GenAI4ELab/papercli-papers huggingface dataset:
SAUCE: Selective Concept Unlearning in Vision-Language Models with Sparse Autoencoders
Authors: Jiahui Geng; Qing Li
Venue: ICCV 2025 (main)

Abstract: Unlearning methods for vision-language models (VLMs) have primarily adapted techniques from large language models (LLMs), relying on weight updates that demand extensive annotated forget sets. Moreover, these methods perform unlearning at a coarse granularity, often leading to excessive forgetting and reduced model utility. To address this issue, we introduce SAUCE, a novel method that leverages sparse autoencoders (SAEs) for fine-grained and selective concept unlearning in VLMs. Briefly, SAUCE first trains SAEs to capture high-dimensional, semantically rich sparse features. It then identifies the features most relevant to the target concept for unlearning. During inference, it selectively modifies these features to suppress specific concepts while preserving unrelated information. We evaluate SAUCE on two distinct VLMs, LLaVA-v1.5-7B and LLaMA-3.2-11B-Vision-Instruct, across two types of tasks: concrete concept unlearning (objects and sports scenes) and abstract concept unlearning (emotions, colors, and materials), encompassing a total of 60 concepts. Extensive experiments demonstrate that SAUCE outperforms state-of-the-art methods by 18.04% in unlearning quality while maintaining comparable model utility. Furthermore, we investigate SAUCE's robustness against widely used adversarial attacks, its transferability across models, and its scalability in handling multiple simultaneous unlearning requests. Our findings establish SAUCE as an effective and scalable solution for selective concept unlearning in VLMs.
ForCenNet: Foreground-Centric Network for Document Image Rectification
Authors: Peng Cai; Qiang Li; Kaicheng Yang; Dong Guo; Jia Li; Nan Zhou; Xiang An; Ninghua Yang; Jiankang Deng
Venue: ICCV 2025 (main)

Abstract: Document image rectification aims to eliminate geometric deformation in photographed documents to facilitate text recognition. However, existing methods often neglect the significance of foreground elements, which provide essential geometric references and layout information for document image correction. In this paper, we introduce Foreground-Centric Network(ForCenNet) to eliminate geometric distortions in document images. Specifically, we initially propose a foreground-centric label generation method, which extracts detailed foreground elements from an undistorted image. Then we introduce a foreground-centric mask mechanism to enhance the distinction between readable and background regions. Furthermore, we design a curvature consistency loss to leverage the detailed foreground labels to help the model understand the distorted geometric distribution. Extensive experiments demonstrate that ForCenNet achieves new state-of-the-art on four real-world benchmarks, such as DocUNet, DIR300, WarpDoc, and DocReal. Quantitative analysis shows that the proposed method effectively undistorts layout elements, such as text lines and table borders. The resources for further comparison are provided at https://github.com/caipeng328/ForCenNet.
Adversarial Data Augmentation for Single Domain Generalization via Lyapunov Exponent-Guided Optimization
Authors: Zuyu Zhang; Ning Chen; Yongshan Liu; Qinghua Zhang; Xu Zhang
Venue: ICCV 2025 (main)

Abstract: Single Domain Generalization (SDG) aims to develop models capable of generalizing to unseen target domains using only one source domain, a task complicated by substantial domain shifts and limited data diversity. Existing SDG approaches primarily rely on data augmentation techniques, which struggle to effectively adapt training dynamics to accommodate large domain shifts. To address this, we propose LEAwareSGD, a novel Lyapunov Exponent (LE)-guided optimization approach inspired by dynamical systems theory. By leveraging LE measurements to modulate the learning rate, LEAwareSGD encourages model training near the edge of chaos, a critical state that optimally balances stability and adaptability. This dynamic adjustment allows the model to explore a wider parameter space and capture more generalizable features, ultimately enhancing the model's generalization capability. Extensive experiments on PACS, OfficeHome, and DomainNet demonstrate that LEAwareSGD yields substantial generalization gains, achieving up to 9.47% improvement on PACS in low-data regimes. These results underscore the effectiveness of training near the edge of chaos for enhancing model generalization capability in SDG tasks.
Always Skip Attention
Authors: Yiping Ji; Hemanth Saratchandran; Peyman Moghadam; Simon Lucey
Venue: ICCV 2025 (main)

