GenAI4ELab/papercli-papers huggingface dataset:
Efficient Personalization of Quantized Diffusion Model without Backpropagation
Authors: Hoigi Seo; Wongi Jeong; Kyungryeol Lee; Se Young Chun
Venue: CVPR 2025 (main)

Abstract: Diffusion models have shown remarkable performance in image synthesis, but they demand extensive computational and memory resources for training, fine-tuning and inference. Although advanced quantization techniques have successfully minimized memory usage for inference, training and fine-tuning these quantized models still require large memory possibly due to dequantization for accurate computation of gradients and/or backpropagation for gradient-based algorithms. However, memory-efficient fine-tuning is particularly desirable for applications such as personalization that often must be run on edge devices like mobile phones with private data. In this work, we address this challenge by quantizing a diffusion model with personalization via Textual Inversion and by leveraging a zeroth-order optimization on personalization tokens without dequantization so that it does not require gradient and activation storage for backpropagation that consumes considerable memory. Since a gradient estimation using zeroth-order optimization is quite noisy for a single or a few images in personalization, we propose to denoise the estimated gradient by projecting it onto a subspace that is constructed with the past history of the tokens, dubbed Subspace Gradient. In addition, we investigated the influence of text embedding in image generation, leading to our proposed time steps sampling, dubbed Partial Uniform Timestep Sampling for sampling with effective diffusion timesteps. Our method achieves comparable performance to prior methods in image and text alignment scores for personalizing Stable Diffusion with only forward passes while reducing training memory demand up to 8.2x. Project page: https://ignoww.github.io/ZOODiP_project/
Alias-Free Latent Diffusion Models: Improving Fractional Shift Equivariance of Diffusion Latent Space
Authors: Yifan Zhou; Zeqi Xiao; Shuai Yang; Xingang Pan
Venue: CVPR 2025 (main)

Abstract: Latent Diffusion Models (LDMs) are known to have an unstable generation process, where even small perturbations or shifts in the input noise can lead to significantly different outputs. This hinders their applicability in applications requiring consistent results. In this work, we redesign LDMs to enhance consistency by making them shift-equivariant. While introducing anti-aliasing operations can partially improve shift-equivariance, significant aliasing and inconsistency persist due to the unique challenges in LDMs, including 1) aliasing amplification during VAE training and multiple U-Net inferences, and 2) self-attention modules that inherently lack shift-equivariance. To address these issues, we redesign the attention modules to be shift-equivariant and propose an equivariance loss that effectively suppresses the frequency bandwidth of the features in the continuous domain. The resulting alias-free LDM (AF-LDM) achieves strong shift-equivariance and is also robust to irregular warping. Extensive experiments demonstrate that AF-LDM produces significantly more consistent results than vanilla LDM across various applications, including video editing and image-to-image translation.
A Unified Latent Schrodinger Bridge Diffusion Model for Unsupervised Anomaly Detection and Localization
Authors: Shilhora Akshay; Niveditha Lakshmi Narasimhan; Jacob George; Vineeth N Balasubramanian
Venue: CVPR 2025 (main)

Abstract: Anomaly detection and localization remain pivotal challenges in computer vision, with applications ranging from industrial inspection to medical diagnostics. While current supervised methods offer high precision, they are often impractical due to the scarcity of annotated data and the infrequent occurrence of anomalies. Recent advancements in unsupervised approaches, particularly reconstruction-based methods, have addressed these issues by training models exclusively on normal data, enabling them to identify anomalies during inference. However, these methods frequently rely on auxiliary networks or specialized adaptations, which can limit their robustness and practicality. This work introduces the Latent Anomaly Schrodinger Bridge (LASB), a unified unsupervised anomaly detection model that operates entirely in the latent space without requiring additional networks or custom modifications. LASB transforms anomaly images into normal images by preserving structural integrity across varying anomaly classes, lighting, and pose conditions, making it highly robust and versatile. Unlike previous methods, LASB does not focus solely on reconstructing anomaly features, but emphasizes anomaly transformation, achieving smooth anomaly-to-normal image conversions. Our method achieves state-of-the-art performance on both the MVTec-AD and VisA datasets, excelling in detection and localization tasks.
KVQ: Boosting Video Quality Assessment via Saliency-guided Local Perception
Authors: Yunpeng Qu; Kun Yuan; Qizhi Xie; Ming Sun; Chao Zhou; Jian Wang
Venue: CVPR 2025 (main)

