GenAI4ELab/papercli-papers huggingface dataset:
Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning
Authors: Weitai Kang; Haifeng Huang; Yuzhang Shang; Mubarak Shah; Yan Yan
Venue: ICCV 2025 (main)

Abstract: Recent advancements in 3D Large Language Models (3DLLMs) show their potential to build general-purpose agents in the 3D real world, yet challenges remain due to the lack of high-quality robust instruction-following data, leading to limited discriminative power and generalization of 3DLLMs. In this paper, we introduce Robin3D, a powerful 3DLLM trained on large-scale instruction-following data generated by our novel data engine, Robust Instruction Generation (RIG) engine. RIG generates two key instruction data: 1) the Adversarial Instruction-following data, which features mixed negative and positive samples to enhance the model's discriminative understanding. 2) the Diverse Instruction-following data, which contains various instruction styles to enhance model's generalization. As a result, we construct 1 million instruction-following data, consisting of 344K Adversarial samples, 508K Diverse samples, and 165K benchmark training set samples. To better handle these complex instructions, Robin3D further integrates an improved vision projector and enhanced sequence organization. Notably, we achieve a 7.8% improvement in the grounding task (Multi3DRefer) and a 6.9% improvement in the captioning task (Scan2Cap).
Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection
Authors: Giacomo D' Amicantonio; Snehashis Majhi; Quan Kong; Lorenzo Garattoni; Gianpiero Francesca; Francois Bremond; Egor Bondarev
Venue: ICCV 2025 (main)

Abstract: Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD) paradigm, only video-level labels are provided during training, while predictions are made at the frame level. Although state-of-the-art models perform well on simple anomalies (e.g., explosions), they struggle with complex real-world events (e.g., shoplifting). This difficulty stems from two key issues: (1) the inability of current models to address the diversity of anomaly types, as they process all categories with a shared model, overlooking category-specific features; and (2) the weak supervision signal, which lacks precise temporal information, limiting the ability to capture nuanced anomalous patterns blended with normal events. To address these challenges, we propose Gaussian Splatting-guided Mixture of Experts (GS-MoE), a novel framework that employs a set of expert models, each specialized in capturing specific anomaly types. These experts are guided by a temporal Gaussian splatting loss, enabling the model to leverage temporal consistency and enhance weak supervision. The Gaussian splatting approach encourages a more precise and comprehensive representation of anomalies by focusing on temporal segments most likely to contain abnormal events. The predictions from these specialized experts are integrated through a mixture-of-experts mechanism to model complex relationships across diverse anomaly patterns. Our approach achieves state-of-the-art performance, with a 91.58% AUC on the UCF-Crime dataset, and demonstrates superior results on XD-Violence and MSAD datasets. By leveraging category-specific expertise and temporal guidance, GS-MoE sets a new benchmark for VAD under weak supervision.
ProJudge: A Multi-Modal Multi-Discipline Benchmark and Instruction-Tuning Dataset for MLLM-based Process Judges
Authors: Jiaxin Ai; Pengfei Zhou; Zhaopan Xu; Ming Li; Fanrui Zhang; Zizhen Li; Jianwen Sun; Yukang Feng; Baojin Huang; Zhongyuan Wang; Kaipeng Zhang
Venue: ICCV 2025 (main)

Abstract: As multi-modal large language models (MLLMs) frequently exhibit errors when solving scientific problems, evaluating the validity of their reasoning processes is critical for ensuring reliability and uncovering fine-grained model weaknesses. Since human evaluation is laborious and costly, prompting MLLMs as automated process judges has become a common practice. However, the reliability of these model-based judges remains uncertain. To address this, we introduce ProJudgeBench, the first comprehensive benchmark specifically designed for evaluating abilities of MLLM-based process judges. ProJudgeBench comprises 2,400 test cases and 50,118 step-level labels, spanning four scientific disciplines with diverse difficulty levels and multi-modal content. In ProJudgeBench, each step is meticulously annotated by human experts for correctness, error type, and explanation, enabling a systematic evaluation of judges' capabilities to detect, classify and diagnose errors. Evaluation on ProJudgeBench reveals a significant performance gap between open-source and proprietary models. To bridge this gap, we further propose ProJudge-173k, a large-scale instruction-tuning dataset, and a Dynamic Dual-Phase fine-tuning strategy that encourages models to explicitly reason through problem-solving before assessing solutions. Both contributions significantly enhance the process evaluation capabilities of open-source models. This project is available at: https://projudge.github.io.
GloPER: Unsupervised Animal Pattern Extraction from Local Reconstruction
Authors: Bowen Chen; Yun Sing Koh; Gillian Dobbie
Venue: ICCV 2025 (main)