Abstract: We highlight a curious empirical result within modern Vision Transformers (ViTs). Specifically, self-attention catastrophically fails to train unless it is used in conjunction with a skip connection. This is in contrast to other elements of a ViT that continue to exhibit good performance (albeit suboptimal) when skip connections are removed. Further, we show that this critical dependence on skip connections is a relatively new phenomenon, with previous deep architectures (e.g., CNNs) exhibiting good performance in their absence. In this paper, we theoretically characterize that the self-attention mechanism is fundamentally ill-conditioned and is, therefore, uniquely dependent on skip connections for regularization. Additionally, we propose Token Graying (TG), a simple yet effective complement (to skip connections) that further improves the condition of in- put tokens. We validate our approach in both supervised and self-supervised training methods
ExCap3D: Expressive 3D Scene Understanding via Object Captioning with Varying Detail
Authors: Chandan Yeshwanth; Dávid Rozenberszki; Angela Dai
Venue: ICCV 2025 (main)

Abstract: Generating text descriptions of objects in 3D indoor scenes is an important building block of embodied understanding. Existing methods do this by describing objects at a single level of detail, which often does not capture fine-grained details such as varying textures, materials, and shapes of the parts of objects.We propose the task of expressive 3D captioning: given an input 3D scene, describe objects at multiple levels of detail: a high-level object description, and a low-level description of the properties of its parts.To produce such captions, we present ExCap3D, an expressive 3D captioning model which takes as input a 3D scan, and for each detected object in the scan, generates a fine-grained collective description of the parts of the object, along with an object-level description conditioned on the part-level description.We design ExCap3D to encourage semantic consistency between the generated text descriptions, as well as textual similarity in the latent space, to further increase the quality of the generated captions.To enable this task, we generated the ExCap3D Dataset by leveraging a visual-language model (VLM) for multi-view captioning. ExCap3D Dataset contains captions on the ScanNet++ dataset with varying levels of detail,comprising 190k text descriptions of 34k 3D objects in 947 indoor scenes.Our experiments show that the object- and part-level of detail captions generated by ExCap3D are of higher quality than those produced by state-of-the-art methods, with a Cider score improvement of 17% and 126% for object- and part-level details respectively. Our code, dataset and models will be made publicly available.
CMAD: Correlation-Aware and Modalities-Aware Distillation for Multimodal Sentiment Analysis with Missing Modalities
Authors: Yan Zhuang; Minhao Liu; Wei Bai; Yanru Zhang; Xiaoyue Zhang; Jiawen Deng; Fuji Ren
Venue: ICCV 2025 (main)

Abstract: Multimodal Sentiment Analysis (MSA) enhances emotion recognition by integrating information from multiple modalities. However, multimodal learning with missing modalities suffers from representation inconsistency and optimization instability, leading to suboptimal performance. In this paper, we introduce Correlation-Aware and Modalities-Aware Distillation (CMAD), a unified framework designed for MSA under varying missing-modality conditions. Specifically, CMAD comprises two key components: (1) Correlation-Aware Feature Distillation (CAFD), which enforces multi-level representation alignment by preserving both feature similarities and high-order correlation structures between teacher and student models, and (2) Modality-Aware Regularization (MAR) employs an adaptive weighting strategy guided by modality difficulty, enabling a curriculum learning paradigm to stabilize the training process. Extensive evaluations on five datasets show that CMAD consistently outperforms existing methods, achieving average performance improvements of 1.0% on MOSEI, 4.4% on IEMOCAP, 1.9% on MUStARD, 0.5% on UR-FUNNY and 1.9% on CHERMA. Code is available at: https://github.com/YetZzzzzz/CMAD.
Describe, Don't Dictate: Semantic Image Editing with Natural Language Intent
Authors: En Ci; Shanyan Guan; Yanhao Ge; Yilin Zhang; Wei Li; Zhenyu Zhang; Jian Yang; Ying Tai
Venue: ICCV 2025 (main)

