pwc-archive/papers-with-abstracts huggingface dataset:
Attention to Burstiness: Low-Rank Bilinear Prompt Tuning
Authors: Yuzhu Wang, Manni Duan, Shu Kong
Date: 2025-06-28 00:00:00
Tasks: Visual Prompt Tuning

Visual Prompt Tuning (VPT) is a parameter-efficient fune-tuning technique that adapts a pre-trained vision Transformer (ViT) by learning a small set of parameters in the input space, known as prompts. In VPT, we uncover ``burstiness'' in the values arising from the interaction of image patch embeddings, and the key and query projectors within Transformer's self-attention module. Furthermore, the values of patch embeddings and the key and query projectors exhibit Laplacian and hyper-Laplacian distribution, respectively. Intuitively, these non-Gaussian distributions pose challenges for learning prompts. To address this, we propose whitening these data, de-correlating them and equalizing their variance towards more Gaussian before learning prompts. We derive the whitening matrix over random image patch embeddings and ViT's key and query projectors, and multiply it with the prompt to be learned in a bilinear manner. Surprisingly, this method significantly accelerates prompt tuning and boosts accuracy, e.g., $>$25 accuracy points on the CUB dataset; interestingly, it learns ``bursty prompts''. Extending the bilinear model which is known to introduce burstiness, we present a compact, low-rank version by learning two smaller matrices whose multiplication yields the final prompts. We call the proposed methods Bilinear Prompt Tuning (BPT). Extensive experiments across multiple benchmark datasets demonstrate that BPT methods not only outperform various VPT methods but also reduce parameter count and computation overhead.
Layer Importance for Mathematical Reasoning is Forged in Pre-Training and Invariant after Post-Training
Authors: Aadim Nepal, Safal Shrestha, Anubhav Shrestha, Minwu Kim, Keith Ross
Date: 2025-06-27 00:00:00
Tasks: Knowledge Distillation, Mathematical Reasoning, reinforcement-learning, Reinforcement Learning

Large language models can exhibit improved mathematical reasoning capabilities following post-training with instruction tuning, reinforcement learning, or knowledge distillation. However, it remains unclear whether these improvements are driven by major changes in transformer layers or from minor adjustments that leave the relative layer importance structures of the base model largely unchanged. We investigate this question through systematic layer-wise ablation experiments, examining base, instruction-tuned, knowledge-distilled, and reinforcement learning variants on mathematical reasoning benchmarks. Our findings show that mathematical reasoning gives rise to a specific layer importance structure, and this structure persists across all post-training paradigms. Removal of such layers causes accuracy drops of up to 80%. In contrast, non-mathematical tasks like factual recall exhibit no critical layers. This distinction suggests that mathematical reasoning requires specialized layers that emerge during pre-training, while other non-reasoning tasks do not. From an information-theoretic perspective, we also observe that these critical layers are the same layers where major representational transformation occurs.
CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation
Authors: Xinlei Yu, Chanmiao Wang, Hui Jin, Ahmed Elazab, Gangyong Jia, Xiang Wan, Changqing Zou, Ruiquan Ge
Date: 2025-06-29 00:00:00
Tasks: Organ Segmentation

Multi-organ medical segmentation is a crucial component of medical image processing, essential for doctors to make accurate diagnoses and develop effective treatment plans. Despite significant progress in this field, current multi-organ segmentation models often suffer from inaccurate details, dependence on geometric prompts and loss of spatial information. Addressing these challenges, we introduce a novel model named CRISP-SAM2 with CRoss-modal Interaction and Semantic Prompting based on SAM2. This model represents a promising approach to multi-organ medical segmentation guided by textual descriptions of organs. Our method begins by converting visual and textual inputs into cross-modal contextualized semantics using a progressive cross-attention interaction mechanism. These semantics are then injected into the image encoder to enhance the detailed understanding of visual information. To eliminate reliance on geometric prompts, we use a semantic prompting strategy, replacing the original prompt encoder to sharpen the perception of challenging targets. In addition, a similarity-sorting self-updating strategy for memory and a mask-refining process is applied to further adapt to medical imaging and enhance localized details. Comparative experiments conducted on seven public datasets indicate that CRISP-SAM2 outperforms existing models. Extensive analysis also demonstrates the effectiveness of our method, thereby confirming its superior performance, especially in addressing the limitations mentioned earlier. Our code is available at: https://github.com/YU-deep/CRISP\_SAM2.git.
Visual-Semantic Knowledge Conflicts in Operating Rooms: Synthetic Data Curation for Surgical Risk Perception in Multimodal Large Language Models
Authors: Weiyi Zhao, Xiaoyu Tan, Liang Liu, Sijia Li, Youwei Song, Xihe Qiu
Date: 2025-06-25 00:00:00
Tasks: Specificity

