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Muhammad Usama, Mohammad Sadil Khan, Didier Stricker, Muhammad Zeshan Afzal

Generating editable 3D CAD models from natural language remains challenging, as existing text-to-CAD systems either produce meshes or rely on scarce design-history data. We present NURBGen, the first framework to generate high fidelity 3D CAD models directly from text using Non-Uniform Rational B-Splines (NURBS). To achieve this, we fine-tune a large language model (LLM) to translate free-form texts into JSON representations containing NURBS surface parameters (i.e, control points, knot vectors, degrees, and rational weights) which can be directly converted into BRep format using Python. We further propose a hybrid representation that combines untrimmed NURBS with analytic primitives to handle trimmed surfaces and degenerate regions more robustly, while reducing token complexity. Additionally, we introduce partABC, a curated subset of the ABC dataset consisting of individual CAD components, annotated with detailed captions using an automated annotation pipeline. NURBGen demonstrates strong performance on diverse prompts, surpassing prior methods in geometric fidelity and dimensional accuracy, as confirmed by expert evaluations.

Mostofa Rafid Uddin, Jana Armouti, Umong Sain, Md Asib Rahman, Xingjian Li, Min Xu

In this work, we propose a disentangled latent optimization-based method for parameterizing grouped deforming 3D objects into shape and deformation factors in an unsupervised manner. Our approach involves the joint optimization of a generator network along with the shape and deformation factors, supported by specific regularization techniques. For efficient amortized inference of disentangled shape and deformation codes, we train two order-invariant PoinNet-based encoder networks in the second stage of our method. We demonstrate several significant downstream applications of our method, including unsupervised deformation transfer, deformation classification, and explainability analysis. Extensive experiments conducted on 3D human, animal, and facial expression datasets demonstrate that our simple approach is highly effective in these downstream tasks, comparable or superior to existing methods with much higher complexity.

Jiahang Tu, Ye Li, Yiming Wu, Hanbin Zhao, Chao Zhang, Hui Qian

The success of diffusion models has raised concerns about the generation of unsafe or harmful content, prompting concept erasure approaches that fine-tune modules to suppress specific concepts while preserving general generative capabilities. However, as the number of erased concepts grows, these methods often become inefficient and ineffective, since each concept requires a separate set of fine-tuned parameters and may degrade the overall generation quality. In this work, we propose a supertype-subtype concept hierarchy that organizes erased concepts into a parent–child structure. Each erased concept is treated as a child node, and semantically related concepts (e.g., macaw, and bald eagle) are grouped under a shared parent node, referred to as a supertype concept (e.g., bird). Rather than erasing concepts individually, we introduce an effective and efficient group-wise suppression method, where semantically similar concepts are grouped and erased jointly by sharing a single set of learnable parameters. During the erasure phase, standard diffusion regularization is applied to preserve denoising process in unmasked regions. To mitigate the degradation of supertype generation caused by excessive erasure of semantically related subtypes, we propose a novel method called Supertype-Preserving Low-Rank Adaptation (SuPLoRA), which encodes the supertype concept information in the frozen down-projection matrix and updates only the up-projection matrix during erasure. Theoretical analysis demonstrates the effectiveness of SuPLoRA in mitigating generation performance degradation. We construct a more challenging benchmark that requires simultaneous erasure of concepts across diverse domains, including celebrities, objects, and pornographic content. Comprehensive experiments demonstrate that our method achieves a superior balance between effective multi-concept erasure and the preservation of desirable generative performance.

