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Mingwei Zhang, Jingtao Hu, Qiang Li, Qi Wang

Remote sensing change detection (CD) has achieved remarkable progress in recent years. However, little attention has been paid to generalizable change detection (GCD) methods that can effectively generalize to unseen scenarios or domains beyond the training distribution. The major challenges in GCD arise from domain diversity and bitemporal domain shifts in remote sensing images, caused by variations in imaging platforms, acquisition times, geographic regions, and observed events. To tackle these challenges, we propose GenCD, a GCD framework built upon vision foundation models (VFMs). Specifically, GenCD introduces two key components: (1) a Low-Rank Exchange Adaptation (LREA) strategy of VFMs that aligns bitemporal representations while preserving the generalization capacity of VFMs on single-temporal inputs; and (2) a Token-Guided Feature Refinement (TGFR) mechanism that leverages an input-independent token as a guide to refine difference features, improving the discrimination between changed and unchanged regions. We conduct extensive cross-dataset evaluations on eight diverse datasets across three binary CD tasks: land cover, land use, and building-only CD. The results consistently demonstrate the superior generalization of GenCD over SoTA methods, highlighting its effectiveness in GCD.

Mingjin Zhang, Zhongkai Yang, Fei Gao

Due to the difficulties of directly obtaining high-resolution hyperspectral images (HR-HSI), the fusion of low-resolution hyperspectral images (LR-HSI) and high-resolution multispectral images (HR-MSI) has emerged as an effective approach. While existing methods leverage image-level priors from HR-MSI, they often lack explicit semantic guidance for precise detail reconstruction. Recognizing that textual scene descriptions encapsulate valuable object attributes and contextual information, we introduce the first Language-Bridging framework for Hyperspectral and Multispectral image fusion (CO²IF). CO²IF leverages language semantics as prior knowledge to explicitly guide the reconstruction process. To bridge the modality gap between textual descriptions and high-dimensional hyperspectral data, we design a Cross-modal Optimal Transport (COT) module. COT establishes precise semantic correspondences between language features and the visual cues of individual spectral bands. Building upon this semantic alignment, we develop a Multimodal Coordinated State Space Model (CoMamba). CoMamba effectively integrates the language-derived priors with spatial information from HR-MSI and spectral information from LR-HSI. This language-guided reconstruction significantly enhances the extraction of crucial spatial-spectral details, leading to superior fidelity in the generated HR-HSI. In addition, this paper adds text descriptions for three widely used datasets. Both qualitative and quantitative experimental results on the public datasets confirm the superiority of the proposed method compared to the SOTA methods.

Liwen Zhang, Zhaoji Miao, Yusong Shen, Zechen Wei, Hui Hui, Jie Tian

Magnetic Particle Imaging (MPI) is an innovative medical modality, providing nanomolar-scale in vivo sensitivity and radiation-free dynamic real-time detection for precision medicine. However, MPI faces a challenging problem in accurately visualizing nanoparticle distributions, where the reconstructed images with unidirectional scanning exhibit anisotropy. The anisotropy in spatial resolution leads to distortion and blurred image boundaries. Existing deep learning methods for anisotropy calibration are only limited to simulation data due to lacking of real-world MPI datasets. To address the aforementioned problems, we spent over three years designing and constructing a real-world MPI anisotropic image datasets (20,156 images) with diverse phantoms (sensitivity, resolution, vessel, shape) and animal scanning. Then, we introduce a novel Mamba-based method, MPI-Mamba, for anisotropic image calibration. Specifically, we propose a latent feature fusion state space model (LFF-SSM) block for feature fusion and leverage conditional latent diffusion model (CL-DM) branch for feature extraction. The CL-DM is performed to extract latent features in a highly compressed latent space for guiding the calibration and deblurring process. Next, we exploit the LFF-SSM to fully fuse the extracted multi-scale features to capture contextual information from the image structure, enabling the model to learn the overall distribution of signal concentration. We evaluate our method and competing methods on simulation dataset and our constructed diverse real-world MPI datasets. The results show that our proposed approach outperforms competing methods for anisotropic image calibration and deblurring.