Abstract: Video Quality Assessment (VQA), which intends to predict the perceptual quality of videos, has attracted increasing attention. Due to factors like motion blur or specific distortions, the quality of different regions in a video varies. Recognizing the region-wise local quality within a video is beneficial for assessing global quality and can guide us in adopting fine-grained enhancement or transcoding strategies. Due to the heavy cost of annotating region-wise quality, the lack of ground truth constraints from relevant datasets further complicates the utilization of local perception. Inspired by the Human Visual System (HVS) that links global quality to the local texture of different regions and their visual saliency, we propose a Kaleidoscope Video Quality Assessment (KVQ) framework, which aims to effectively assess both saliency and local texture, thereby facilitating the assessment of global quality. Our framework extracts visual saliency and allocates attention using Fusion-Window Attention (FWA) while incorporating a Local Perception Constraint (LPC) to mitigate the reliance of regional texture perception on neighboring areas. KVQ obtains significant improvements across multiple scenarios on five VQA benchmarks compared to SOTA methods. Furthermore, to assess local perception, we establish a new Local Perception Visual Quality (LPVQ) dataset with region-wise annotations. Experimental results demonstrate the capability of KVQ in perceiving local distortions. KVQ models and the LPVQ dataset will be available at https://github.com/qyp2000/KVQ.
MambaVision: A Hybrid Mamba-Transformer Vision Backbone
Authors: Ali Hatamizadeh; Jan Kautz
Venue: CVPR 2025 (main)

Abstract: We propose a novel hybrid Mamba-Transformer backbone, MambaVision, specifically tailored for vision applications. Our core contribution includes redesigning the Mamba formulation to enhance its capability for efficient modeling of visual features. Through a comprehensive ablation study, we demonstrate the feasibility of integrating Vision Transformers (ViT) with Mamba. Our results show that equipping the Mamba architecture with self-attention blocks in the final layers greatly improves its capacity to capture long-range spatial dependencies. Based on these findings, we introduce a family of MambaVision models with a hierarchical architecture to meet various design criteria. For classification on the ImageNet-1K dataset, MambaVision variants achieve state-of-the-art (SOTA) performance in terms of both Top-1 accuracy and throughput. In downstream tasks such as object detection, instance segmentation, and semantic segmentation on MS COCO and ADE20K datasets, MambaVision outperforms comparably sized backbones while demonstrating favorable performance. Code: https://github.com/NVlabs/MambaVision
Learning Flow Fields in Attention for Controllable Person Image Generation
Authors: Zijian Zhou; Shikun Liu; Xiao Han; Haozhe Liu; Kam Woh Ng; Tian Xie; Yuren Cong; Hang Li; Mengmeng Xu; Juan-Manuel Perez-Rua; Aditya Patel; Tao Xiang; Miaojing Shi; Sen He
Venue: CVPR 2025 (main)

Abstract: Controllable person image generation aims to generate a person image conditioned on reference images, allowing precise control over the person's appearance or pose.However, prior methods often distort fine-grained textural details from the reference image, despite achieving high overall image quality.We attribute these distortions to inadequate attention to corresponding regions in the reference image.To address this, we thereby propose learning flow fields in attention (Leffa), which explicitly guides the target query to attend to the correct reference key in the attention layer during training.Specifically, it is realized via a regularization loss on top of the attention map within a diffusion-based baseline.Our extensive experiments show that Leffa achieves state-of-the-art performance in controlling appearance (virtual try-on) and pose (pose transfer), significantly reducing fine-grained detail distortion while maintaining high image quality.Additionally, we show that our loss is model-agnostic and can be used to improve the performance of other diffusion models.
Multi-Label Prototype Visual Spatial Search for Weakly Supervised Semantic Segmentation
Authors: Songsong Duan; Xi Yang; Nannan Wang
Venue: CVPR 2025 (main)