Chongjun Tu, Peng Ye, Dongzhan Zhou, Tao Chen, Wanli Ouyang

Current Multimodal Chain-of-Thought (MCoT) methods suffer from low-quality multimodal reasoning, characterized by overthinking on simple queries and inefficient utilization of visual information, resulting in vast inefficient and ineffective computations. In this paper, we discover that Multimodal Large Language Models (MLLMs) possess inherent capabilities to distinguish between simple and difficult queries and enhance task-related visual information, which remain underutilized by existing approaches. Based on this insight, we propose Self-Driven Refined Multimodal CoT (SDR-MCoT), a training-free framework that mitigates these issues through two self-driven modules. First, our selective thinking module employs entropy-based confidence estimation to determine whether queries require detailed reasoning, preventing overthinking on simple questions. Second, our step-wise visual enhancement module strengthens attention to relevant visual regions at each reasoning step without inserting additional tokens, achieving fine-grained visual grounding and enhancement with minimal overhead. Moreover, SDR-MCoT can be seamlessly integrated into various MLLMs, offering a practical solution for improving multimodal reasoning. Comprehensive experiments across eight benchmarks from diverse domains (multimodal reasoning, visual understanding, hallucination, and mathematical reasoning) demonstrate that SDR-MCoT consistently outperforms existing MCoT methods on four different base models with reduced overhead. For instance, on Qwen2-VL-7B, our method improves average accuracy by over 6% while reducing token consumption by approximately 60% compared to zero-shot CoT.

Cheng-Chang Tsai, Kai-Wen Cheng, Chun-Shien Lu

Federated learning (FL) has shown success in collaboratively training a model among decentralized data resources without directly sharing privacy-sensitive training data. Despite recent advances, non-IID (non-independent and identically distributed) data poses an inevitable challenge that hinders the use of FL. In this work, we address the issue of non-IID histopathological images with feature distribution shifts from an intuitive perspective that has only received limited attention. Specifically, we address this issue from the perspective of data distribution by solely adjusting the data distributions of all clients. Building on the success of diffusion models in fitting data distributions and leveraging stain separation to extract the pivotal features that are closely related to the non-IID properties of histopathological images, we propose a Federated Stain Distribution Alignment (FedSDA) method. FedSDA aligns the stain distribution of each client with a target distribution in an FL framework to mitigate distribution shifts among clients. Furthermore, considering that training diffusion models on raw data in FL has been shown to be susceptible to privacy leakage risks, we circumvent this problem while still effectively achieving alignment. Extensive experimental results show that FedSDA is not only effective in improving baselines that focus on mitigating disparities across clients’ model updates but also outperforms baselines that address the non-IID data issues from the perspective of data distribution. We show that FedSDA provides valuable and practical insights for the computational pathology community.

Xuan Tong, Yuxuan Lin, Junxiong Lin, Xinji Mai, Haoran Wang, Zeng Tao, Yang Yao, Ruofan Wang, Wenqiang Zhang

Generating anomalies is a crucial method to enhance detection and classification performance by expanding anomalous data repository. However, existing anomaly generation methods overlook the intrinsic entanglement between diverse anomaly types and product structures, leading to semantic ambiguity. We propose CADiff, a context-aware generation framework that reframes anomalies as compositional perturbations. Firstly, we propose Context-aware Text Prompt (CTP), a mechanism which contains multiple tokens that characterize anomalies and products separately to enhance the contextual consistency of generated images and refine the local variability of anomalies. Secondly, we develop Self-adaptive Spatial Control (SSC), a self-adaptive interaction design that mitigates anomaly leakage or missing phenomena. Thirdly, we introduce Intensity-controllable Attention Re-weighting (IAR), an inference scheduling scheme with the ability to amplify or attenuate abnormal semantic effects to improve generation diversity. Extensive experiments on MVTec AD and VisA datasets demonstrate the superiority of our proposed method over state-of-the-art methods in both realism and diversity of the generated results, and significantly improve the performance of downstream tasks, including anomaly detection, anomaly localization, and anomaly classification tasks.