Lirui Zhang, Zhengkai Zhao, Zhi Zuo, Pan Gao, Jie Qin

Point cloud completion is a fundamental task in 3D vision. A persistent challenge in this field is simultaneously preserving fine-grained details present in the input while ensuring the global structural integrity of the completed shape. While recent works leveraging local symmetry transformations via direct regression have significantly improved the preservation of geometric structure details, these methods suffer from two major limitations: (1) These regression-based methods are prone to overfitting which tend to memorize instant-specific transformations instead of learning a generalizable geometric prior. (2) Their reliance on point-wise transformation regression lead to high sensitivity to input noise, severely degrading their robustness and generalization. To address these challenges, we introduce Simba, a novel framework that reformulates point-wise transformation regression as a distribution learning problem. Our approach integrates symmetry priors with the powerful generative capabilities of diffusion models, avoiding instance-specific memorization while capturing robust geometric structures. Additionally, we introduce a hierarchical Mamba-based architecture to achieve high-fidelity upsampling. Extensive experiments across the PCN, ShapeNet, and KITTI benchmarks validate our method's state-of-the-art (SOTA) performance.

Lingfeng Zhang, Haoxiang Fu, Xiaoshuai Hao, Shuyi Zhang, Qiang Zhang, Rui Liu, Long Chen, Wenbo Ding

Embodied navigation is a fundamental capability that enables embodied agents to effectively interact with the physical world in various complex environments. However, a significant gap remains between current embodied navigation tasks and real-world requirements, as existing methods often struggle to integrate high-level human instructions with spatial understanding. To address this gap, we propose a new task of embodied navigation called spatial navigation, which encompasses two key components: spatial object navigation (SpON) for object-specific guidance and spatial area navigation (SpAN) for navigating to designated areas. Specifically, SpON guides agents to specific objects by leveraging spatial relationships and contextual understanding, while SpAN focuses on navigating to defined areas within complex environments. Together, these components significantly enhance agents’ navigation capabilities, enabling more effective interactions in real-world scenarios. To support this task, we have generated a spatial navigation dataset consisting of 10K trajectories within the simulator. This dataset includes high-level human instructions, detailed observations, and corresponding navigation actions, providing a comprehensive resource to enhance agent training and performance. Building on the spatial navigation dataset, we introduce SpNav, a hierarchical navigation framework. Specifically, SpNav employs vision-language model (VLM) to interpret high-level human instructions and accurately identify goal objects or areas within the observation range, achieving precise point-to-point navigation using a map and enhancing the agent’s ability to oper- ate effectively in complex environments by bridging the gap between perception and action. Extensive experiments show that SpNav achieves state-of-the-art (SOTA) performance in spatial navigation tasks across both simulated and real-world environments, validating the effectiveness of our method.

Liheng Zhang, Jin Wang, Hui Li, Bingfeng Zhang, Weifeng Liu

3D understanding has drawn significant attention recently, leveraging Vision-Language Models (VLMs) to enable multi-modal reasoning between point cloud and text data. Current 3D-VLMs directly embed the 3D point clouds into 3D tokens, following large 2D-VLMs with powerful reasoning capabilities. However, this framework has a great computational cost limiting its application, where we identify that the bottleneck lies in processing all 3D tokens in the Large Language Model (LLM) part. This raises the question: how can we reduce the computational overhead introduced by 3D tokens while preserving the integrity of their essential information? To address this question, we introduce Hierarchical Compensatory Compression (HCC-3D) to efficiently compress 3D tokens while maintaining critical detail retention. Specifically, we first propose a global structure compression (GSC), in which we design global queries to compress all 3D tokens into a few key tokens while keeping overall structural information. Then, to compensate for the information loss in GSC, we further propose an adaptive detail mining (ADM) module that selectively recompresses salient but under-attended features through complementary scoring. Extensive experiments demonstrate that HCC-3D not only achieves extreme compression ratios (approximately 98%) compared to previous 3D VLMs, but also achieves new state-of-the-art performance, showing the great improvements on both efficiency and performance.