Abstract: Existing Weakly Supervised Semantic Segmentation (WSSS) relies on the CNN-based Class Activation Map (CAM) and Transformer-based self-attention map to generate class-specific masks for semantic segmentation. However, CAM and self-attention maps usually cause incomplete segmentation due to classification bias issue. To address this issue, we propose a Multi-Label Prototype Visual Spatial Search (MuP-VSS) method with a spatial query mechanism, which learns a set of learnable class token vectors as queries to search the similarity visual tokens from image patch tokens. Specifically, MuP-VSS consists of two key components: multi-label prototype representation and multi-label prototype optimization. The former designs a global embedding to learn the global tokens from the images, and then proposes a Prototype Embedding Module (PEM) to interact with patch tokens to understand the local semantic information. The latter utilizes the exclusivity and consistency principles of the multi-label prototypes to design three prototype losses to optimize them, which contain cross-class prototype (CCP) contrastive loss, cross-image prototype (CIP) contrastive loss, and patch-to-prototype (P2P) consistency loss. CCP loss models exclusivity of multi-label prototypes learned from a single image to enhance the discriminative properties of each class better. CCP loss learns the consistency of the same class-specific prototypes extracted from multiple images to enhance the semantic consistency. P2P loss is proposed to control the semantic response of the prototype to the image patches. Experimental results on Pascal VOC 2012 and MS COCO show that MuP-VSS significantly outperforms recent methods and achieves state-of-the-art performance.
Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training
Authors: Lexington Whalen; Zhenbang Du; Haoran You; Chaojian Li; Sixu Li; Yingyan Lin
Venue: CVPR 2025 (main)

Abstract: Training diffusion models (DMs) requires substantial computational resources due to multiple forward and backward passes across numerous timesteps, motivating research into efficient training techniques. In this paper, we propose EB-Diff-Train, a new efficient DM training approach that is orthogonal to other methods of accelerating DM training, by investigating and leveraging Early-Bird (EB) tickets--sparse subnetworks that manifest early in the training process and maintain high generation quality. We first investigate the existence of traditional EB tickets in DMs, enabling competitive generation quality without fully training a dense model. Then, we delve into the concept of diffusion-dedicated EB tickets, drawing on insights from varying importance of different timestep regions. These tickets adapt their sparsity levels according to the importance of corresponding timestep regions, allowing for aggressive sparsity during non-critical regions while conserving computational resources for crucial timestep regions. Building on this, we develop an efficient DM training technique that derives timestep-aware EB tickets, trains them in parallel, and combines them during inference for image generation.  Extensive experiments validate the existence of both traditional and timestep-aware EB tickets, as well as the effectiveness of our proposed EB-Diff-Train method. This approach can significantly reduce training time both spatially and temporally--achieving 2.9x 5.8x speedups over training unpruned dense models, and up to 10.3x faster training compared to standard train-prune-finetune pipelines--without compromising generative quality. Our code is available at https://github.com/GATECH-EIC/Early-Bird-Diffusion.
FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model
Authors: Jun Zhou; Jiahao Li; Zunnan Xu; Hanhui Li; Yiji Cheng; Fa-Ting Hong; Qin Lin; Qinglin Lu; Xiaodan Liang
Venue: CVPR 2025 (main)

