GenAI4ELab/papercli-papers huggingface dataset:
Spherical Epipolar Rectification for Deep Two-View Absolute Depth Estimation
Authors: Pierre-André Brousseau; Sébastien Roy
Venue: ICCV 2025 (main)

Abstract: Absolute depth estimation from single camera sequence of images is a relevant task given that mobile machines increasingly rely on vision to navigate. Deep learning for stereo matching has been demonstrated to improve performance for stereo rectified depth estimation but these methods require straightforward left-right camera setups. This work proposes to introduce deep stereo matching to two views of a monocular image sequence obtained from a camera in motion in a static scene. This paper introduces a novel and principled spherical epipolar rectification model, which handles all camera motions. This rectification model is differentiable and allows self-supervised deep stereo matching algorithms to compute disparity and recover depth, given known camera pose. This paper also introduces a spherical crop operation which limits rectified image size and allows for competitive absolute depth estimation performance. This results in a spherical rectification model that is demonstrated to provide metric depth and compete favorably with current state-of-the-art monocular depth estimators. The code is available at https://gitlab.com/labv3d/spherical-stereo.git.
ScenePainter: Semantically Consistent Perpetual 3D Scene Generation with Concept Relation Alignment
Authors: Chong Xia; Shengjun Zhang; Fangfu Liu; Chang Liu; Khodchaphun Hirunyaratsameewong; Yueqi Duan
Venue: ICCV 2025 (main)

Abstract: Perpetual 3D scene generation aims to produce long-range and coherent 3D view sequences, which is applicable for long-term video synthesis and 3D scene reconstruction. Existing methods follow a "navigate-and-imagine" fashion and rely on outpainting for successive view expansion. However, the generated view sequences suffer from semantic drift issue derived from the accumulated deviation of the outpainting module. To tackle this challenge, we propose ScenePainter, a new framework for semantically consistent 3D scene generation, which aligns the outpainter's scene-specific prior with the comprehension of the current scene. To be specific, we introduce a hierarchical graph structure dubbed SceneConceptGraph to construct relations among multi-level scene concepts, which directs the outpainter for consistent novel views and can be dynamically refined to enhance diversity. Extensive experiments demonstrate that our framework overcomes the semantic drift issue and generates more consistent and immersive 3D view sequences.
ESCNet:Edge-Semantic Collaborative Network for Camouflaged Object Detection
Authors: Sheng Ye; Xin Chen; Yan Zhang; Xianming Lin; Liujuan Cao
Venue: ICCV 2025 (main)

Abstract: Camouflaged object detection (COD) faces unique challenges where target boundaries are intrinsically ambiguous due to their textural similarity to backgrounds. Existing methods relying on single-modality features often produce fragmented predictions due to insufficient boundary constraints.To address this, we propose ESCNet with dynamically coupled edge-texture perception. Our framework introduces three core innovations that work in concert:1) Adaptive Edge-Texture Perceptor (AETP), which creates an edge prediction behaviour where edge and texture information are mutually reinforcing based on the multi-scale features of the image integrated with the global semantic context of the Transformer;2) Dual-Stream Feature Augmentor (DSFA), which dynamically adjusts the kernel sampling position according to the local texture complexity and edge orientation, thus accurately enhancing the feature information at fractal boundaries and amorphous texture locations;3) Multi-Feature Modulation Module (MFMM), which establishes incremental fine-grained improvements for feature calibration and model prediction through enhanced characterisation of edge perception and hierarchical integration of multiple textures. This interconnected system forms a feedback loop where enhanced representations of edge perception enhance model texture prediction and vice versa. Our ESCNet demonstrates significant performance advantages on all three authoritative datasets.
PixelStitch: Structure-Preserving Pixel-Wise Bidirectional Warps for Unsupervised Image Stitching
Authors: Hengzhe Jin; Lang Nie; Chunyu Lin; Xiaomei Feng; Yao Zhao
Venue: ICCV 2025 (main)