Ligang Zhang, Jun Li, Qiming Li

Multi-model fitting is fundamental for robust geometric estimation in computer vision. However, recent deep learning methods enable parallel model detection but rely on simple architectures that inadequately model spatial relationships. Moreover, current methods typically generate hypotheses only through minimal solvers on randomly sampled points, thus failing to explore the full diversity of the solution space. To address these limitations, we propose a novel Jacobian-based Gaussian uncertainty modeling framework, which analytically propagates covariance through geometric transformations and enables efficient expansion of the hypothesis space with strong theoretical guarantees. We further introduce a Gaussian Hypothesis Generation Network (GHG-Net) to learn global parameter distributions, enabling the generation of diverse and geometrically valid hypotheses. Additionally, our network captures spatial relationships among observations by employing a dynamic graph neural network with a multi-head attention mechanism. This yields more accurate sample and inlier weights, significantly improving the quality of hypothesis generation. Extensive experiments on three representative geometric estimation tasks (i.e. vanishing point detection, fundamental matrix estimation, and homography estimation) demonstrate that our method achieves new state-of-the-art accuracy and stability, while maintaining high computational efficiency.

Ke Zhang, Tianyu Ding, Jiachen Jiang, Tianyi Chen, Ilya Zharkov, Vishal M. Patel, Luming Liang

Image cropping is crucial for enhancing the visual appeal and narrative impact of photographs, yet existing rule-based and data-driven approaches often lack diversity or require annotated training data. We introduce ProCrop, a retrieval-based method that leverages professional photography to guide cropping decisions. By fusing features from professional photographs with those of the query image, ProCrop learns from professional compositions, significantly boosting performance. Additionally, we present a large-scale dataset of 242K weakly-annotated images, generated by out-painting professional images and iteratively refining diverse crop proposals. This composition-aware dataset generation offers diverse high-quality crop proposals guided by aesthetic principles and becomes the largest publicly available dataset for image cropping. Extensive experiments show that ProCrop significantly outperforms existing methods in both supervised and weakly-supervised settings. Notably, when trained on the new dataset, our ProCrop surpasses previous weakly-supervised methods and even matches fully supervised approaches.

Jusheng Zhang, Kaitong Cai, Jing Yang, Jian Wang, Chengpei Tang, Keze Wang

Large Vision-Language Models (VLMs) face an inherent contradiction in image captioning: their powerful single-step generation capabilities often lead to a myopic decision-making process. This makes it difficult to maintain global narrative coherence while capturing rich details, a limitation that is particularly pronounced in tasks that require multi-step and complex scene description. To overcome this fundamental challenge, we redefine image captioning as a goal-oriented hierarchical refinement planning problem, and further propose a novel framework, named Top-Down Semantic Refinement (TDSR), which models the generation process as a Markov Decision Process (MDP). However, planning within the vast state space of a VLM presents a significant computational hurdle. Our core contribution, therefore, is the design of a highly efficient Monte Carlo Tree Search (MCTS) algorithm tailored for VLMs. By incorporating a visual-guided parallel expansion and a lightweight value network, our TDSR reduces the call frequency to the expensive VLM by an order of magnitude without sacrificing planning quality. Furthermore, an adaptive early stopping mechanism dynamically matches computational overhead to the image's complexity. Extensive experiments on multiple benchmarks, including DetailCaps, COMPOSITIONCAP, and POPE, demonstrate that our TDSR, as a plug-and-play module, can significantly enhance the performance of existing VLMs (e.g., LLaVA-1.5, Qwen2.5-VL) by achieving state-of-the-art or highly competitive results in fine-grained description, compositional generalization, and hallucination suppression.

Junze Zhang, Luoxi Jing, Yuanyuan Wang, Xueqi Li, Guoli Yang, Songchang Jin, Chunping Qiu

Stereo matching recovers 3D scene information based on the correlation between corresponding pixels. Despite impressive progress, existing methods lack sufficient correlation priors in ill-posed regions such as occlusions, detailed and reflective regions. In this paper, we propose Geometry Aware Stereo Matching Network (GEAStereo) to enhance geometric structure perception and address this issue. We adaptively incorporate the Monocular Disparity Distribution Prior into the stereo cost volume, building Mono-Stereo Fusion Volume (MSFV), which effectively captures global geometric structures and rectifies the correlation information in ill-posed regions. Furthermore, we introduce rich detail information from gradient features and construct a Detail-Aware Volume (DAV) by aggregating the group-wise cost volume under the guidance of gradient spatial attention, thus enhancing the correlation modeling in detailed structures. Jointly, MSFV and DAV provide rich correlation priors for disparity iterative optimization. Experimental results show that our method achieves competitive results on the ETH3D and KITTI2015 benchmarks. Compared with the state-of-the-art methods, our method demonstrates stronger performance in zero-shot generalization.