Tianxu Tong, Xinrun Liu, Hongmin Liu, Bin Fan

3D object detection is a critical component of autonomous driving, yet its performance degrades severely in adverse weather due to the degradation of LiDAR point clouds. While existing LiDAR-4D radar fusion methods enhance robustness by incorporating weather-robust 4D radar data, they often depend on well geometric structures from LiDAR and so struggle to effectively exploit radar data in case of degraded LiDAR data. To tackle this challenge, we propose REL, a novel 4D radar-guided LiDAR geometric enhancement framework. It utilizes 4D radar features to dynamically generate virtual LiDAR points, effectively increasing the density of degraded LiDAR data. Moreover, a Position-Guided Cross Attention (PGCA) module is proposed to enhance the feature representation of virtual points, while an Adaptive Feature Fusion (AFF) module is designed to integrate virtual and real LiDAR features. Extensive experiments on the K-Radar and Vod-Fog datasets demonstrate that REL achieves state-of-the-art 3D object detection performance under diverse adverse weather conditions. Notably, REL improves the overall AP3D by 9.3% on K-Radar and boosts the cyclist class by up to 52.9% 3D mAP under the most severe foggy condition on Vod-Fog.

Haoyang Tong, Hongbo Wang, Jin Liu, Qi Wang, Jie Cao, Ran He

Score Distillation Sampling has driven recent advances in text-to-3D generation. However, current approaches often fail to produce 3D assets that are both rich in detail and consistent across viewpoints. These limitations primarily arise from imbalanced guidance on fine-grained details and an overdependence on single-view optimization—issues exacerbated by the excessive randomness in selecting diffusion timesteps and camera configurations. Such deficiencies commonly lead to blurry textures and inter-view inconsistencies, which degrade visual realism and hinder practical deployment. To tackle these challenges, we introduce CoGrad3D, a unified generative refinement framework that adopts a continuously adaptive optimization strategy. By dynamically modulating the optimization focus based on real-time convergence signals, CoGrad3D ensures balanced progress toward both geometric completeness and high-fidelity detail. Concretely, we propose an adaptive region sampling strategy that emphasizes under-converged viewing areas, promoting stable and uniform optimization. To facilitate the transition from coarse geometry to fine-grained reconstruction, we develop a region-aware temporal scheduling scheme that integrates global training dynamics with local convergence feedback. Furthermore, we introduce a gradient fusion mechanism that consolidates historical gradients from adjacent viewpoints, mitigating view-specific artifacts and promoting the emergence of coherent 3D structures. Extensive experiments demonstrate that CoGrad3D substantially surpasses existing methods in both geometric consistency and texture fidelity, enabling the generation of high-quality, view-consistent 3D models from textual descriptions.

Xuanyu Tian, Lixuan Chen, Qing Wu, Xiao Wang, Jie Feng, Yuyao Zhang, Hongjiang Wei

Cardiac magnetic resonance (CMR) imaging is widely used to characterize cardiac morphology and function. To accelerate CMR imaging, various methods have been proposed to recover high-quality spatiotemporal CMR images from highly undersampled k-t space data. However, current CMR reconstruction techniques either fail to achieve satisfactory image quality or are restricted by the scarcity of ground truth data, leading to limited applicability in clinical scenarios. In this work, we proposed MoCo‑INR, a new unsupervised method that integrates implicit neural representations (INR) with the conventional motion‑compensated (MoCo) framework. Using the explicit motion modeling and the continuous prior of INRs, our MoCo-INR can produce accurate cardiac motion decomposition and high-quality CMR reconstruction. Moreover, we present a new INR network architecture tailored to the CMR problem, which can greatly stabilize model optimization. Experiments on retrospective (i.e., simulated) datasets demonstrate the superiority of MoCo‑INR over state‑of‑the‑art methods, achieving fast convergence and fine‑detailed reconstructions at ultra‑high acceleration factors (e.g., 20x in VISTA sampling). In addition, evaluations on prospective (i.e., real-acquired) free‑breathing CMR scans highlight its clinical practicality for real‑time imaging. Several ablation studies also confirm the effectiveness of critical components of MoCo-INR.