Abstract: We propose PixelStitch, a pixel-wise bidirectional warp that learns to stitch images as well as preserve structure in an unsupervised paradigm. To produce natural stitched images, we first determine the middle plane through homography decomposition and globally project the original images toward the desired plane. Compared with unidirectional homography transformation, it evenly spreads projective distortion across two views and decreases the proportion of invalid pixels. Then, the bidirectional optical flow fields are established to carry out residual pixel-wise deformation with projection-weighted natural coefficients, encouraging pixel motions to be as unnoticeable as possible in non-overlapping regions while smoothly transitioning into overlapping areas. Crucially, this flexible deformation enables PixelStitch to align large-parallax images and preserve the structural integrity of non-overlapping contents. To obtain high-quality stitched images in the absence of labels, a comprehensive unsupervised objective function is proposed to simultaneously encourage content alignment, structure preservation, and bidirectional consistency. Finally, extensive experiments are conducted to show our superiority to existing state-of-the-art (SoTA) methods in the quantitative metric, qualitative appearance, and generalization ability. The code will be available.
SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation
Authors: Junsong Chen; Shuchen Xue; Yuyang Zhao; Jincheng Yu; Sayak Paul; Junyu Chen; Han Cai; Song Han; Enze Xie
Venue: ICCV 2025 (main)

Abstract: This paper presents SANA-Sprint, an efficient diffusion model for ultra-fast text-to-image (T2I) generation. SANA-Sprint is built on a pre-trained foundation model and augmented with hybrid distillation, dramatically reducing inference steps from 20 to 1-4.We introduce three key innovations: (1) We propose a training-free approach that transforms a pre-trained flow-matching model for continuous-time consistency distillation (sCM), eliminating costly training from scratch and achieving high training efficiency. Our hybrid distillation strategy combines sCM with latent adversarial distillation (LADD): sCM ensures alignment with the teacher model, while LADD enhances single-step generation fidelity. (2) SANA-Sprint is a unified step-adaptive model that achieves high-quality generation in 1-4 steps, eliminating step-specific training and improving efficiency. (3) We integrate ControlNet with SANA-Sprint for real-time interactive image generation, enabling instant visual feedback for user interaction. SANA-Sprint establishes a new Pareto frontier in speed-quality tradeoffs, achieving state-of-the-art performance with 7.59 FID and 0.74 GenEval in just 1 step -- outperforming FLUX-schnell (7.94 FID / 0.71 GenEval) while being 10x faster (0.1s vs 1.1s on H100). It also achieves 0.1s (T2I) and 0.25s (ControlNet) latency for 1024x1024 images on H100, and 0.31s (T2I) on an RTX 4090, showcasing its exceptional efficiency and potential for AI-powered consumer applications (AIPC). Code and pre-trained models will be open-sourced.
Information-Bottleneck Driven Binary Neural Network for Change Detection
Authors: Kaijie Yin; Zhiyuan Zhang; Shu Kong; Tian Gao; Cheng-Zhong Xu; Hui Kong
Venue: ICCV 2025 (main)

Abstract: In this paper, we propose Binarized Change Detection (BiCD), the first binary neural network (BNN) designed specifically for change detection. Conventional network binarization approaches, which directly quantize both weights and activations in change detection models, severely limit the network's ability to represent input data and distinguish between changed and unchanged regions. This results in significantly lower detection accuracy compared to real-valued networks. To overcome these challenges, BiCD enhances both the representational power and feature separability of BNNs, improving detection performance. Specifically, we introduce an auxiliary objective based on the Information Bottleneck (IB) principle, guiding the encoder to retain essential input information while promoting better feature discrimination. Since directly computing mutual information under the IB principle is intractable, we design a compact, learnable auxiliary module as an approximation target, leading to a simple yet effective optimization strategy that minimizes both reconstruction loss and standard change detection loss.Extensive experiments on street-view and remote sensing datasets demonstrate that BiCD establishes a new benchmark for BNN-based change detection, achieving state-of-the-art performance in this domain.
Erasing More Than Intended? How Concept Erasure Degrades the Generation of Non-Target Concepts
Authors: Ibtihel Amara; Ahmed Imtiaz Humayun; Ivana Kajic; Zarana Parekh; Natalie Harris; Sarah Young; Chirag Nagpal; Najoung Kim; Junfeng He; Cristina Nader Vasconcelos; Deepak Ramachandran; Golnoosh Farnadi; Katherine Heller; Mohammad Havaei; Negar Rostamzadeh
Venue: ICCV 2025 (main)

