GenAI4ELab/papercli-papers huggingface dataset:
GECKO: Gigapixel Vision-Concept Contrastive Pretraining in Histopathology
Authors: Saarthak Kapse; Pushpak Pati; Srikar Yellapragada; Srijan Das; Rajarsi R. Gupta; Joel Saltz; Dimitris Samaras; Prateek Prasanna
Venue: ICCV 2025 (main)

Abstract: Pretraining a Multiple Instance Learning (MIL) aggregator enables the derivation of Whole Slide Image (WSI)-level embeddings from patch-level representations without supervision. While recent multimodal MIL pretraining approaches leveraging auxiliary modalities have demonstrated performance gains over unimodal WSI pretraining, the acquisition of these additional modalities necessitates extensive clinical profiling. This requirement increases costs and limits scalability in existing WSI datasets lacking such paired modalities. To address this, we propose Gigapixel Vision-Concept Knowledge Contrastive pretraining (GECKO), which aligns WSIs with a Concept Prior derived from the available WSIs. First, we derive an inherently interpretable concept prior by computing the similarity between each WSI patch and textual descriptions of predefined pathology concepts. GECKO then employs a dual-branch MIL network: one branch aggregates patch embeddings into a WSI-level deep embedding, while the other aggregates the concept prior to a corresponding WSI-level concept embedding. Both aggregated embeddings are aligned using a contrastive objective, thereby pretraining the entire dual-branch MIL model. Moreover, when auxiliary modalities such as transcriptomics data are available, GECKO seamlessly integrates them. Across five diverse tasks, GECKO consistently outperforms prior unimodal and multimodal pretraining approaches while also delivering clinically meaningful interpretability that bridges the gap between computational models and pathology expertise. Code is made available at github.com/bmi-imaginelab/GECKO
Single-Scanline Relative Pose Estimation for Rolling Shutter Cameras
Authors: Petr Hruby; Marc Pollefeys
Venue: ICCV 2025 (main)

Abstract: We propose a novel approach for estimating the relative pose between rolling shutter cameras using the intersections of line projections with a single scanline per image. This allows pose estimation without explicitly modeling camera motion. Alternatively, scanlines can be selected within a single image, enabling single-view relative pose estimation for scanlines of rolling shutter cameras. Our approach is designed as a foundational building block for rolling shutter structure-from-motion (SfM), where no motion model is required, and each scanline's pose can be computed independently.We classify minimal solvers for this problem in both generic and specialized settings, including cases with parallel lines and known gravity direction. Furthermore, we develop minimal solvers for the parallel-lines scenario, both with and without gravity priors, by leveraging connections between this problem and the estimation of 2D structure from 1D cameras.Experiments on rolling shutter images from the Fastec dataset demonstrate the feasibility of our approach for initializing rolling shutter SfM, highlighting its potential for further development.The code will be made publicly available.
Compression of 3D Gaussian Splatting with Optimized Feature Planes and Standard Video Codecs
Authors: Soonbin Lee; Fangwen Shu; Yago Sanchez; Thomas Schierl; Cornelius Hellge
Venue: ICCV 2025 (main)

Abstract: 3D Gaussian Splatting is a recognized method for 3D scene representation, known for its high rendering quality and speed. However, its substantial data requirements present challenges for practical applications. In this paper, we introduce an efficient compression technique that significantly reduces storage overhead by using compact representation. We propose a unified architecture that combines point cloud data and feature planes through a progressive tri-plane structure. Our method utilizes 2D feature planes, enabling continuous spatial representation. To further optimize these representations, we incorporate entropy modeling in the frequency domain, specifically designed for standard video codecs. We also propose channel-wise bit allocation to achieve a better trade-off between bitrate consumption and feature plane representation. Consequently, our model effectively leverages spatial correlations within the feature planes to enhance rate-distortion performance using standard, non-differentiable video codecs. Experimental results demonstrate that our method outperforms existing methods in data compactness while maintaining high rendering quality.
CULTURE3D: A Large-Scale and Diverse Dataset of Cultural Landmarks and Terrains for Gaussian-Based Scene Rendering
Authors: Xinyi Zheng; Steve Zhang; Weizhe Lin; Aaron Zhang; Walterio W. Mayol-Cuevas; Yunze Liu; Junxiao Shen
Venue: ICCV 2025 (main)