Surgical risk identification is critical for patient safety and reducing preventable medical errors. While multimodal large language models (MLLMs) show promise for automated operating room (OR) risk detection, they often exhibit visual-semantic knowledge conflicts (VS-KC), failing to identify visual safety violations despite understanding textual rules. To address this, we introduce a dataset comprising over 34,000 synthetic images generated by diffusion models, depicting operating room scenes containing entities that violate established safety rules. These images were created to alleviate data scarcity and examine MLLMs vulnerabilities. In addition, the dataset includes 214 human-annotated images that serve as a gold-standard reference for validation. This comprehensive dataset, spanning diverse perspectives, stages, and configurations, is designed to expose and study VS-KC. Fine-tuning on OR-VSKC significantly improves MLLMs' detection of trained conflict entities and generalizes well to new viewpoints for these entities, but performance on untrained entity types remains poor, highlighting learning specificity and the need for comprehensive training. The main contributions of this work include: (1) a data generation methodology tailored for rule-violation scenarios; (2) the release of the OR-VSKC dataset and its associated benchmark as open-source resources; and (3) an empirical analysis of violation-sensitive knowledge consistency in representative MLLMs. The dataset and appendix are available at https://github.com/zgg2577/VS-KC.
Dare to Plagiarize? Plagiarized Painting Recognition and Retrieval
Authors: Sophie Zhou, Shu Kong
Date: 2025-06-29 00:00:00
Tasks: Metric Learning, Retrieval

Art plagiarism detection plays a crucial role in protecting artists' copyrights and intellectual property, yet it remains a challenging problem in forensic analysis. In this paper, we address the task of recognizing plagiarized paintings and explaining the detected plagarisms by retrieving visually similar authentic artworks. To support this study, we construct a dataset by collecting painting photos and synthesizing plagiarized versions using generative AI, tailored to specific artists' styles. We first establish a baseline approach using off-the-shelf features from the visual foundation model DINOv2 to retrieve the most similar images in the database and classify plagiarism based on a similarity threshold. Surprisingly, this non-learned method achieves a high recognition accuracy of 97.2\% but suffers from low retrieval precision 29.0\% average precision (AP). To improve retrieval quality, we finetune DINOv2 with a metric learning loss using positive and negative sample pairs sampled in the database. The finetuned model greatly improves retrieval performance by 12\% AP over the baseline, though it unexpectedly results in a lower recognition accuracy (92.7\%). We conclude with insightful discussions and outline directions for future research.
MDPG: Multi-domain Diffusion Prior Guidance for MRI Reconstruction
Authors: Lingtong Zhang, Mengdie Song, Xiaohan Hao, Huayu Mai, Bensheng Qiu
Date: 2025-06-30 00:00:00
Tasks: Mamba, MRI Reconstruction