Abstract: Concept erasure techniques have recently gained significant attention for their potential to remove unwanted concepts from text-to-image models. While these methods often demonstrate promising results in controlled settings, their robustness in real-world applications and suitability for deployment remain uncertain. In this work, we (1) identify a critical gap in evaluating sanitized models, particularly in assessing their performance across diverse concept dimensions, and (2) systematically analyze the failure modes of text-to-image models post-erasure. We focus on the unintended consequences of concept removal on non-target concepts across different levels of interconnected relationships including visually similar, binomial, and semantically related concepts. To address this, we introduce EraseBench, a comprehensive benchmark for evaluating post-erasure performance. EraseBench includes over 100 curated concepts, targeted evaluation prompts, and a robust set of metrics to assess both effectiveness and side effects of erasure. Our findings reveal a phenomenon of concept entanglement, where erasure leads to unintended suppression of non-target concepts, causing spillover degradation that manifests as distortions and a decline in generation quality.
Global and Local Entailment Learning for Natural World Imagery
Authors: Srikumar Sastry; Aayush Dhakal; Eric Xing; Subash Khanal; Nathan Jacobs
Venue: ICCV 2025 (main)

Abstract: Learning the hierarchical structure of data in vision-language models is a significant challenge. Previous works have attempted to address this challenge by employing entailment learning. However, these approaches fail to model the transitive nature of entailment explicitly, which establishes the relationship between order and semantics within a representation space. In this work, we introduce Radial Cross-Modal Embeddings (RCME), a framework that enables the explicit modeling of transitivity-enforced entailment. Our proposed framework optimizes for the partial order of concepts within vision-language models. By leveraging our framework, we develop a hierarchical vision-language foundation model capable of representing the hierarchy in the Tree of Life. Our experiments on hierarchical species classification and hierarchical retrieval tasks demonstrate the enhanced performance of our models compared to the existing state-of-the-art models. Our code and models are open-sourced at https://vishu26.github.io/RCME.
Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness
Authors: Haochen Wang; Yucheng Zhao; Tiancai Wang; Haoqiang Fan; Xiangyu Zhang; Zhaoxiang Zhang
Venue: ICCV 2025 (main)

Abstract: The rapid development of Large Multimodal Models (LMMs) for 2D images and videos has spurred efforts to adapt these models for interpreting 3D scenes. However, the absence of large-scale 3D vision-language datasets has posed a significant obstacle. To address this issue, typical approaches focus on injecting 3D awareness into 2D LMMs by designing 3D input-level scene representations. This work provides a new perspective. We introduce reconstructive visual instruction tuning with 3D-awareness (ROSS3D), which integrates 3D-aware visual supervision into the training procedure. Specifically, it incorporates cross-view and global-view reconstruction. The former requires reconstructing masked views by aggregating overlapping information from other views. The latter aims to aggregate information from all available views to recover Bird's-Eye-View images, contributing to a comprehensive overview of the entire scene. Empirically, ROSS3D achieves state-of-the-art performance across various 3D scene understanding benchmarks. More importantly, our semi-supervised experiments demonstrate significant potential in leveraging large amounts of unlabeled 3D vision-only data.
LVFace: Progressive Cluster Optimization for Large Vision Models in Face Recognition
Authors: Jinghan You; Shanglin Li; Yuanrui Sun; Jiangchuan Wei; Mingyu Guo; Chao Feng; Jiao Ran
Venue: ICCV 2025 (main)

Abstract: Vision Transformers (ViTs) have revolutionized large-scale visual modeling, yet remain underexplored in face recognition (FR) where CNNs still dominate. We identify a critical bottleneck: CNN-inspired training paradigms fail to unlock ViT's potential, leading to suboptimal performance and convergence instability.To address this challenge, we propose LVFace, a ViT-based FR model that integrates Progressive Cluster Optimization (PCO) to achieve superior results. Specifically, PCO sequentially applies negative class sub-sampling (NCS) for robust and fast feature alignment from random initialization, feature expectation penalties for centroid stabilization, performing cluster boundary refinement through full-batch training without NCS constraints. LVFace establishes a new state-of-the-art face recognition baseline, surpassing leading approaches such as UniFace and TopoFR across multiple benchmarks. Extensive experiments demonstrate that LVFace delivers consistent performance gains, while exhibiting scalability to large-scale datasets and compatibility with mainstream VLMs and LLMs. Notably, LVFace secured 1st place in the ICCV 2021 Masked Face Recognition (MFR)-Ongoing Challenge (March 2025), proving its efficacy in real-world scenarios. Project is available at https://github.com/bytedance/LVFace.
Dataset Ownership Verification for Pre-trained Masked Models
Authors: Yuechen Xie; Jie Song; Yicheng Shan; Xiaoyan Zhang; Yuanyu Wan; Shengxuming Zhang; Jiarui Duan; Mingli Song
Venue: ICCV 2025 (main)