Abstract: Current state-of-the-art 3D reconstruction models face limitations in building extra-large scale outdoor scenes, primarily due to the lack of sufficiently large-scale and detailed datasets. In this paper, we present a extra-large fine-grained dataset with 10 billion points composed of 41,006 drone-captured high-resolution aerial images, covering 20 diverse and culturally significant scenes from worldwide locations such as Cambridge campus, the Pyramids, and the Forbidden City. Compared to existing datasets, ours offers significantly larger scale and higher detail, uniquely suited for fine-grained 3D applications. Each scene contains an accurate spatial layout and comprehensive structural information, supporting detailed 3D reconstruction tasks. By reconstructing environments using these detailed images, our dataset supports multiple applications, including outputs in the widely adopted COLMAP format, establishing a novel benchmark for evaluating state-of-the-art large-scale Gaussian Splatting methods.The dataset's flexibility encourages innovations and supports model plug-ins, paving the way for future 3D breakthroughs. All datasets and code will be open-sourced for community use.
Real3D: Towards Scaling Large Reconstruction Models with Real Images
Authors: Hanwen Jiang; Qixing Huang; Georgios Pavlakos
Venue: ICCV 2025 (main)

Abstract: Training single-view Large Reconstruction Models (LRMs) follows the fully supervised route, requiring multi-view supervision. However, the multi-view data typically comes from synthetic 3D assets, which are hard to scale further and are not representative of the distribution of real-world object shapes. To address these limitations, we introduce Real3D, the first LRM that uses single-view real images for training, benefiting from their scalability and capturing the real-world shape distribution. Real3D introduces a novel self-training framework, including unsupervised losses at the pixel- and semantic-level, enabling LRMs to learn from these single-view images without multi-view supervision. Simultaneously, to deal with the noise of real data, Real3D also presents an automatic data curation approach to gather high-quality examples that have positive impact on training. Our experiments show that Real3D consistently outperforms prior work in diverse evaluation settings that include real and synthetic data, as well as both in-domain and out-of-domain shapes.
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
Authors: Xinhua Lu; Runhe Lai; Yanqi Wu; Kanghao Chen; Wei-Shi Zheng; Ruixuan Wang
Venue: ICCV 2025 (main)

Abstract: Pre-trained vision-language models (VLMs) have advanced out-of-distribution (OOD) detection recently. However, existing CLIP-based methods often focus on learning OOD-related knowledge to improve OOD detection, showing limited generalization or reliance on external large-scale auxiliary datasets. In this study, instead of delving into the intricate OOD-related knowledge, we propose an innovative CLIP-based framework based on Forced prompt leArning (FA), designed to make full use of the In-Distribution (ID) knowledge and ultimately boost the effectiveness of OOD detection. Our key insight is to learn a prompt (i.e., forced prompt) that contains more diversified and richer descriptions of the ID classes beyond the textual semantics of class labels. Specifically, it promotes better discernment for ID images, by forcing more notable semantic similarity between ID images and the learnable forced prompt. Moreover, we introduce a forced coefficient, encouraging the forced prompt to learn more comprehensive and nuanced descriptions of the ID classes. In this way, FA is capable of achieving notable improvements in OOD detection, even when trained without any external auxiliary datasets, while maintaining an identical number of trainable parameters as CoOp. Extensive empirical evaluations confirm our method consistently outperforms current state-of-the-art methods. Code is available at https://github.com/0xFAFA/FA.
Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction
Authors: Zeren Jiang; Chuanxia Zheng; Iro Laina; Diane Larlus; Andrea Vedaldi
Venue: ICCV 2025 (main)

Abstract: We introduce Geo4D, a method to repurpose video diffusion models for monocular 3D reconstruction of dynamic scenes. By leveraging the strong dynamic priors captured by large-scale pre-trained video models, Geo4D can be trained using only synthetic data while generalizing well to real data in a zero-shot manner. Geo4D predicts several complementary geometric modalities, namely point, disparity, and ray maps. We propose a new multi-modal alignment algorithm to align and fuse these modalities, as well as a sliding window approach at inference time, thus enabling robust and accurate 4D reconstruction of long videos. Extensive experiments across multiple benchmarks show that Geo4D significantly surpasses state-of-the-art video depth estimation methods.
REPARO: Compositional 3D Assets Generation with Differentiable 3D Layout Alignment
Authors: Haonan Han; Rui Yang; Huan Liao; Jiankai Xing; Zunnan Xu; Xiaoming Yu; Junwei Zha; Xiu Li; Wanhua Li
Venue: ICCV 2025 (main)