Magnetic Resonance Imaging (MRI) reconstruction is essential in medical diagnostics. As the latest generative models, diffusion models (DMs) have struggled to produce high-fidelity images due to their stochastic nature in image domains. Latent diffusion models (LDMs) yield both compact and detailed prior knowledge in latent domains, which could effectively guide the model towards more effective learning of the original data distribution. Inspired by this, we propose Multi-domain Diffusion Prior Guidance (MDPG) provided by pre-trained LDMs to enhance data consistency in MRI reconstruction tasks. Specifically, we first construct a Visual-Mamba-based backbone, which enables efficient encoding and reconstruction of under-sampled images. Then pre-trained LDMs are integrated to provide conditional priors in both latent and image domains. A novel Latent Guided Attention (LGA) is proposed for efficient fusion in multi-level latent domains. Simultaneously, to effectively utilize a prior in both the k-space and image domain, under-sampled images are fused with generated full-sampled images by the Dual-domain Fusion Branch (DFB) for self-adaption guidance. Lastly, to further enhance the data consistency, we propose a k-space regularization strategy based on the non-auto-calibration signal (NACS) set. Extensive experiments on two public MRI datasets fully demonstrate the effectiveness of the proposed methodology. The code is available at https://github.com/Zolento/MDPG.
Refine Any Object in Any Scene
Authors: Ziwei Chen, Ziling Liu, Zitong Huang, Mingqi Gao, Feng Zheng
Date: 2025-06-30 00:00:00
Tasks: Novel View Synthesis, Object

Viewpoint missing of objects is common in scene reconstruction, as camera paths typically prioritize capturing the overall scene structure rather than individual objects. This makes it highly challenging to achieve high-fidelity object-level modeling while maintaining accurate scene-level representation. Addressing this issue is critical for advancing downstream tasks requiring detailed object understanding and appearance modeling. In this paper, we introduce Refine Any object In any ScenE (RAISE), a novel 3D enhancement framework that leverages 3D generative priors to recover fine-grained object geometry and appearance under missing views. Starting from substituting degraded objects with proxies, via a 3D generative model with strong 3D understanding, RAISE progressively refines geometry and texture by aligning each proxy to its degraded counterpart in 7-DOF pose, followed by correcting spatial and appearance inconsistencies via registration-constrained enhancement. This two-stage refinement ensures the high-fidelity geometry and appearance of the original object in unseen views while maintaining consistency in spatial positioning, observed geometry, and appearance. Extensive experiments on challenging benchmarks show that RAISE significantly outperforms state-of-the-art methods in both novel view synthesis and geometry completion tasks. RAISE is made publicly available at https://github.com/PolySummit/RAISE.
Sensing Security Oriented OFDM-ISAC Against Multi-Intercept Threats
Authors: Lingyun Xu, Bowen Wang, Huiyong Li, Ziyang Cheng
Date: 2025-06-28 00:00:00
Tasks: Integrated sensing and communication, ISAC

In recent years, security has emerged as a critical aspect of integrated sensing and communication (ISAC) systems. While significant research has focused on secure communications, particularly in ensuring physical layer security, the issue of sensing security has received comparatively less attention. This paper addresses the sensing security problem in ISAC, particularly under the threat of multi-intercept adversaries. We consider a realistic scenario in which the sensing target is an advanced electronic reconnaissance aircraft capable of employing multiple signal interception techniques, such as power detection (PD) and cyclostationary analysis (CA). To evaluate sensing security under such sophisticated threats, we analyze two critical features of the transmitted signal: (i) power distribution and (ii) cyclic spectrum. Further, we introduce a novel ergodic cyclic spectrum metric which leverages the intrinsic mathematical structure of cyclostationary signals to more comprehensively characterize their behavior. Building on this analysis, we formulate a new ISAC design problem that explicitly considers sensing security, and we develop a low-complexity, efficient optimization approach to solve it. Simulation results demonstrate that the proposed metric is both effective and insightful, and that our ISAC design significantly enhances sensing security performance in the presence of multi-intercept threats.
MTADiffusion: Mask Text Alignment Diffusion Model for Object Inpainting
Authors: Jun Huang, Ting Liu, Yihang Wu, Xiaochao Qu, Luoqi Liu, Xiaolin Hu
Date: 2025-06-30 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Inpainting