Junyi Zhang, Yiming Wang, Yunhong Lu, Qichao Wang, Wenzhe Qian, Xiaoyin Xu, David Gu, Min Zhang

A fundamental challenge in text-to-3D face generation is achieving high-quality geometry. The core difficulty lies in the arbitrary and intricate distribution of vertices in 3D space, making it challenging for existing models to establish clean connectivity and resulting in suboptimal geometry. To address this, our core insight is to simplify the underlying geometric structure by constraining the distribution onto a simple and regular manifold, a topological sphere. Building on this, we first propose the Spherical Geometry Representation, a novel face representation that anchors geometric signals to uniform spherical coordinates. This guarantees a regular point distribution, from which the mesh connectivity can be robustly reconstructed. Critically, this canonical sphere can be seamlessly unwrapped into a 2D map, creating a perfect synergy with powerful 2D generative models. We then introduce Spherical Geometry Diffusion, a conditional diffusion framework built upon this 2D map. It enables diverse and controllable generation by jointly modeling geometry and texture, where the geometry explicitly conditions the texture synthesis process. Our method's effectiveness is demonstrated through its success in a wide range of tasks: text-to-3D generation, face reconstruction, and text-based 3D editing. Extensive experiments show that our approach substantially outperforms existing methods in geometric quality, textual fidelity, and inference efficiency.

Junjie Zhang, Feng Zhao, Hanqiang Liu, Jun Yu

The booming remote sensing (RS) technology is giving rise to a novel multimodality generalization task, which requires the model to overcome data heterogeneity while possessing powerful cross-scene generalization ability. Moreover, most vision-language models usually describe surface materials using universal texts, lacking proprietary linguistic prior knowledge specific to different RS modalities. In this work, we formalize RS multimodality generalization (RSMG) as a learning paradigm, and propose a frequency-aware vision-language multimodality generalization network (FVMGN) for RS image classification. Specifically, a diffusion-based training-test-time augmentation (DTAug) strategy is designed to reconstruct multimodal land-cover distributions, enriching input information for FVMGN. Following that, to overcome multimodal heterogeneity, a multimodal wavelet disentanglement (MWDis) module is developed to learn cross-domain invariant features by resampling low and high frequency components in the frequency domain. Considering the characteristics of RS vision modalities, shared and proprietary class texts is designed as linguistic inputs for the transformer-based text encoder to extract diverse text features. For multimodal vision inputs, a spatial-frequency-aware image encoder (SFIE) is constructed to realize local-global feature reconstruction and representation. Finally, a multiscale spatial-frequency feature alignment (MSFFA) module is suggested to construct a unified semantic space, ensuring refined multiscale alignment of different text and vision features in spatial and frequency domains. Extensive experiments show that FVMGN has the excellent multimodality generalization ability compared with state-of-the-art methods.

Jiwei Zhang, Wenbo Feng, Siwei Wang, Feifei Kou, Haoyang Yu, Shaozhang Niu

The proliferation of the tampered images on social media can pose serious societal risks, influencing public opinion and causing panic. Image Manipulation Localization technique has advanced to address this, but some methods focus on microscopic traces, overlooking macroscopic semantics that deceive viewers. To address this problem, we propose a novel Image Manipulation Localization framework called Collaborative Transformers (Co-Transformers), designed to fully explore and utilize the collaborative information between macroscopic semantics and microscopic traces. This framework is based on two Vision Transformer variants. The first variant captures the semantic logic of the image. The second variant delves into microscopic tampering traces. By dynamically fusing these two complementary features, the framework enables interaction between macroscopic semantic inconsistencies and microscopic abnormal traces, effectively coordinating their relationship in the latent space. Furthermore, we introduce a new Multi-Level Forensic Attention (MLF-Attention) mechanism to enhance the model's ability to extract various tampered traces, this mechanism can be integrated into our framework. Compared with existing methods, our proposed framework achieves state-of-the-art results in localization accuracy and shows good robustness against various attacks.