Abstract: Despite the progress in text-to-image generation, semantic image editing remains a challenge. Inversion-based algorithms unavoidably introduce reconstruction errors, while instruction-based models mainly suffer from limited dataset quality and scale. To address these problems, we propose a descriptive-prompt-based editing framework, named DescriptiveEdit. The core idea is to re-frame `instruction-based image editing' as `reference-image-based text-to-image generation', which preserves the generative power of well-trained Text-to-Image models without architectural modifications or inversion. Specifically, taking the reference image and a prompt as input, we introduce a Cross-Attentive UNet, which newly adds attention bridges to inject reference image features into the prompt-to-edit-image generation process. Owing to its text-to-image nature, DescriptiveEdit overcomes limitations in instruction dataset quality, integrates seamlessly with ControlNet, IP-Adapter, and other extensions, and is more scalable. Experiments on the Emu Edit benchmark show it improves editing accuracy and consistency.
LiT: Delving into a Simple Linear Diffusion Transformer for Image Generation
Authors: Jiahao Wang; Ning Kang; Lewei Yao; Mengzhao Chen; Chengyue Wu; Songyang Zhang; Shuchen Xue; Yong Liu; Taiqiang Wu; Xihui Liu; Kaipeng Zhang; Shifeng Zhang; Wenqi Shao; Zhenguo Li; Ping Luo
Venue: ICCV 2025 (main)

Abstract: In this paper, we investigate how to convert a pre-trained Diffusion Transformer (DiT) into a linear DiT, as its simplicity, parallelism, and efficiency for image generation. Through detailed exploration, we offer a suite of ready-to-use solutions, ranging from linear attention design to optimization strategies. Our core contributions include 5 practical guidelines: 1) Applying depth-wise convolution within simple linear attention is sufficient for image generation. 2) Using fewer heads in linear attention provides a free-lunch performance boost without increasing latency. 3) Inheriting weights from a fully converged, pre-trained DiT. 4) Loading all parameters except those related to linear attention. 5) Hybrid knowledge distillation: using a pre-trained teacher DiT to help the training of the student linear DiT, supervising not only the predicted noise but also the variance of the reverse diffusion process. These guidelines lead to our proposed Linear Diffusion Transformer (LiT), which serves as a safe and efficient alternative baseline for DiT with pure linear attention. In class-conditional 256x256 and 512x512 ImageNet generation, LiT can be quickly adapted from DiT using only 20% and 33% of DiT's training steps, respectively, while achieving comparable performance. LiT also rivals methods based on Mamba or Gated Linear Attention. Moreover, the same guidelines generalize to text-to-image generation: LiT can be swiftly converted from PixArt-S to generate high-quality images, maintaining comparable GenEval scores.
LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity
Authors: Walid Bousselham; Angie Boggust; Sofian Chaybouti; Hendrik Strobelt; Hilde Kuehne
Venue: ICCV 2025 (main)

Abstract: Vision Transformers (ViTs) have become a standard architecture in computer vision. However, because of their modeling of long-range dependencies through self-attention mechanisms, the explainability of these models remains a challenge. To address this, we propose LeGrad, an explainability method specifically designed for ViTs. LeGrad computes the gradient with respect to the attention maps of single ViT layers, considering the gradient itself as the explainability signal. We aggregate the signal over all layers, combining the activations of the last as well as intermediate tokens to produce the merged explainability map. This makes LeGrad a conceptually simple and an easy-to-implement method to enhance the transparency of ViTs. We evaluate LeGrad in various setups, including segmentation, perturbation, and open-vocabulary settings, showcasing its improved spatial fidelity and its versatility compared to other SotA explainability methods. A demo and the code is available at https://walidbousselham.com/LeGrad
GaussianFlowOcc: Sparse and Weakly Supervised Occupancy Estimation using Gaussian Splatting and Temporal Flow
Authors: Simon Boeder; Fabian Gigengack; Benjamin Risse
Venue: ICCV 2025 (main)