Advancements in generative models have enabled image inpainting models to generate content within specific regions of an image based on provided prompts and masks. However, existing inpainting methods often suffer from problems such as semantic misalignment, structural distortion, and style inconsistency. In this work, we present MTADiffusion, a Mask-Text Alignment diffusion model designed for object inpainting. To enhance the semantic capabilities of the inpainting model, we introduce MTAPipeline, an automatic solution for annotating masks with detailed descriptions. Based on the MTAPipeline, we construct a new MTADataset comprising 5 million images and 25 million mask-text pairs. Furthermore, we propose a multi-task training strategy that integrates both inpainting and edge prediction tasks to improve structural stability. To promote style consistency, we present a novel inpainting style-consistency loss using a pre-trained VGG network and the Gram matrix. Comprehensive evaluations on BrushBench and EditBench demonstrate that MTADiffusion achieves state-of-the-art performance compared to other methods.
The Trilemma of Truth in Large Language Models
Authors: Germans Savcisens, Tina Eliassi-Rad
Date: 2025-06-30 00:00:00
Tasks: Attribute, Conformal Prediction, Knowledge Distillation, Multiple Instance Learning, Text Classification

We often attribute human characteristics to large language models (LLMs) and claim that they "know" certain things. LLMs have an internal probabilistic knowledge that represents information retained during training. How can we assess the veracity of this knowledge? We examine two common methods for probing the veracity of LLMs and discover several assumptions that are flawed. To address these flawed assumptions, we introduce sAwMIL (short for Sparse Aware Multiple-Instance Learning), a probing method that utilizes the internal activations of LLMs to separate statements into true, false, and neither. sAwMIL is based on multiple-instance learning and conformal prediction. We evaluate sAwMIL on 5 validity criteria across 16 open-source LLMs, including both default and chat-based variants, as well as on 3 new datasets. Among the insights we provide are: (1) the veracity signal is often concentrated in the third quarter of an LLM's depth; (2) truth and falsehood signals are not always symmetric; (3) linear probes perform better on chat models than on default models; (4) nonlinear probes may be required to capture veracity signals for some LLMs with reinforcement learning from human feedback or knowledge distillation; and (5) LLMs capture a third type of signal that is distinct from true and false and is neither true nor false. These findings provide a reliable method for verifying what LLMs "know" and how certain they are of their probabilistic internal knowledge.
The Curse of Depth in Large Language Models
Authors: Wenfang Sun, Xinyuan Song, Pengxiang Li, Lu Yin, Yefeng Zheng, Shiwei Liu
Date: 2025-02-09 00:00:00

In this paper, we introduce the Curse of Depth, a concept that highlights, explains, and addresses the recent observation in modern Large Language Models(LLMs) where nearly half of the layers are less effective than expected. We first confirm the wide existence of this phenomenon across the most popular families of LLMs such as Llama, Mistral, DeepSeek, and Qwen. Our analysis, theoretically and empirically, identifies that the underlying reason for the ineffectiveness of deep layers in LLMs is the widespread usage of Pre-Layer Normalization (Pre-LN). While Pre-LN stabilizes the training of Transformer LLMs, its output variance exponentially grows with the model depth, which undesirably causes the derivative of the deep Transformer blocks to be an identity matrix, and therefore barely contributes to the training. To resolve this training pitfall, we propose LayerNorm Scaling, which scales the variance of output of the layer normalization inversely by the square root of its depth. This simple modification mitigates the output variance explosion of deeper Transformer layers, improving their contribution. Our experimental results, spanning model sizes from 130M to 1B, demonstrate that LayerNorm Scaling significantly enhances LLM pre-training performance compared to Pre-LN. Moreover, this improvement seamlessly carries over to supervised fine-tuning. All these gains can be attributed to the fact that LayerNorm Scaling enables deeper layers to contribute more effectively during training.
FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation
Authors: Jiacheng Cui, Xinyue Bi, Yaxin Luo, Xiaohan Zhao, Jiacheng Liu, Zhiqiang Shen
Date: 2025-06-30 00:00:00
Tasks: Computational Efficiency, Dataset Distillation, GPU

