocre, often resulting in foreground and background blending, over-smoothing surface textures, missing accessories, and distorted limbs. Addressing these challenges, we propose a novel approach by constructing a human body-aware diffusion model that leverages domain-specific knowledge to enhance performance. Specifically, we employ a pretrained body attention module to guide the diffusion model's focus on the foreground, addressing issues caused by blending between the subject and background. We also demonstrate the value of revisiting the language modality of the diffusion model in restoration tasks by seamlessly incorporating text prompt to improve the quality of surface texture and additional clothing and accessories details. Additionally, we introduce a diffusion sampler tailored for fine-grained human body parts, utilizing local semantic information to rectify limb distortions. Lastly, we collect a comprehensive dataset for benchmarking and advancing the field of human body restoration. Extensive experimental validation showcases the superiority of our approach, both quantitatively and qualitatively, over existing methods.

cs.CV↗

arXiv2024

CI w/o TN: Context Injection without Task Name for Procedure Planning

This paper explores the challenge of procedure planning in instructional videos, which involves creating goal-directed plans based on visual start and goal observations from videos. Previous research has tackled this problem with gradually weaker training supervision, from heavy intermediate visual observations or language instructions to task class supervision. However, with the advent of large language models, even given only the task name, these models can produce a detailed plan. In this study, we propose a much weaker setting without task name as supervision, which is not currently solvable by existing large language models since they require good prompts with sufficient information. Specifically, we hypothesize that previous intermediate supervisions can serve as context information, and we use captions of visual start and goal observations as a much cheaper form of supervision. This approach greatly reduces the labeling cost since the captions can be easily obtained by large pre-trained vision-language foundation models. Technically, we apply BLIP to generate captions as supervision to train the context feature with contrastive learning loss. Afterward, the context feature is fed into the generator to aid in plan generation. Our experiments on two datasets with varying scales demonstrate that our model can achieve comparable performance on multiple metrics, which validates our hypothesis.

cs.CV↗

arXiv2023

On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation

In recommender systems, knowledge graph (KG) can offer critical information that is lacking in the original user-item interaction graph (IG). Recent process has explored this direction and shows that contrastive learning is a promising way to integrate both. However, we observe that existing KG-enhanced recommenders struggle in balancing between the two contrastive views of IG and KG, making them sometimes even less effective than simply applying contrastive learning on IG without using KG. In this paper, we propose a new contrastive learning framework for KG-enhanced recommendation. Specifically, to make full use of the knowledge, we construct two separate contrastive views for KG and IG, and maximize their mutual information; to ease the contrastive learning on the two views, we further fuse KG information into IG in a one-direction manner.Extensive experimental results on three real-world datasets demonstrate the effectiveness and efficiency of our method, compared to the state-of-the-art. Our code is available through the anonymous link:https://figshare.com/articles/conference_contribution/SimKGCL/22783382

cs.IR↗

arXiv2021

Semantic Bilinear Pooling for Fine-Grained Recognition

Naturally, fine-grained recognition, e.g., vehicle identification or bird classification, has specific hierarchical labels, where fine categories are always harder to be classified than coarse categories. However, most of the recent deep learning based methods neglect the semantic structure of fine-grained objects and do not take advantage of the traditional fine-grained recognition techniques (e.g. coarse-to-fine classification). In this paper, we propose a novel framework with a two-branch network (coarse branch and fine branch), i.e., semantic bilinear pooling, for fine-grained recognition with a hierarchical label tree. This framework can adaptively learn the semantic information from the hierarchical levels. Specifically, we design a generalized cross-entropy loss for the training of the proposed framework to fully exploit the semantic priors via considering the relevance between adjacent levels and enlarge the distance between samples of different coarse classes. Furthermore, our method leverages only the fine branch when testing so that it adds no overhead to the testing time. Experimental results show that our proposed method achieves state-of-the-art performance on four public datasets.

cs.CV↗

arXiv2021

Stereo CenterNet based 3D Object Detection for Autonomous Driving

Recently, three-dimensional (3D) detection based on stereo images has progressed remarkably; however, most advanced methods adopt anchor-based two-dimensional (2D) detection or depth estimation to address this problem. Nevertheless, high computational cost inhibits these methods from achieving real-time performance. In this study, we propose a 3D object detection method, Stereo CenterNet (SC), using geometric information in stereo imagery. SC predicts the four semantic key points of the 3D bounding box of the object in space and utilizes 2D left and right boxes, 3D dimension, orientation, and key points to restore the bounding box of the object in the 3D space. Subsequently, we adopt an improved photometric alignment module to further optimize the position of the 3D bounding box. Experiments conducted on the KITTI dataset indicate that the proposed SC exhibits the best speed-accuracy trade-off among advanced methods without using extra data.

cs.CV↗

arXiv2021

Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising

Images captured underwater are often characterized by low contrast, color distortion, and noise. To address these visual degradations, we propose a novel scheme by constructing an adaptive color and contrast enhancement, and denoising (ACCE-D) framework for underwater image enhancement. In the proposed framework, Difference of Gaussian (DoG) filter and bilateral filter are respectively employed to decompose the high-frequency and low-frequency components. Benefited from this separation, we utilize soft-thresholding operation to suppress the noise in the high-frequency component. Specially, the low-frequency component is enhanced by using an adaptive color and contrast enhancement (ACCE) strategy. The proposed ACCE is an adaptive variational framework implemented in the HSI color space, which integrates data term and regularized term, as well as introduces Gaussian weight and Heaviside function to avoid over-enhancement and oversaturation. Moreover, we derive a numerical solution for ACCE, and adopt a pyramid-based strategy to accelerate the solving procedure. Experimental results demonstrate that our strategy is effective in color correction, visibility improvement, and detail revealing. Comparison with state-of-the-art techniques also validate the superiority of proposed method. Furthermore, we have verified the utility of our proposed ACCE-D for enhancing other types of degraded scenes, including foggy scene, sandstorm scene and low-light scene.

cs.CV↗