Abstract: Traditional image segmentation methods struggle with fine-grained pattern extraction, especially in an unsupervised setting without labeled data. Shallow and deep learning approaches either lack structural coherence or focus on object-level segmentation rather than internal textures. Additionally, existing methods often fail to generalize across diverse animal species due to variations in pattern complexity and lighting variations. We introduce GloPER, an unsupervised segmentation framework that extracts fine-grained animal patterns without labeled supervision. By enforcing local image reconstruction with only two colors per region, GloPER captures structured patterns while mitigating the effects of shadows and lighting inconsistencies. Given the lack of fine-detailed labeled data, we construct a dataset of 10 animal species, each with at least 100 well labeled images, enabling direct segmentation assessment. Experimental results show that GloPER outperforms both shallow and deep segmentation baselines, with a 42.44% higher DICE score on average across all 10 animal species. We also assess its effectiveness through animal re-identification (ReID), where GloPER's extracted binary patterns achieve superior accuracy, in some cases exceeding full-image ReID performance, underscoring the discriminative power of structured segmentation.
One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory
Authors: Chenhao Zheng; Jieyu Zhang; Mohammadreza Salehi; Ziqi Gao; Vishnu Iyengar; Norimasa Kobori; Quan Kong; Ranjay Krishna
Venue: ICCV 2025 (main)

Abstract: Effective video tokenization is critical for scaling transformer models for long videos. Current approaches tokenize videos using space-time patches, leading to excessive tokens and computational inefficiencies. The best token reduction strategies degrade performance and barely reduce the number of tokens when the camera moves. We introduce grounded video tokenization, a paradigm that organizes tokens based on panoptic sub-object trajectories rather than fixed patches. Our method aligns with fundamental perceptual principles, ensuring that tokenization reflects scene complexity rather than video duration. We propose TrajViT, a video encoder that extracts object trajectories and converts them into semantically meaningful tokens, significantly reducing redundancy while maintaining temporal coherence. Trained with contrastive learning, TrajViT significantly outperforms space-time ViT (ViT3D) across multiple video understanding benchmarks, e.g., TrajViT outperforms ViT3D by a large margin of 6% top-5 recall in average at video-text retrieval task with 10x token deduction. We also show TrajViT as a stronger model than ViT3D for being the video encoder for modern VideoLLM, obtaining an average of 5.2% performance improvement across 6 VideoQA benchmarks while having 4x faster training time and 20x less inference FLOPs. TrajViT is the first efficient encoder to consistently outperform ViT3D across diverse video analysis tasks, making it a robust and scalable solution.
Kaleidoscopic Background Attack: Disrupting Pose Estimation with Multi-Fold Radial Symmetry Textures
Authors: Xinlong Ding; Hongwei Yu; Jiawei Li; Feifan Li; Yu Shang; Bochao Zou; Huimin Ma; Jiansheng Chen
Venue: ICCV 2025 (main)

Abstract: Camera pose estimation is a fundamental computer vision task that is essential for applications like visual localization and multi-view stereo reconstruction. In the object-centric scenarios with sparse inputs, the accuracy of pose estimation can be significantly influenced by background textures that occupy major portions of the images across different viewpoints. In light of this, we introduce the Kaleidoscopic Background Attack (KBA), which uses identical segments to form discs with multi-fold radial symmetry. These discs maintain high similarity across different viewpoints, enabling effective attacks on pose estimation models even with natural texture segments. Additionally, a projected orientation consistency loss is proposed to optimize the kaleidoscopic segments, leading to a significant enhancement in the attack effectiveness. Experimental results show that optimized adversarial kaleidoscopic backgrounds can effectively attack various camera pose estimation models.
COSTARR: Consolidated Open Set Technique with Attenuation for Robust Recognition
Authors: Ryan Rabinowitz; Steve Cruz; Walter Scheirer; Terrance E. Boult
Venue: ICCV 2025 (main)