Abstract: High-quality open-source datasets have emerged as a pivotal catalyst driving the swift advancement of deep learning, while facing the looming threat of potential exploitation. Protecting these datasets is of paramount importance for the interests of their owners. The verification of dataset ownership has evolved into a crucial approach in this domain; however, existing verification techniques are predominantly tailored to supervised models and contrastive pre-trained models, rendering them ill-suited for direct application to the increasingly prevalent masked models. In this work, we introduce the inaugural methodology addressing this critical, yet unresolved challenge, termed Dataset Ownership Verification for Masked Modeling (DOV4MM). The central objective is to ascertain whether a suspicious black-box model has been pre-trained on a particular unlabeled dataset, thereby assisting dataset owners in safeguarding their rights. DOV4MM is grounded in our empirical observation that when a model is pre-trained on the target dataset, the difficulty of reconstructing masked information within the embedding space exhibits a marked contrast to models not pre-trained on that dataset. We validated the efficacy of DOV4MM through ten masked image models on ImageNet-1K and four masked language models on WikiText-103. The results demonstrate that DOV4MM rejects the null hypothesis, with a p-value considerably below 0.05, surpassing all prior approaches. Code is available at https://github.com/xieyc99/DOV4MM.
VLR-Driver: Large Vision-Language-Reasoning Models for Embodied Autonomous Driving
Authors: Fanjie Kong; Yitong Li; Weihuang Chen; Chen Min; Yizhe Li; Zhiqiang Gao; Haoyang Li; Zhongyu Guo; Hongbin Sun
Venue: ICCV 2025 (main)

Abstract: The rise of embodied intelligence and multi-modal large language models has led to exciting advancements in the field of autonomous driving, establishing it as a prominent research focus in both academia and industry. However, when confronted with intricate and ambiguous traffic scenarios, the lack of logical reasoning and cognitive decision-making capabilities remains the primary challenge impeding the realization of embodied autonomous driving. Although Vision Language Models (VLMs) have enhanced the deep semantic understanding of autonomous driving systems, they exhibit notable limitations in decision explainability when handling rare and long-tail traffic scenarios. In this paper, we propose VLR-Driver, a novel multi-modal Vision-Language-Reasoning (VLR) framework based on Chain of Thought (CoT) for embodied autonomous driving. The framework employs a spatiotemporal CoT reasoning approach to recursively analyze potential safety risks and driving intentions of other agents, thereby delivering an efficient and transparent decision-making process. Furthermore, we construct a multi-modal reasoning-decision dataset to support the advancement of hierarchical reasoning of VLMs in autonomous driving. Closed-loop experiments conducted in CARLA demonstrate that the VLR-Driver significantly outperforms state-of-the-art end-to-end methods. Notably, key metrics such as driving score improved by 17.5%, while the success rate improved by 22.2%, offering a more transparent, reliable, and secure solution for autonomous driving systems.
ResGS: Residual Densification of 3D Gaussian for Efficient Detail Recovery
Authors: Yanzhe Lyu; Kai Cheng; Xin Kang; Xuejin Chen
Venue: ICCV 2025 (main)