Jing Zhang, Zhikai Li, Chengzhi Hu, Xuewen Liu, Qingyi Gu

Segment Anything Model (SAM) exhibits remarkable zero-shot segmentation capability; however, its prohibitive computational costs make edge deployment challenging. Although post-training quantization (PTQ) offers a promising compression solution, existing methods yield unsatisfactory results when applied to SAM, owing to its specialized model components and promptable workflow: (i) The mask decoder's attention exhibits extreme activation outliers, and we find that aggressive clipping (even 100x), without smoothing or isolation, is effective in suppressing outliers while maintaining performance. Unfortunately, traditional distribution-based metrics (e.g., MSE) fail to provide such large-scale clipping. (ii) Existing quantization reconstruction methods neglect semantic interactivity of SAM, leading to misalignment between image feature and prompt intention. To address the above issues, we propose SAQ-SAM in this paper, which boosts PTQ for SAM from the perspective of semantic alignment. Specifically, we propose Perceptual-Consistency Clipping, which exploits attention focus overlap to promote aggressive clipping while preserving semantic capabilities. Furthermore, we propose Prompt-Aware Reconstruction, which incorporates image-prompt interactions by leveraging cross-attention in mask decoder, thus facilitating alignment in both distribution and semantic. Moreover, to ensure the interaction efficiency, we design a layer-skipping strategy for image tokens in encoder. Extensive experiments are conducted on various SAM sizes and tasks, including instance segmentation, oriented object detection, and semantic segmentation, and the results show that our method consistently exhibits advantages. For example, when quantizing SAM-B to 4-bit, SAQ-SAM achieves 11.7% higher mAP than the baseline in instance segmentation task.

Jialong Zhang, Yachao Zhang, Yao Wu, Jiangming Shi, Fangyong Wang, Yanyun Qu

3D semantic segmentation serves as a fundamental component in many applications, such as autonomous driving and medical image analysis. Although recent methods have advanced the field, adapting these methods to new environments or object categories without extensive retraining remains a significant challenge. To address this, we introduce xMHashSeg, a novel training-free cross-modal LiDAR semantic segmentation framework. xMHashSeg leverages foundation models and non-parametric network to extract features from 2D images and 3D point clouds, subsequently integrating these features through hash learning. Specifically, We develop point-SANN, a novel self-adaption non-parametric network that can extract robust 3D features from raw point clouds, while 2D features are directly extracted through the foundation model DINOv2. To reconcile inconsistencies across different modals, we introduce a Hash Code Learning Module that projects all information into a common hash space, learning a consistent hash code that enhances feature integration. Additionally, depth maps are utilized as an intermediary form between 2D and 3D data to facilitate convergence during hash code learning. Our experimental results on various multi-modality datasets demonstrate that xMHashSeg outperforms zero-shot learning approaches and achieve performance close to that of unsupervised domain adaptation and test-time adaptation methods, without requiring any annotations or additional training.

Hui Zhang, Weikang Gao, Tao Yang, Yuan Cao

With the rapid growth of visual content in open-world environments, zero-shot hashing image retrieval (ZSHIR) has emerged to tackle the challenge of recognizing novel classes using attribute-level and semantic information. However, existing methods often rely on shallow fusion of multi-source cues (e.g., attributes, labels, and visual features) through external supervision or feature concatenation, failing to capture the underlying semantic structure in a generative way. Particularly, current bridging strategies between modalities suffer from information fragmentation and weak alignment, hindering the model's ability to fully understand complex attribute-visual relations. Moreover, subtle semantic gaps or “semantic drift” between seen and unseen classes further degrade inter-class separability and the scalability of hashing models. To address these issues, we propose a novel framework called Proxy Zero-Shot Hashing with Multimodal Fusion via Stable Diffusion (PZSH), which integrates generative modeling and contrastive learning. PZSH leverages a pre-trained Stable Diffusion (SD) model to synthesize multimodal content, and uses dual BLIP encoders to enhance semantic alignment across modalities. We further design a proxy hashing loss to enforce discriminative binary representations. Extensive experiments on benchmark datasets show that PZSH achieves state-of-the-art performance with stronger generalization to unseen classes.

Hongyu Zhang, Haipeng Chen, Chengxin Yang, Yingda Lyu

Progress in medical image segmentation is fundamentally constrained by the scarcity of annotated data. While diffusion models offer a promising solution by generating high-fidelity image–mask pairs, their utility for downstream tasks remains underexplored. A key bottleneck lies in the misalignment between generation outputs and task-specific needs—samples are produced independently of their utility for downstream training. To this end, we propose Value-Guided Diffusion (VGD), a lightweight sampling framework that integrates downstream model feedback into the generative inference process. VGD estimates a value score for each sample based on its utility to downstream training, and leverages this signal to iteratively guide the denoising trajectory toward high-reward regions of the data manifold. Crucially, VGD can be seamlessly integrated into existing medical diffusion models without any additional training or architectural modifications. Extensive experiments across multiple diffusion backbones and segmentation benchmarks demonstrate that VGD significantly boosts downstream segmentation performance while maintaining visual fidelity. Our findings highlight a task-aware sampling principle with potential to underpin future synthetic segmentation pipelines.