Huiyuan Tian, Bonan Xu, Shijian Li

While feature-based knowledge distillation has proven highly effective for compressing CNNs, these techniques unexpectedly fail when applied to Vision Transformers (ViTs), often performing worse than simple logit-based distillation. We provide the first comprehensive analysis of this phenomenon through a novel analytical framework termed as "distillation dynamics", combining frequency spectrum analysis, information entropy metrics, and activation magnitude tracking. Our investigation reveals that ViTs exhibit a distinctive U-shaped information processing pattern: initial compression followed by expansion. We identify the root cause of negative transfer in feature distillation: a fundamental representational paradigm mismatch between teacher and student models. Through frequency-domain analysis, we show that teacher models employ distributed, high-dimensional encoding strategies in later layers that smaller student models cannot replicate due to limited channel capacity. This mismatch causes late-layer feature alignment to actively harm student performance. Our findings reveal that successful knowledge transfer in ViTs requires moving beyond naive feature mimicry to methods that respect these fundamental representational constraints, providing essential theoretical guidance for designing effective ViTs compression strategies.

Zeze Tao, Jinjia Peng, Huibing Wang

Deep neural networks are susceptible to adversarial examples, which induce incorrect predictions through imperceptible perturbations. Transfer-based attacks create adversarial examples for surrogate models and transfer these examples to target models under black-box scenarios. Recent studies have established a strong correlation between the geometric properties of loss landscapes and the transferability of adversarial examples, demonstrating that flatter loss surfaces consistently yield superior transferability. However, we identify that these methods fail to account for the loss landscape flatness along the path from the current point to local minima, resulting in poor transferability. To address this, this paper constructs a novel Path Flatness Attack (PFA) method to significantly enhance the transferability of adversarial examples. Specifically, this paper proposes a novel path flatness indicator that not only evaluates the flatness in local minima regions but also explicitly quantifies the loss surface geometry along the trajectory from the current point to the minimum. Furthermore, we incorporate the path flatness indicator into the attack process, integrating penalties over low-loss points along the path while maximizing the loss function, thereby explicitly flattening the loss landscape. Extensive experiments demonstrate that PFA consistently achieves state-of-the-art attack performance across all experimental settings.

Hongyu Tao, Shuaiying Hou, Junheng Fang, Mingyao Shi, Weiwei Xu

Large and high-quality motion datasets are essential for advancing human motion modeling. However, limitations of existing motion datasets, such as insufficient scale or inadequate quality, significantly hinder the progress of this field. To address these limitations, we introduce Mimic-X, a large-scale (52 hours), physically plausible 3D human motion dataset. To construct Mimic-X, we develop an adaptive option framework that controls a physically simulated character to imitate low-quality motions extracted from a vast collection of online videos. Specifically, we first apply hierarchical clustering to group motions into clusters, and then train option policies to mimic motions sampled from these clusters. Considering the noisy nature of low-quality motions, we utilize a separate encoder for each cluster to map the noisy motions within the cluster into a compact latent space. This significantly enhances the quality of the imitated motions while accelerating the learning process. Subsequently, we employ dynamic programming as a meta-policy to efficiently organize the option policies to generate complete motion clips. Finally, we perform fine-tuning to each motion sequence to further refine motion quality. The proposed adaptive option framework outperforms state-of-the-art human motion recovery methods across various evaluation metrics, demonstrating that motions in Mimic-X exhibit higher quality and greater physical plausibility. Furthermore, experimental results show that Mimic-X enhances the performance of motion generation methods, verifying its effectiveness for motion modeling tasks.

Zhenyu Tang, Chaoran Feng, Xinhua Cheng, Wangbo Yu, Junwu Zhang, Yuan Liu, Xiao-Xiao Long, Wenping Wang, Li Yuan

3D Gaussian Splatting (3DGS) achieves impressive quality and rendering speed, but with millions of 3D Gaussians and significant storage and transmission costs. In this paper, we aim to develop a simple yet effective method called NeuralGS that compresses the original 3DGS into a compact representation. Our observation is that neural fields like NeRF can represent complex 3D scenes with Multi-Layer Perceptron (MLP) neural networks using only a few megabytes. Thus, NeuralGS effectively adopts the neural field representation to encode the attributes of 3D Gaussians with MLPs, only requiring a small storage size even for a large-scale scene. To achieve this, we adopt a clustering strategy and fit the Gaussians within each cluster using different tiny MLPs, based on importance scores of Gaussians as fitting weights. We experiment on multiple datasets, achieving a 91$\times$ average model size reduction without harming the visual quality.