Abstract: Traditional image-to-3D models often struggle with scenes containing multipleobjects due to biases and occlusion complexities. To address this challenge, wepresent REPARO, a novel approach for compositional 3D asset generation fromsingle images. REPARO employs a two-step process: first, it extracts individualobjects from the scene and reconstructs their 3D meshes using off-the-shelf imageto-3D models; then, it optimizes the layout of these meshes through differentiablerendering techniques, ensuring coherent scene composition. By integrating optimaltransport-based long-range appearance loss term and high-level semantic loss termin the differentiable rendering, REPARO can effectively recover the layout of 3Dassets. The proposed method can significantly enhance object independence, detailaccuracy, and overall scene coherence. Extensive evaluation of multi-object scenesdemonstrates that our REPARO offers a comprehensive approach to address thecomplexities of multi-object 3D scene generation from single images.
VACE: All-in-One Video Creation and Editing
Authors: Zeyinzi Jiang; Zhen Han; Chaojie Mao; Jingfeng Zhang; Yulin Pan; Yu Liu
Venue: ICCV 2025 (main)

Abstract: Diffusion Transformer has demonstrated powerful capability and scalability in generating high-quality images and videos. Further pursuing the unification of generation and editing tasks has yielded significant progress in the domain of image content creation. However, due to the intrinsic demands for consistency across both temporal and spatial dynamics, achieving a unified approach for video synthesis remains challenging. We introduce VACE, which enables users to perform Video tasks within an All-in-one framework for Creation and Editing. These tasks include reference-to-video generation, video-to-video editing, and masked video-to-video editing. Specifically, we effectively integrate the requirements of various tasks by organizing video task inputs, such as editing, reference, and masking, into a unified interface referred to as the Video Condition Unit (VCU). Furthermore, by utilizing a Context Adapter structure, we inject different task concepts into the model using formalized representations of temporal and spatial dimensions, allowing it to handle arbitrary video synthesis tasks flexibly. Extensive experiments demonstrate that the unified model of VACE achieves performance on par with task-specific models across various subtasks. Simultaneously, it enables diverse applications through versatile task combinations.
SparseMM: Head Sparsity Emerges from Visual Concept Responses in MLLMs
Authors: Jiahui Wang; Zuyan Liu; Yongming Rao; Jiwen Lu
Venue: ICCV 2025 (main)

Abstract: Multimodal Large Language Models (MLLMs) are commonly derived by extending pre-trained Large Language Models (LLMs) with visual capabilities. In this work, we investigate how MLLMs process visual inputs by analyzing their attention mechanisms. We reveal a surprising sparsity phenomenon: only a small subset (approximately less than 5%) of attention heads in LLMs actively contribute to visual understanding, termed visual heads. To identify these heads efficiently, we design a training-free framework that quantifies head-level visual relevance through targeted response analysis. Building on this discovery, we introduce SparseMM, a KV-Cache optimization strategy that allocates asymmetric computation budgets to heads in LLMs based on their visual scores, leveraging the sparity of visual heads for accelerating the inference of MLLMs. Compared with prior KV-Cache acceleration methods that ignore the particularity of visual, SparseMM prioritizes stress and retaining visual semantics during decoding. Extensive evaluations across mainstream multimodal benchmarks demonstrate that SparseMM achieves superior accuracy-efficiency trade-offs. Notably, SparseMM delivers 1.38x real-time acceleration and 52% memory reduction during generation while maintaining performance parity on efficiency test. Our project is open sourced at https://github.com/CR400AF-A/SparseMM.
Focal Plane Visual Feature Generation and Matching on a Pixel Processor Array
Authors: Hongyi Zhang; Laurie Bose; Jianing Chen; Piotr Dudek; Walterio Mayol-Cuevas
Venue: ICCV 2025 (main)