Abstract: Recently, 3D Gaussian Splatting (3D-GS) has prevailed in novel view synthesis, achieving high fidelity and efficiency. However, it often struggles to capture rich details and complete geometry. Our analysis reveals that the 3D-GS densification operation lacks adaptiveness and faces a dilemma between geometry coverage and detail recovery. To address this, we introduce a novel densification operation, residual split, which adds a downscaled Gaussian as a residual. Our approach is capable of adaptively retrieving details and complementing missing geometry. To further support this method, we propose a pipeline named ResGS. Specifically, we integrate a Gaussian image pyramid for progressive supervision and implement a selection scheme that prioritizes the densification of coarse Gaussians over time. Extensive experiments demonstrate that our method achieves SOTA rendering quality. Consistent performance improvements can be achieved by applying our residual split on various 3D-GS variants, underscoring its versatility and potential for broader application in 3D-GS-based applications.
Language Driven Occupancy Prediction
Authors: Zhu Yu; Bowen Pang; Lizhe Liu; Runmin Zhang; Qiang Li; Si-Yuan Cao; Maochun Luo; Mingxia Chen; Sheng Yang; Hui-Liang Shen
Venue: ICCV 2025 (main)

Abstract: We introduce LOcc, an effective and generalizable framework for open-vocabulary occupancy (OVO) prediction. Previous approaches typically supervise the networks through coarse voxel-to-text correspondences via image features as intermediates or noisy and sparse correspondences from voxel-based model-view projections. To alleviate the inaccurate supervision, we propose a semantic transitive labeling pipeline to generate dense and fine-grained 3D language occupancy ground truth. Our pipeline presents a feasible way to dig into the valuable semantic information of images, transferring text labels from images to LiDAR point clouds and ultimately to voxels, to establish precise voxel-to-text correspondences. By replacing the original prediction head of supervised occupancy models with a geometry head for binary occupancy states and a language head for language features, LOcc effectively uses the generated language ground truth to guide the learning of 3D language volume. Through extensive experiments, we demonstrate that our transitive semantic labeling pipeline can produce more accurate pseudo-labeled ground truth, diminishing labor-intensive human annotations. Additionally, we validate LOcc across various architectures, where all models consistently outperform state-of-the-art zero-shot occupancy prediction approaches on the Occ3D-nuScenes dataset.
Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation
Authors: Sebastian Schmidt; Julius Koerner; Dominik Fuchsgruber; Stefano Gasperini; Federico Tombari; Stephan Günnemann
Venue: ICCV 2025 (main)

Abstract: In panoptic segmentation, individual instances must be separated within semantic classes. As state-of-the-art methods rely on a pre-defined set of classes, they struggle with novel categories and out-of-distribution (OOD) data. This is particularly problematic in safety-critical applications, such as autonomous driving, where reliability in unseen scenarios is essential. We address the gap between outstanding benchmark performance and reliability by proposing Prior2Former (P2F), the first approach for segmentation vision transformers rooted in evidential learning. P2F extends the mask vision transformer architecture by incorporating a Beta prior for computing model uncertainty in pixel-wise binary mask assignments. This design enables high-quality uncertainty estimation that effectively detects novel and OOD objects, enabling state-of-the-art anomaly instance segmentation and open-world panoptic segmentation. Unlike most segmentation models addressing unknown classes, P2F operates without access to OOD data samples or contrastive training on void (i.e., unlabeled) classes, making it highly applicable in real-world scenarios where such prior information is unavailable. Additionally, P2F can be flexibly applied to anomaly instance and panoptic segmentation. Through comprehensive experiments on the Cityscapes, COCO, SegmentMeIfYouCan, and OoDIS datasets, P2F demonstrates state-of-the-art performance. Especially in OoDIS, P2F ranks first in its category.
E-NeMF: Event-based Neural Motion Field for Novel Space-time View Synthesis of Dynamic Scenes
Authors: Yan Liu; Zehao Chen; Haojie Yan; De Ma; Huajin Tang; Qian Zheng; Gang Pan
Venue: ICCV 2025 (main)

Abstract: Synthesizing novel space-time views from a monocular video is a highly ill-posed problem, and its effectiveness relies on accurately reconstructing motion and appearance of the dynamic scene.Frame-based methods for novel space-time view synthesis in dynamic scenes rely on simplistic motion assumptions due to the absence of inter-frame cues, which makes them fall in complex motion. Event camera captures inter-frame cues with high temporal resolution, which makes it hold the promising potential to handle high speed and complex motion. However, it is still difficult due to the event noise and sparsity. To mitigate the impact caused by event noise and sparsity, we propose E-NeMF, which alleviates the impact of event noise with Parametric Motion Representation and mitigates the event sparsity with Flow Prediction Module. Experiments on multiple real-world datasets demonstrate our superior performance in handling high-speed and complex motion.