Zhenhua Tang, Yudian Zheng, Yuzhang Zhong, Haolun Li, Yanbin Hao, Chi-Man Pun

While diffusion models show promise for intent-based grasp generation, their isotropic noise schedules struggle with joint-specific sensitivity and task-aware variability. This limitation leads to grasps with suboptimal semantic alignment or physical feasibility. To address this challenge, we propose Semantic-guided Noise Scaling for grasp generation (SNS-Grasp), a novel framework that integrates two key innovations. First, the Semantic-guided Noise Scaling Diffusion (SNS-Diff) module generates intent-aware grasps by replacing isotropic noise with anisotropic modulation, dynamically adapting to task semantics and joint-specific sensitivity. Specifically, SNS-Diff leverages a pretrained Intent Recognizer to extract task-aware confidence scores and joint-specific gradient sensitivities from the interaction context. These signals adjust the noise scaling during denoising, downweighting perturbations for semantically critical joints to ensure semantic alignment. Second, the Fine-grained Grasp Refinement (FGR) module establishes dynamic joint-vertex coupling through fine-grained hand-object spatial relationships, enabling iterative optimization of physically executable grasps. Extensive experiments on OakInk and GRAB demonstrate SNS-Grasp's superior performance in semantic accuracy and physical feasibility, with robust generalization to unseen objects.

Zhenchen Tang, Songlin Yang, Bo Peng, Zichuan Wang, Jing Dong

The rapid progress of multi-modal large language models (MLLMs) has boosted the task of image quality assessment (IQA). However, a key challenge arises from the inherent mismatch between the discrete token outputs of MLLMs and the continuous nature of quality scores required by IQA tasks. This discrepancy significantly hinders the performance of MLLM-based IQA methods. Previous approaches that convert discrete token predictions into continuous scores often suffer from conversion errors. Moreover, the semantic confusion introduced by level tokens (e.g., “good”) further constrains the performance of MLLMs on IQA tasks and degrades their original capabilities to related tasks. To tackle these problems, we provide a theoretical analysis of the errors inherent in previous approaches and, motivated by this analysis, propose a simple yet effective framework, Q-Scorer. This framework incorporates a lightweight regression module and IQA-specific score tokens into the MLLM pipeline. Extensive experiments demonstrate that Q-Scorer achieves state-of-the-art performance across multiple IQA benchmarks, generalizes well to mixed datasets, and further improves combined with other methods.

Yuanmin Tang, Jing Yu, Keke Gai, Gang Xiong, Gaopeng Gou, Meikang Qiu, Qi Wu

Zero-shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with varied visual manipulation intents across domains, scenes, objects, and attributes. A key challenge is that existing datasets contain limited intent-relevant annotations, making it hard for models to infer human intent from textual modifications. We introduce an intent-centric image–text dataset generated via reasoning by a Multimodal Large Language Model (MLLM) to better train ZS-CIR models for human manipulation intent understanding. Building on this dataset, we propose De-MINDS, a framework that distills the MLLM’s reasoning ability to capture manipulation intent and enhance models’ comprehension of modified text. A simple mapping network translates image information into language space and combines it with the manipulation text to form a query. De-MINDS then extracts intention-relevant information from this query and encodes it as pseudo-word tokens for accurate ZS-CIR. Across four ZS-CIR tasks, De-MINDS shows strong generalization and improves over existing methods by 2.15% to 4.05%, establishing new state-of-the-art results with comparable inference time.