Abstract: Handling novelty remains a key challenge in visual recognition systems. Existing open-set recognition (OSR) methods rely on the familiarity hypothesis, detecting novelty by the absence of familiar features. We propose a novel attenuation hypothesis: small weights learned during training attenuate features and serve a dual role--differentiating known classes while discarding information useful for distinguishing known from unknown classes. To leverage this overlooked information, we present COSTARR, a novel approach that combines both the requirement of familiar features and the lack of unfamiliar ones. We provide a probabilistic interpretation of the COSTARR score, linking it to the likelihood of correct classification and belonging in a known class. To determine the individual contributions of the pre- and post-attenuated features to COSTARR's performance, we conduct ablation studies that show both pre-attenuated deep features and the underutilized post-attenuated Hadamard product features are essential for improving OSR. Also, we evaluate COSTARR in a large-scale setting using ImageNet2012-1K as known data and NINCO, iNaturalist, OpenImage-O, and other datasets as unknowns, across multiple modern pre-trained architectures (ViTs, ConvNeXts, and ResNet). The experiments demonstrate that COSTARR generalizes effectively across various architectures and significantly outperforms prior state-of-the-art methods by incorporating previously discarded attenuation information, advancing open-set recognition capabilities.
Agreement aware and dissimilarity oriented GLOM
Authors: Ru Zeng; Yan Song; Yang Zhang; Yanling Hu; Hui Yu
Venue: ICCV 2025 (main)

Abstract: GLOM, an innovative departure from standard deep learning architectures, has been proposed and gained special concern recently due to its good interpretability in representing part-whole relationships in computer vision. However, GLOM faces challenges in achieving agreement and is usually computationally demanding. First, current implementations struggle to produce identical vectors that reliably converge to represent nodes in a parse tree. Second, GLOM is computationally intensive due to the need to maintain equal resolution across all levels. To address these issues, inspired by contrastive learning, we proposed a contrastive agreement enhancer (CAE), which effectively promotes agreement between positive embedding pairs while pushing apart negative pairs, thereby facilitating forming distinct "islands." Furthermore, we introduce a dissimilarity-focused head (H_d) to reduce redundancy in the top-level embeddings, where embedding weights for downsampling are negatively correlated with similarity within a sliding window. The results of comparison experiments indicate that the proposed approach delicately retains informative content and significantly reduces the number of parameters. Additionally, the ablation experiments and visualization results demonstrate that CAE successfully promotes islands of agreement.
Att-Adapter: A Robust and Precise Domain-Specific Multi-Attributes T2I Diffusion Adapter via Conditional Variational Autoencoder
Authors: Wonwoong Cho; Yan-Ying Chen; Matthew Klenk; David I. Inouye; Yanxia Zhang
Venue: ICCV 2025 (main)

Abstract: Text-to-Image (T2I) Diffusion Models have achieved remarkable performance in generating high quality images. However, enabling precise control of continuous attributes, especially multiple attributes simultaneously, in a new domain (e.g., numeric values like eye openness or car width) with text-only guidance remains a significant challenge. To address this, we introduce the **Attribute (Att) Adapter**, a novel plug-and-play module designed to enable fine-grained, multi-attributes control in pretrained diffusion models. Our approach learns a single control adapter from a set of sample images that can be unpaired and contain multiple visual attributes. The Att-Adapter leverages the decoupled cross attention module to naturally harmonize the multiple domain attributes with text conditioning.We further introduce Conditional Variational Autoencoder (CVAE) to the Att-Adapter to mitigate overfitting, matching the diverse nature of the visual world.Evaluations on two public datasets show that Att-Adapter outperforms all LoRA-based baselines in controlling continuous attributes. Additionally, our method enables a broader control range and also improves disentanglement across multiple attributes, surpassing StyleGAN-based techniques. Notably, Att-Adapter is flexible, requiring no paired synthetic data for training, and is easily scalable to multiple attributes within a single model.
PERSONA: Personalized Whole-Body 3D Avatar with Pose-Driven Deformations from a Single Image
Authors: Geonhee Sim; Gyeongsik Moon
Venue: ICCV 2025 (main)