Abstract: Currently, instruction-based image editing methods have made significant progress by leveraging the powerful cross-modal understanding capabilities of visual language models (VLMs). However, they still face challenges in three key areas: 1) complex scenarios; 2) semantic consistency; and 3) fine-grained editing. To address these issues, we propose FireEdit, an innovative Fine-grained Instruction-based image editing framework that exploits a REgion-aware VLM. FireEdit is designed to accurately comprehend user instructions and ensure effective control over the editing process. Specifically, we enhance the fine-grained visual perception capabilities of the VLM by introducing additional region tokens. Relying solely on the output of the LLM to guide the diffusion model may lead to suboptimal editing results. Therefore, we propose a Time-Aware Target Injection module and a Hybrid Visual Cross Attention module. The former dynamically adjusts the guidance strength at various denoising stages by integrating timestep embeddings with the text embeddings. The latter enhances visual details for image editing, thereby preserving semantic consistency between the edited result and the source image. By combining the VLM enhanced with fine-grained region tokens and the time-dependent diffusion model, FireEdit demonstrates significant advantages in comprehending editing instructions and maintaining high semantic consistency. Extensive experiments indicate that our approach surpasses the state-of-the-art instruction-based image editing methods.
Doppelgangers++: Improved Visual Disambiguation with Geometric 3D Features
Authors: Yuanbo Xiangli; Ruojin Cai; Hanyu Chen; Jeffrey Byrne; Noah Snavely
Venue: CVPR 2025 (main)

Abstract: Accurate 3D reconstruction is frequently hindered by visual aliasing, where visually similar but distinct surfaces (aka, doppelgangers), are incorrectly matched. These spurious matches distort the structure-from-motion (SfM) process, leading to misplaced model elements and reduced accuracy. Prior efforts addressed this with CNN classifiers trained on curated datasets, but these approaches struggle to generalize across diverse real-world scenes and can require extensive parameter tuning. In this work, we present Doppelgangers++, a method to enhance doppelganger detection and improve 3D reconstruction accuracy. Our contributions include a diversified training dataset that incorporates geo-tagged images from everyday scenes to expand robustness beyond landmark-based datasets. We further propose a Transformer-based classifier that leverages 3D-aware features from the MASt3R model, achieving superior precision and recall across both in-domain and out-of-domain tests. Doppelgangers++ integrates seamlessly into standard SfM and MASt3R-SfM pipelines, offering efficiency and adaptability across varied scenes. To evaluate SfM accuracy, we introduce an automated, geotag-based method for validating reconstructed models, eliminating the need for manual inspection. Through extensive experiments, we demonstrate that Doppelgangers++ significantly enhances pairwise visual disambiguation and improves 3D reconstruction quality in complex and diverse scenarios.
Learnable Infinite Taylor Gaussian for Dynamic View Rendering
Authors: Bingbing Hu; Yanyan Li; Rui Xie; Bo Xu; Haoye Dong; Junfeng Yao; Gim Hee Lee
Venue: CVPR 2025 (main)

Abstract: Capturing the temporal evolution of Gaussian properties such as position, rotation, and scale is a challenging task due to the vast number of time-varying parameters and the limited photometric data available, which generally results in convergence issues, making it difficult to find an optimal solution. While feeding all inputs into an end-to-end neural network can effectively model complex temporal dynamics, this approach lacks explicit supervision and struggles to generate high-quality transformation fields. On the other hand, using time-conditioned polynomial functions to model Gaussian trajectories and orientations provides a more explicit and interpretable solution, but requires significant handcrafted effort and lacks generalizability across diverse scenes. To overcome these limitations, this paper introduces a novel approach based on a learnable infinite Taylor Formula to model the temporal evolution of Gaussians. This method offers both the flexibility of an implicit network-based approach and the interpretability of explicit polynomial functions, allowing for more robust and generalizable modeling of Gaussian dynamics across various dynamic scenes.Extensive experiments on dynamic novel view rendering tasks are conducted on public datasets, demonstrating that the proposed method achieves state-of-the-art performance in this domain.
DL2G: Degradation-guided Local-to-Global Restoration for Eyeglass Reflection Removal
Authors: Zhilv Yi; Xiao Lu; Hong Ding; Jingbo Hu; Zhi Jiang; Chunxia Xiao
Venue: CVPR 2025 (main)