Abstract: Pixel Processor Arrays (PPAs) are vision sensors that embed data and processing into every pixel element. PPAs can execute visual processing directly at the point of light capture, and output only sparse, high-level information. This is in sharp contrast with the conventional visual pipeline, where whole images must be transferred from sensor to processor. This sparse data readout also provides several major benefits such as higher frame rate, lower energy consumption and lower bandwidth requirements. In this work, we demonstrate generation, matching and storage of binary descriptors for visual keypoint features, entirely upon PPA with no need to output images to external processing, making our approach inherently privacy-aware.Our method spreads descriptors across multiple pixel-processors, which allows for significantly larger descriptors than any prior pixel-processing works. These large descriptors can be used for a range of tasks such as place and object recognition. We demonstrate the accuracy of our in-pixel feature matching up to  ~94.5%, at ~210fps, across a range of datasets, with a greater than 100xreduction in data transfer and bandwidth requirements over traditional cameras.
Q-Frame: Query-aware Frame Selection and Multi-Resolution Adaptation for Video-LLMs
Authors: Shaojie Zhang; Jiahui Yang; Jianqin Yin; Zhenbo Luo; Jian Luan
Venue: ICCV 2025 (main)

Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant success in visual understanding tasks. However, challenges persist in adapting these models for video comprehension due to the large volume of data and temporal complexity. Existing Video-LLMs using uniform frame sampling often struggle to capture the query-related crucial spatiotemporal clues of videos effectively. In this paper, we introduce Q-Frame, a novel approach for adaptive frame selection and multi-resolution scaling tailored to the video's content and the specific query. Q-Frame employs a training-free, plug-and-play strategy generated by a text-image matching network like CLIP, utilizing the Gumbel-Max trick for efficient frame selection. Q-Frame allows Video-LLMs to process more frames without exceeding computational limits, thereby preserving critical temporal and spatial information. We demonstrate Q-Frame's effectiveness through extensive experiments on benchmark datasets, including MLVU, LongVideoBench, and Video-MME, illustrating its superiority over existing methods and its applicability across various video understanding tasks.
Prompt-driven Transferable Adversarial Attack on Person Re-Identification with Attribute-aware Textual Inversion
Authors: Yuan Bian; Min Liu; Yunqi Yi; Xueping Wang; Shuai Jiang; Yaonan Wang
Venue: ICCV 2025 (main)

Abstract: Person re-identification (re-id) models are vital in security surveillance systems, requiring transferable adversarial attacks to explore the vulnerabilities of them. Recently, vision-language models (VLM) based attacks have shown superior transferability by attacking generalized image and textual features of VLM, but they lack comprehensive feature disruption due to the overemphasis on discriminative semantics in integral representation. In this paper, we introduce the Attribute-aware Prompt Attack (AP-Attack), a novel method that leverages VLM's image-text alignment capability to explicitly disrupt fine-grained semantic features of pedestrian images by destroying attribute-specific textual embeddings. To obtain personalized textual descriptions for individual attributes, textual inversion networks are designed to map pedestrian images to pseudo tokens that represent semantic embeddings, trained in the contrastive learning manner with images and a predefined prompt template that explicitly describes the pedestrian attributes. Inverted benign and adversarial fine-grained textual semantics facilitate attacker in effectively conducting thorough disruptions, enhancing the transferability of adversarial examples. Extensive experiments show that AP-Attack achieves state-of-the-art transferability, significantly outperforming previous methods by 22.9% on mean Drop Rate in cross-model&dataset attack scenarios.
OV-SCAN: Semantically Consistent Alignment for Novel Object Discovery in Open-Vocabulary 3D Object Detection
Authors: Adrian Chow; Evelien Riddell; Yimu Wang; Sean Sedwards; Krzysztof Czarnecki
Venue: ICCV 2025 (main)