Abstract: Occupancy estimation has become a prominent task in 3D computer vision, particularly within the autonomous driving community. In this paper, we present a novel approach to occupancy estimation, termed GaussianFlowOcc, which is inspired by Gaussian Splatting and replaces traditional dense voxel grids with a sparse 3D Gaussian representation. Our efficient model architecture based on a Gaussian Transformer significantly reduces computational and memory requirements by eliminating the need for expensive 3D convolutions used with inefficient voxel-based representations that predominantly represent empty 3D spaces. GaussianFlowOcc effectively captures scene dynamics by estimating temporal flow for each Gaussian during the overall network training process, offering a straightforward solution to a complex problem that is often neglected by existing methods. Moreover, GaussianFlowOcc is designed for scalability, as it employs weak supervision and does not require costly dense 3D voxel annotations based on additional data (e.g., LiDAR). Through extensive experimentation, we demonstrate that GaussianFlowOcc significantly outperforms all previous methods for weakly supervised occupancy estimation on the nuScenes dataset while featuring an inference speed that is 50 times faster than current SOTA.
DreamLayer: Simultaneous Multi-Layer Generation via Diffusion Model
Authors: Junjia Huang; Pengxiang Yan; Jinhang Cai; Jiyang Liu; Zhao Wang; Yitong Wang; Xinglong Wu; Guanbin Li
Venue: ICCV 2025 (main)

Abstract: Text-driven image generation using diffusion models has recently gained significant attention. To enable more flexible image manipulation and editing, recent research has expanded from single image generation to transparent layer generation and multi-layer compositions. However, existing approaches often fail to provide a thorough exploration of multi-layer structures, leading to inconsistent inter-layer interactions, such as occlusion relationships, spatial layout, and shadowing. In this paper, we introduce DreamLayer, a novel framework that enables coherent text-driven generation of multiple image layers, by explicitly modeling the relationship between transparent foreground and background layers. DreamLayer incorporates three key components, i.e., Context-Aware Cross-Attention (CACA) for global-local information exchange, Layer-Shared Self-Attention (LSSA) for establishing robust inter-layer connections, and Information Retained Harmonization (IRH) for refining fusion details at the latent level.By leveraging a coherent full-image context, DreamLayer builds inter-layer connections through attention mechanisms and applies a harmonization step to achieve seamless layer fusion. To facilitate research in multi-layer generation, we construct a high-quality, diverse multi-layer dataset including 400k samples. Extensive experiments and user studies demonstrate that DreamLayer generates more coherent and well-aligned layers, with broad applicability, including latent-space image editing and image-to-layer decomposition.
OD-RASE: Ontology-Driven Risk Assessment and Safety Enhancement for Autonomous Driving
Authors: Kota Shimomura; Masaki Nambata; Atsuya Ishikawa; Ryota Mimura; Koki Inoue; Takayoshi Yamashita; Takayuki Kawabuchi
Venue: ICCV 2025 (main)

Abstract: Although autonomous driving systems demonstrate high perception performance, they still face limitations when handling rare situations or complex road structures. Since existing road infrastructures are designed for human drivers, safety improvements are typically introduced only after accidents occur. This reactive approach poses a significant challenge for autonomous systems, which require proactive risk mitigation. To address this issue, we propose OD-RASE, a framework for enhancing the safety of autonomous driving systems by detecting road structures that cause traffic accidents and connecting these findings to infrastructure development. First, we formalize an ontology based on specialized domain knowledge of road traffic systems. In parallel, we generate infrastructure improvement proposals using a large-scale visual language model (LVLM) and use ontology-driven data filtering to enhance their reliability. This process automatically annotates improvement proposals on pre-accident road images, leading to the construction of a new dataset. Furthermore, we introduce the Baseline approach (OD-RASE model), which leverages LVLM and a diffusion model to produce both infrastructure improvement proposals and generated images of the improved road environment. Our experiments demonstrate that ontology-driven data filtering enables highly accurate prediction of accident-causing road structures and the corresponding improvement plans. We believe that this work contributes to the overall safety of traffic environments and marks an important step toward the broader adoption of autonomous driving systems.
FREE-Merging: Fourier Transform for Efficient Model Merging
Authors: Shenghe Zheng; Hongzhi Wang
Venue: ICCV 2025 (main)