Abstract: Two major approaches exist for creating animatable human avatars. The first, a 3D-based approach, optimizes a NeRF- or 3DGS-based avatar from videos of a single person, achieving personalization through a disentangled identity representation. However, modeling pose-driven deformations, such as non-rigid cloth deformations, requires numerous pose-rich videos, which are costly and impractical to capture in daily life. The second, a diffusion-based approach, learns pose-driven deformations from large-scale in-the-wild videos but struggles with identity preservation and pose-dependent identity entanglement. We present PERSONA, a framework that combines the strengths of both approaches to obtain a personalized 3D human avatar with pose-driven deformations from a single image. PERSONA leverages a diffusion-based approach to generate pose-rich videos from the input image and optimizes a 3D avatar based on them. To ensure high authenticity and sharp renderings across diverse poses, we introduce balanced sampling and geometry-weighted optimization. Balanced sampling oversamples the input image to mitigate identity shifts in diffusion-generated training videos. Geometry-weighted optimization prioritizes geometry constraints over image loss, preserving rendering quality in diverse poses.
PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction
Authors: Jiahui Ren; Mochu Xiang; Jiajun Zhu; Yuchao Dai
Venue: ICCV 2025 (main)

Abstract: Wide-baseline panorama reconstruction has emerged as a highly effective and pivotal approach for not only achieving geometric reconstruction of the surrounding 3D environment, but also generating highly realistic and immersive novel views. Although existing methods have shown remarkable performance across various benchmarks, they are predominantly reliant on accurate pose information. In practical real-world scenarios, the acquisition of precise pose often requires additional computational resources and is highly susceptible to noise. These limitations hinder the broad applicability and practicality of such methods. In this paper, we present PanoSplatt3R, an unposed wide-baseline panorama reconstruction method. We extend and adapt the foundational reconstruction pretrainings from the perspective domain to the panoramic domain, thus enabling powerful generalization capabilities. To ensure a seamless and efficient domain-transfer process, we introduce RoPE rolling that spans rolled coordinates in rotary positional embeddings across different attention heads, maintaining a minimal modification to RoPE's mechanism, while modeling the horizontal periodicity of panorama images.Comprehensive experiments demonstrate that PanoSplatt3R, even in the absence of pose information, significantly outperforms current state-of-the-art methods. This superiority is evident in both the generation of high-quality novel views and the accuracy of depth estimation, thereby showcasing its great potential for practical applications.
CCMNet: Leveraging Calibrated Color Correction Matrices for Cross-Camera Color Constancy
Authors: Dongyoung Kim; Mahmoud Afifi; Dongyun Kim; Michael S. Brown; Seon Joo Kim
Venue: ICCV 2025 (main)

Abstract: Computational color constancy, or white balancing, is a key module in a camera's image signal processor (ISP) that corrects color casts from scene lighting. Because this operation occurs in the camera-specific raw color space, white balance algorithms must adapt to different cameras. This paper introduces a learning-based method for cross-camera color constancy that generalizes to new cameras without retraining. Our method leverages pre-calibrated color correction matrices (CCMs) available on ISPs that map the camera's raw color space to a standard space (e.g., CIE XYZ). Our method uses these CCMs to transform predefined illumination colors (i.e., along the Planckian locus) into the test camera's raw space. The mapped illuminants are encoded into a compact camera fingerprint embedding (CFE) that enables the network to adapt to unseen cameras. To prevent overfitting due to limited cameras and CCMs during training, we introduce a data augmentation technique that interpolates between cameras and their CCMs. Experimental results across multiple datasets and backbones show that our method achieves state-of-the-art cross-camera color constancy while remaining lightweight and relying only on data readily available in camera ISPs.
Seeing 3D Through 2D Lenses: 3D Few-Shot Class-Incremental Learning via Cross-Modal Geometric Rectification
Authors: Tuo Xiang; Xuemiao Xu; Bangzhen Liu; Jinyi Li; Yong Li; Shengfeng He
Venue: ICCV 2025 (main)