Residual connection has been extensively studied and widely applied at the model architecture level. However, its potential in the more challenging data-centric approaches remains unexplored. In this work, we introduce the concept of Data Residual Matching for the first time, leveraging data-level skip connections to facilitate data generation and mitigate data information vanishing. This approach maintains a balance between newly acquired knowledge through pixel space optimization and existing core local information identification within raw data modalities, specifically for the dataset distillation task. Furthermore, by incorporating optimization-level refinements, our method significantly improves computational efficiency, achieving superior performance while reducing training time and peak GPU memory usage by 50%. Consequently, the proposed method Fast and Accurate Data Residual Matching for Dataset Distillation (FADRM) establishes a new state-of-the-art, demonstrating substantial improvements over existing methods across multiple dataset benchmarks in both efficiency and effectiveness. For instance, with ResNet-18 as the student model and a 0.8% compression ratio on ImageNet-1K, the method achieves 47.7% test accuracy in single-model dataset distillation and 50.0% in multi-model dataset distillation, surpassing RDED by +5.7% and outperforming state-of-the-art multi-model approaches, EDC and CV-DD, by +1.4% and +4.0%. Code is available at: https://github.com/Jiacheng8/FADRM.
Context-Driven Knowledge Graph Completion with Semantic-Aware Relational Message Passing
Authors: Siyuan Li, Ruitong Liu, Yan Wen, Te Sun
Date: 2025-06-29 00:00:00
Tasks: Knowledge Graph Completion, Knowledge Graphs, Link Prediction, Triplet

Semantic context surrounding a triplet $(h, r, t)$ is crucial for Knowledge Graph Completion (KGC), providing vital cues for prediction. However, traditional node-based message passing mechanisms, when applied to knowledge graphs, often introduce noise and suffer from information dilution or over-smoothing by indiscriminately aggregating information from all neighboring edges. To address this challenge, we propose a semantic-aware relational message passing. A core innovation of this framework is the introduction of a \textbf{semantic-aware Top-K neighbor selection strategy}. Specifically, this strategy first evaluates the semantic relevance between a central node and its incident edges within a shared latent space, selecting only the Top-K most pertinent ones. Subsequently, information from these selected edges is effectively fused with the central node's own representation using a \textbf{multi-head attention aggregator} to generate a semantically focused node message. In this manner, our model not only leverages the structure and features of edges within the knowledge graph but also more accurately captures and propagates the contextual information most relevant to the specific link prediction task, thereby effectively mitigating interference from irrelevant information. Extensive experiments demonstrate that our method achieves superior performance compared to existing approaches on several established benchmarks.
Gaussian Herding across Pens: An Optimal Transport Perspective on Global Gaussian Reduction for 3DGS
Authors: Tao Wang, Mengyu Li, Geduo Zeng, Cheng Meng, Qiong Zhang
Date: 2025-06-11 00:00:00
Tasks: 3DGS, Neural Rendering, SSIM

3D Gaussian Splatting (3DGS) has emerged as a powerful technique for radiance field rendering, but it typically requires millions of redundant Gaussian primitives, overwhelming memory and rendering budgets. Existing compaction approaches address this by pruning Gaussians based on heuristic importance scores, without global fidelity guarantee. To bridge this gap, we propose a novel optimal transport perspective that casts 3DGS compaction as global Gaussian mixture reduction. Specifically, we first minimize the composite transport divergence over a KD-tree partition to produce a compact geometric representation, and then decouple appearance from geometry by fine-tuning color and opacity attributes with far fewer Gaussian primitives. Experiments on benchmark datasets show that our method (i) yields negligible loss in rendering quality (PSNR, SSIM, LPIPS) compared to vanilla 3DGS with only 10% Gaussians; and (ii) consistently outperforms state-of-the-art 3DGS compaction techniques. Notably, our method is applicable to any stage of vanilla or accelerated 3DGS pipelines, providing an efficient and agnostic pathway to lightweight neural rendering.