Yi Tang, Hiroshi Kawasaki, Takafumi Iwaguchi, Yuhang Zhang, Hiroshi Masui

Since high-fidelity reference images are difficult to obtain in real underwater scenes, most deep models trained by synthetic paired data cannot match real-world data exactly. In this paper, we propose an unsupervised training framework for underwater image enhancement (UIE) by leveraging an iterative training strategy and quantification of specific neural units. Specifically, to eliminate the heavy color cast and distortion in the underwater images, we decompose the unsupervised image enhancement as two targeted sub-tasks, namely colorization and color compensation. First, a diffusion model is introduced for colorization to correct the green and blue color casts. Then, to intensify the learning ability of balanced color information, we introduce an extra network branch and propose a quantification mechanism for color compensation. The extra branch encodes style information from normal images into the generative model, while the quantification mechanism identifies and adjusts neural units relevant to warm colors, improving the model’s ability to learn balanced color feature representations for robust generation. In the end, through iterative training, color cast and distortion are progressively reduced, leading to a gradual improvement in the quality of the generated images. Experimental results on various widely used underwater datasets demonstrate that our approach achieves excellent performance, even when compared to recent supervised methods.

Ni Tang, Xiaotong Luo, Zihan Cheng, Liangtai Zhou, Dongxiao Zhang, Yanyun Qu

Diffusion models have revealed powerful potential in all-in-one image restoration (AiOIR), which is talented in generating abundant texture details. The existing AiOIR methods either retrain a diffusion model or fine-tune the pretrained diffusion model with extra conditional guidance. However, they often suffer from high inference costs and limited adaptability to diverse degradation types. In this paper, we propose an efficient AiOIR method, Diffusion Once and Done (DOD), which aims to achieve superior restoration performance with only one-step sampling of Stable Diffusion (SD) models. Specifically, multi-degradation feature modulation is first introduced to capture different degradation prompts with a pretrained diffusion model. Then, parameter-efficient conditional low-rank adaptation integrates the prompts to enable the fine-tuning of the SD model for adapting to different degradation types. Besides, a high-fidelity detail enhancement module is integrated into the decoder of SD to improve structural and textural details. Experiments demonstrate that our method outperforms existing diffusion-based restoration approaches in both visual quality and inference efficiency.

Lexiang Tang, Xianwei Zhuang, Bang Yang, Zhiyuan Hu, Hongxiang Li, Lu Ma, Jinghan Ru, Yuexian Zou

Large vision-language models (LVLMs) have demonstrated impressive capabilities across diverse multimodal tasks, yet they remain highly susceptible to visual hallucinations (VH), often producing confident but inaccurate descriptions of visual content. Building on the insight that not all tokens and attention heads contribute equally to VH mitigation, we introduce VisFlow, a lightweight and training-free framework that alleviates hallucinations by directly modulating attention patterns during inference. To address two primary challenges of VH, namely insufficient visual attention and the dominance of language priors, we identify three problematic attention behaviors in LVLMs: (1) disproportionate allocation of attention to uninformative or trailing visual tokens, (2) over-dependence on the previously generated token, and (3) excessive fixation on system prompts that hinders multimodal integration. To overcome these issues, VisFlow introduces a dual-level Attention Intervention, consisting of Token-level Attention Intervention (TAI), which reinforces attention to salient visual regions, and Head-level Attention Intervention (HAI), which suppresses undue focus on system prompts and adjacent text tokens. Together, these interventions strengthen visual alignment while reducing linguistic bias. Extensive experiments across diverse models and benchmarks demonstrate that VisFlow effectively mitigates hallucinations with minimal computational overhead.

Keke Tang, Tianyu Hao, Xiaofei Wang, Weilong Peng, Denghui Zhang, Peican Zhu, Zhihong Tian

Most adversarial attacks on point clouds perturb a large number of points, causing widespread geometric changes and limiting applicability in real-world scenarios. While recent works explore sparse attacks by modifying only a few points, such approaches often struggle to maintain effectiveness due to the limited influence of individual perturbations. In this paper, we propose SCP, a sparse and cooperative perturbation framework that selects and leverages a compact subset of points whose joint perturbations produce amplified adversarial effects. Specifically, SCP identifies the subset where the misclassification loss is locally convex with respect to their joint perturbations, determined by checking the positive-definiteness of the corresponding Hessian block. The selected subset is then optimized to generate high-impact adversarial examples with minimal modifications. Extensive experiments show that SCP achieves 100% attack success rates, surpassing state-of-the-art sparse attacks, and delivers superior imperceptibility to dense attacks with far fewer modifications.