Abstract: The rapid growth of 3D digital content necessitates expandable recognition systems for open-world scenarios. However, existing 3D class-incremental learning methods struggle under extreme data scarcity due to geometric misalignment and texture bias. While recent approaches integrate 3D data with 2D foundation models (e.g., CLIP), they suffer from semantic blurring caused by texture-biased projections and indiscriminate fusion of geometric-textural cues, leading to unstable decision prototypes and catastrophic forgetting. To address these issues, we propose Cross-Modal Geometric Rectification (CMGR), a framework that enhances 3D geometric fidelity by leveraging CLIP's hierarchical spatial semantics. Specifically, we introduce a Structure-Aware Geometric Rectification module that hierarchically aligns 3D part structures with CLIP's intermediate spatial priors through attention-driven geometric fusion. Additionally, a Texture Amplification Module synthesizes minimal yet discriminative textures to suppress noise and reinforce cross-modal consistency. To further stabilize incremental prototypes, we employ a Base-Novel Discriminator that isolates geometric variations. Extensive experiments demonstrate that our method significantly improves 3D few-shot class-incremental learning, achieving superior geometric coherence and robustness to texture bias across cross-domain and within-domain settings.
Secure On-Device Video OOD Detection Without Backpropagation
Authors: Shawn Li; Peilin Cai; Yuxiao Zhou; Zhiyu Ni; Renjie Liang; You Qin; Yi Nian; Zhengzhong Tu; Xiyang Hu; Yue Zhao
Venue: ICCV 2025 (main)

Abstract: Out-of-Distribution (OOD) detection is critical for ensuring the reliability of machine learning models in safety-critical applications such as autonomous driving and medical diagnosis. While deploying personalized OOD detection directly on edge devices is desirable, it remains challenging due to large model sizes and the computational infeasibility of on-device training. Federated learning partially addresses this but still requires gradient computation and backpropagation, exceeding the capabilities of many edge devices.To overcome these challenges, we propose SecDOOD, a secure cloud-device collaboration framework for efficient on-device OOD detection without requiring device-side backpropagation.SecDOOD utilizes cloud resources for model training while ensuring user data privacy by retaining sensitive information on-device. Central to SecDOOD is a HyperNetwork-based personalized parameter generation module, which adapts cloud-trained models to device-specific distributions by dynamically generating local weight adjustments, effectively combining central and local information without local fine-tuning. Additionally, our dynamic feature sampling and encryption strategy selectively encrypts only the most informative feature channels, largely reducing encryption overhead without compromising detection performance.Extensive experiments across multiple datasets and OOD scenarios demonstrate that SecDOOD achieves performance comparable to fully fine-tuned models, enabling secure, efficient, and personalized OOD detection on resource-limited edge devices. To enhance accessibility and reproducibility, our code is publicly available at https://github.com/Dystopians/SecDOOD.
SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion
Authors: Ahmed Nassar; Matteo Omenetti; Maksym Lysak; Nikolaos Livathinos; Christoph Auer; Lucas Morin; Rafael Teixeira de Lima; Yusik Kim; A. Said Gurbuz; Michele Dolfi; Peter W. J. Staar
Venue: ICCV 2025 (main)

Abstract: We introduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion. Our model comprehensively processes entire pages by generating DocTags, a new universal markup format that captures all page elements in their full context with location. Unlike existing approaches that rely on large foundational models, or ensemble solutions that rely on handcrafted pipelines of multiple specialized models, SmolDocling offers an end-to-end conversion for accurately capturing content, structure and spatial location of document elements in a 256M parameters vision-language model. SmolDocling exhibits robust performance in correctly reproducing document features such as code listings, tables, equations, charts, lists, and more across a diverse range of document types including business documents, academic papers, technical reports, patents, and forms -- significantly extending beyond the commonly observed focus on scientific papers. Additionally, we contribute novel publicly sourced datasets for charts, tables, equations, and code recognition.Experimental results demonstrate that SmolDocling competes with other Vision Language Models that are up to 27 times larger in size, while reducing computational requirements substantially. The model weights and datasets are available at: https://huggingface.co/ds4sd