Abstract: Eyeglass reflection removal can restore the texture information in the reflection destructed eye area, which is meaningful for various tasks on the facial images. It is still challenging to correctly eliminate reflections, reasonably restore the lost contents, and guarantee that the final result has a consistent color and illumination with the input image. In this paper, we introduce a Degradation-guided Local-to-Global (DL2G) restoration framework to address this problem. We first propose a multiplicative reflection degradation model, which is used to alleviate reflection degradation to obtain a preliminary result. Then, in the local details restoration stage, we propose a local structure-aware diffusion model to learn the true distribution of texture details in the eye area. This helps in recovering lost contents in the regions of heavy degradation where the background is invisible. Finally, in the global consistency refinement stage, we utilize the input image as a reference image to generate the final result that is consistent with the input image in color and illumination. Extensive experiments demonstrate that our method can improve the effect of reflection removal and generate results with more reasonable semantics, exquisite details, and harmonious illumination.
DIV-FF: Dynamic Image-Video Feature Fields For Environment Understanding in Egocentric Videos
Authors: Lorenzo Mur-Labadia; Josechu Guerrero; Ruben Martinez-Cantin
Venue: CVPR 2025 (main)

Abstract: Environment understanding in egocentric videos is an important step for applications like robotics, augmented reality and assistive technologies. These videos are characterized by dynamic interactions and a strong dependence on the wearer's engagement with the environment. Traditional approaches often focus on isolated clips or fail to integrate rich semantic and geometric information, limiting scene comprehension. We introduce Dynamic Image-Video Feature Fields (DIV-FF), a framework that decomposes the egocentric scene into persistent, dynamic, and actor-based components while integrating both image and video-language features. Our model enables detailed segmentation, captures affordances, understands the surroundings and maintains consistent understanding over time. DIV-FF outperforms state-of-the-art methods, particularly in dynamically evolving scenarios, demonstrating its potential to advance long-term, spatio-temporal scene understanding.
SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer
Authors: Hongda Liu; Longguang Wang; Ye Zhang; Ziru Yu; Yulan Guo
Venue: CVPR 2025 (main)

Abstract: Global effective receptive field plays a crucial role for image style transfer (ST) to obtain high-quality stylized results. However, existing ST backbones (e.g., CNNs and Transformers) suffer huge computational complexity to achieve global receptive fields. Recently, the State Space Model (SSM), especially the improved variant Mamba, has shown great potential for long-range dependency modeling with linear complexity, which offers a approach to resolve the above dilemma. In this paper, we develop a Mamba-based style transfer framework, termed SaMam. Specifically, a mamba encoder is designed to efficiently extract content and style information. In addition, a style-aware mamba decoder is developed to flexibly adapt to various styles. Moreover, to address the problems of local pixel forgetting, channel redundancy and spatial discontinuity of existing SSMs, we introduce both local enhancement and zigzag scan. Qualitative and quantitative results demonstrate that our SaMam outperforms state-of-the-art methods in terms of both accuracy and efficiency.
MFogHub: Bridging Multi-Regional and Multi-Satellite Data for Global Marine Fog Detection and Forecasting
Authors: Mengqiu Xu; Kaixin Chen; Heng Guo; Yixiang Huang; Ming Wu; Zhenwei Shi; Chuang Zhang; Jun Guo
Venue: CVPR 2025 (main)

Abstract: Deep learning approaches for marine fog detection and forecasting have outperformed traditional methods, demonstrating significant scientific and practical importance. However, the limited availability of open-source datasets remains a major challenge. Existing datasets, often focused on a single region or satellite, restrict the ability to evaluate model performance across diverse conditions and hinder the exploration of intrinsic marine fog characteristics. To address these limitations, we introduce MFogHub, the first multi-regional and multi-satellite dataset to integrate annotated marine fog observations from 15 coastal fog-prone regions and six geostationary satellites, comprising over 68,000 high-resolution samples. By encompassing diverse regions and satellite perspectives, MFogHub facilitates rigorous evaluation of both detection and forecasting methods under varying conditions. Extensive experiments with 16 baseline models demonstrate that MFogHub can reveal generalization fluctuations due to regional and satellite discrepancy, while also serving as a valuable resource for the development of targeted and scalable fog prediction techniques. Through MFogHub, we aim to advance both the practical monitoring and scientific understanding of marine fog dynamics on a global scale. The dataset and code are available in the supplementary materials.