Abstract: Open-vocabulary 3D object detection for autonomous driving aims to detect novel objects beyond the predefined training label sets in point cloud scenes. Existing approaches achieve this by connecting traditional 3D object detectors with vision-language models (VLMs) to regress 3D bounding boxes for novel objects and perform open-vocabulary classification through cross-modal alignment between 3D and 2D features. However, achieving robust cross-modal alignment remains a challenge due to semantic inconsistencies when generating corresponding 3D and 2D feature pairs. To overcome this challenge, we present OV-SCAN, an Open-Vocabulary 3D framework that enforces Semantically Consistent Alignment for Novel object discovery. OV-SCAN employs two core strategies: discovering precise 3D annotations and filtering out low-quality or corrupted alignment pairs (arising from 3D annotation, occlusion-induced, or resolution-induced noise). Extensive experiments on the nuScenes dataset demonstrate that OV-SCAN achieves state-of-the-art performance.
ContextFace: Generating Facial Expressions from Emotional Contexts
Authors: Min-jung Kim; Minsang Kim; Seung Jun Baek
Venue: ICCV 2025 (main)

Abstract: The task of generating 3D facial expressions given various situational contexts is important for applications such as virtual avatars or human-robot interactions. The task is, however, challenging not only because it requires a comprehensive understanding of emotion, expression and contexts, but also there rarely are datasets to support the task. We propose ContextFace, a Multi-modal Large Language Model (MLLM) fine-tuned to generate 3D facial expressions depending on complex situational contexts. To overcome the lack of datasets, we perform a context augmentation to existing emotion recognition datasets; we generate plausible situations and quotes from images and emotions to annotate the dataset. Next, we perform visual instruction tuning of MLLMs on context-augmented datasets to boost their capability of visual synthesis from emotions. Experiments show a superior performance of ContextFace in the zero-shot evaluation of contextual emotion recognition. A qualitative evaluation shows that our method generates expressions consistent with diverse contexts and performs complex emotion reasoning, e.g., speculative generation of expressions of occluded faces through interactive prompting.
MDP-Omni: Parameter-free Multimodal Depth Prior-based Sampling for Omnidirectional Stereo Matching
Authors: Eunjin Son; HyungGi Jo; Wookyong Kwon; Sang Jun Lee
Venue: ICCV 2025 (main)

Abstract: Omnidirectional stereo matching (OSM) estimates 360deg depth by performing stereo matching on multi-view fisheye images. Existing methods assume a unimodal depth distribution, matching each pixel to a single object. However, this assumption constrains the sampling range, causing over-smoothed depth artifacts, especially at object boundaries. To address these limitations, we propose MDP-Omni, a novel OSM network that leverages parameter-free multimodal depth priors. Specifically, we design a sampling strategy that adaptively adjusts the sampling range based on a multimodal probability distribution, without introducing any additional parameters. Furthermore, we present the azimuth-based multi-view volume fusion module to build a single cost volume. It mitigates false matches caused by occlusions in warped multi-view volumes. Experimental results demonstrate that MDP-Omni significantly improves existing methods, particularly in capturing fine details.
The Inter-Intra Modal Measure: A Predictive Lens on Fine-Tuning Outcomes in Vision-Language Models
Authors: Laura Niss; Kevin Vogt-Lowell; Theodoros Tsiligkaridis
Venue: ICCV 2025 (main)

Abstract: The fine-tuning of large vision-language foundation models remains an underexplored area, particularly regarding its impact on learning gains and catastrophic forgetting. Inspired by the significance of modality gaps in contrastive dual-encoders, we introduce the Inter-Intra Modal Measure (IIMM)--a predictive metric that quantifies the relationship between intra-modal image embedding similarity and inter-modal misalignment. Through extensive empirical analysis across four state-of-the-art vision-language models and five fine-tuning techniques, we establish a strong linear relationship: tasks with higher IIMM scores yield greater in-domain performance improvements but suffer from more pronounced out-of-domain degradation, with some parameter-efficient fine-tuning (PEFT) methods exhibiting severe forgetting. Compared to existing transferability measures, the IIMM demonstrates significantly stronger predictive power for accuracy changes post fine-tuning in dual-encoder models. Moreover, we provide a theoretical bound, proving that changes in IIMM are limited by the Wasserstein distance between pre- and post-fine-tuning embedding distributions, ensuring its stability and robustness as a predictive measure. With only a single forward pass of the target data, practitioners can leverage this key insight to evaluate the degree to which a model can be expected to improve following fine-tuning. When combined with prior knowledge of a model's performance across diverse tasks, the IIMM further enhances transferability predictions for novel tasks, offering a lightweight yet effective tool for guiding model adaptation strategies. Our code is provided at https://github.com/mit-ll/IIMM.