Hong Zhang, Yixuan Lyu, Hanyang Liu, Jianbo Song, Ding Yuan, Yifan Yang

Video Camouflaged Object Detection (VCOD) poses significant challenges due to the subtle appearance of camouflaged objects, especially under dynamic motion and occlusion. Existing methods predominantly rely on optical flow or black-box features for motion modeling, which often entail substantial computational costs and suffer from limited interpretability. Inspired by the human strategy of identifying abnormal movements between frames and the principle of event camera image formation, we propose an eventstream-inspired dual-branch framework for VCOD. Specifically, we design an eventstream-like data extraction module to capture pixel-level motion variations, effectively distinguishing object motion from background dynamics. This event-based representation is integrated into SAM2 through a dual-branch memory-augmented framework, consisting of Time Bridge Attention and Visual Bridge Attention, enabling joint modeling of motion and appearance cues. In addition, we introduce a Prompt Embedding Generator to eliminate the need for human-provided interactive prompts, facilitating fully automatic VCOD. Extensive experiments on MoCA-Mask and CAD2016 demonstrate that our approach significantly outperforms state-of-the-art methods, achieving both superior segmentation accuracy and interpretable motion modeling. To the best of our knowledge, this is the first work to incorporate eventstream-inspired representations into the VCOD task.

Haoyu Zhang, Qiaohui Chu, Meng Liu, Haoxiang Shi, Yaowei Wang, Liqiang Nie

AI personal assistants, deployed through robots or wearables, require embodied understanding to collaborate effectively with humans. However, current Multimodal Large Language Models (MLLMs) primarily focus on third-person (exocentric) vision, overlooking the unique challenges of first-person (egocentric) videos. Additionally, high acquisition costs limit data size, impairing MLLM performance. To address these challenges, we propose learning the mapping between exocentric and egocentric domains, leveraging the extensive exocentric knowledge within existing MLLMs to enhance egocentric video understanding. To this end, we introduce Ego-ExoClip, a pre-training dataset comprising 1.1M synchronized ego-exo clip-text pairs derived from Ego-Exo4D, together with the instruction-tuning dataset EgoIT, which is collected from multiple sources to enhance the model's instruction-following capabilities. Building upon the datasets, we propose a migration strategy and further design a progressive mapping learning pipeline with three stages: Demonstrator Self-Preparation, Demonstrator-Learner Guidance, and Learner Self-Practice. Extensive experiments across diverse egocentric tasks reveal that existing MLLMs perform inadequately in egocentric video understanding, while our model significantly outperforms these leading models.

Haowei Zhang, Yuanpei Zhao, Ji-Zhe Zhou, Mao Li

Improving the diversity of generated results while maintaining high visual quality remains a significant challenge in image generation tasks. Fractal Generative Models (FGMs) are efficient in generating high-quality images, but their inherent self-similarity limits the diversity of output images. To address this issue, we propose a novel approach based on the Hausdorff Dimension (HD), a widely recognized concept in fractal geometry used to quantify structural complexity, which aids in enhancing the diversity of generated outputs. To incorporate HD into FGM, we propose a learnable HD estimation method that predicts HD directly from image embeddings, addressing computational cost concerns. However, simply introducing HD into a hybrid loss is insufficient to enhance diversity in FGMs due to: 1) degradation of image quality, and 2) limited improvement in generation diversity. To this end, during training, we adopt an HD-based loss with a monotonic momentum-driven scheduling strategy to progressively optimize the hyperparameters, obtaining optimal diversity without sacrificing visual quality. Moreover, during inference, we employ HD-guided rejection sampling to select geometrically richer outputs. Extensive experiments on the ImageNet dataset demonstrate that our FGM-HD framework yields a 39% improvement in output diversity compared to vanilla FGMs, while preserving comparable image quality. To our knowledge, this is the very first work introducing HD into FGM. Our method effectively enhances the diversity of generated outputs while offering a principled theoretical contribution to FGM development.