Abstract: With the rapid growth of deep learning, there is an increasing availability of open-source models for various tasks. However, single fine-tuned models often fall short of meeting the diverse needs of users. Model merging has thus emerged as an efficient method to integrate the capabilities of existing models into a unified model. Nevertheless, existing model merging methods face challenging trade-offs between performance and deployment costs, primarily due to task interference. For the first time, we reveal that task interference is evident in the frequency domain of model parameters, yet current efforts only focus on spatial domain solutions, which are largely ineffective in addressing frequency domain interference. To mitigate the impact of frequency domain interference, we propose **FR-Merging**, an innovative method that effectively filters harmful frequency domain interference on the backbone with minimal computational overhead. Since performance loss is inevitable with cost-free methods, we propose a lightweight task-specific expert module that dynamically compensates for information loss during merging. This proposed framework, **FREE-Merging** (FR-Merging with experts), strikes a balanced trade-off between training cost, inference latency, storage requirements, and performance. We demonstrate the effectiveness of both FR-Merging and FREE-Merging on multiple tasks across CV, NLP, and Multi-Modal domains and show that they can be flexibly adapted to specific needs.
ForgeLens: Data-Efficient Forgery Focus for Generalizable Forgery Image Detection
Authors: Yingjian Chen; Lei Zhang; Yakun Niu
Venue: ICCV 2025 (main)

Abstract: The rise of generative models has raised concerns about image authenticity online, highlighting the urgent need for a detector that is (1) highly generalizable, capable of handling unseen forgery techniques, and (2) data-efficient, achieving optimal performance with minimal training data, enabling it to counter newly emerging forgery techniques effectively. To achieve this, we propose ForgeLens, a data-efficient, feature-guided framework that incorporates two lightweight designs to enable a frozen network to focus on forgery-specific features. First, we introduce the Weight-Shared Guidance Module (WSGM), which guides the extraction of forgery-specific features during training. Second, a forgery-aware feature integrator, FAFormer, is used to effectively integrate forgery information across multi-stage features. ForgeLens addresses a key limitation of previous frozen network-based methods, where general-purpose features extracted from large datasets often contain excessive forgery-irrelevant information. As a result, it achieves strong generalization and reaches optimal performance with minimal training data. Experimental results on 19 generative models, including both GANs and diffusion models, demonstrate improvements of 13.61% in Avg.Acc and 8.69% in Avg.AP over the base model. Notably, ForgeLens outperforms existing forgery detection methods, achieving state-of-the-art performance with just 1% of the training data.
Serialization based Point Cloud Oversegmentation
Authors: Chenghui Lu; Jianlong Kwan; Dilong Li; Ziyi Chen; Haiyan Guan
Venue: ICCV 2025 (main)

Abstract: Point cloud oversegmentation, as a fundamental preprocessing step for 3D understanding, is a challenging task due to its spatial proximity and semantic similarity requirements. Most existing works struggle to efficiently group semantically consistent points into superpoints while maintaining spatial proximity. In this paper, we propose a novel serialization based point cloud oversegmentation method, which leverages serialization to avoid complex spatial queries, directly accessing neighboring points through sequence locality for similarity matching and superpoint clustering. Specifically, we first serialize point clouds into a Hilbert curve and spatially-continuously partition them into initial segments. Then, to guarantee the internal semantic consistency of superpoints, we design an adaptive update algorithm that clusters superpoints by matching feature similarities between neighboring segments and refines segment features via Cross-Attention. Experiments on largescale indoor and outdoor datasets demonstrate state-of-the-art performance in point cloud oversegmentation. Moreover, it is also adaptable to semantic segmentation and achieves promising performance. The code is available at https://github.com/CHL-glitch/SPCNet.