GenAI4ELab/papercli-papers huggingface dataset:
Towards Cross-modal Backward-compatible Representation Learning for Vision-Language Models
Authors: Young Kyun Jang; Ser-nam Lim
Venue: ICCV 2025 (main)

Abstract: Modern retrieval systems often struggle with upgrading to new and more powerful models due to the incompatibility of embeddings between the old and new models. This necessitates a costly process known as backfilling, which involves re-computing the embeddings for a large number of data samples. In vision, Backward-compatible Training (BT) has been proposed to ensure that the new model aligns with the old model's embeddings. This paper extends the concept of vision-only BT to the field of cross-modal retrieval, marking the first attempt to address Cross-modal BT (XBT). Our goal is to achieve backward-compatibility between Vision-Language Pretraining (VLP) models, such as CLIP, for the cross-modal retrieval task. To address XBT challenges, we propose an efficient solution: a projection module that maps the new model's embeddings to those of the old model. This module, pretrained solely with text data, significantly reduces the number of image-text pairs required for XBT learning, and, once it is pretrained, it avoids using the old model during training. Furthermore, we utilize parameter-efficient training strategies that improve efficiency and preserve the off-the-shelf new model's knowledge by avoiding any modifications. Experimental results on cross-modal retrieval datasets demonstrate the effectiveness of XBT and its potential to enable backfill-free upgrades when a new VLP model emerges.
Fuse Before Transfer: Knowledge Fusion for Heterogeneous Distillation
Authors: Guopeng Li; Qiang Wang; Ke Yan; Shouhong Ding; Yuan Gao; Gui-Song Xia
Venue: ICCV 2025 (main)

Abstract: Most knowledge distillation (KD) methods focus on teacher-student pairs with similar architectures, such as both being CNN models. The potential and flexibility of KD can be greatly improved by expanding it to Cross-Architecture KD (CAKD), where the knowledge of homogeneous and heterogeneous teachers can be distilled selectively. However, substantial feature gaps between heterogeneous models (e.g., ViT teacher v.s. CNN student) make CAKD extremely challenging, caused by the distinction of inherent inductive biases and module functions. To this end, we fuse heterogeneous knowledge before transferring it from teacher to student. This fusion combines the advantages of both cross-architecture inductive biases and module functions by merging different combinations of convolution, attention, and MLP modules derived directly from student and teacher module functions. Furthermore, heterogeneous features exhibit diverse spatial distributions, hindering the effectiveness of conventional pixel-wise MSE loss. Therefore, we replace it with a spatial-agnostic InfoNCE loss. Our method is evaluated across various homogeneous models and arbitrary heterogeneous combinations of CNNs, ViTs, and MLPs, yielding promising performance for distilled models with a maximum gain of 11.47% on CIFAR-100 and 3.67% on ImageNet-1K. Code is available at https://github.com/liguopeng0923/FBT.
CoLMDriver: LLM-based Negotiation Benefits Cooperative Autonomous Driving
Authors: Changxing Liu; Genjia Liu; Zijun Wang; Jinchang Yang; Siheng Chen
Venue: ICCV 2025 (main)

Abstract: Vehicle-to-vehicle (V2V) cooperative autonomous driving holds great promise for improving safety by addressing the perception and prediction uncertainties inherent in single-agent systems. However, traditional cooperative methods are constrained by rigid collaboration protocols and limited generalization to unseen interactive scenarios. While LLM-based approaches offer generalized reasoning capabilities, their challenges in spatial planning and unstable inference latency hinder their direct application in cooperative driving. To address these limitations, we propose CoLMDriver, the first full-pipeline LLM-based cooperative driving system, enabling effective language-based negotiation and real-time driving control. CoLMDriver features a parallel driving pipeline with two key components: (i) an LLM-based negotiation module under an critic-feedback paradigm, which continuously refines cooperation policies through feedback from previous decisions of all vehicles; and (ii) an intention-guided waypoint generator, which translates negotiation outcomes into executable waypoints. Additionally, we introduce InterDrive, a CARLA-based simulation benchmark comprising 10 challenging interactive driving scenarios for evaluating V2V cooperation. Experimental results demonstrate that CoLMDriver significantly outperforms existing approaches, achieving an 11% higher success rate across diverse highly interactive V2V driving scenarios. Code is released on https://github.com/cxliu0314/CoLMDriver.
Dual Domain Control via Active Learning for Remote Sensing Domain Incremental Object Detection
Authors: Jiachen Sun; De Cheng; Xi Yang; Nannan Wang
Venue: ICCV 2025 (main)

Abstract: Domain incremental object detection in remote sensing addresses the challenge of adapting to continuously emerging domains with distinct characteristics. Unlike natural images, remote sensing data vary significantly due to differences in sensors, altitudes, and geographic locations, leading to data distribution shifts and feature misalignments. These challenges make it difficult for models to generalize across domains while retaining knowledge from previous tasks, requiring effective adaptation strategies to mitigate catastrophic forgetting. To address these challenges, we propose the Dual Domain Control via Active Learning (Active-DDC) method, which integrates active learning strategies to handle data distribution and model feature shifts. The first component, the Data-based Active Learning Example Replay (ALER) module, combines a high-information sample selection strategy from active learning with the characteristic extreme foreground-background ratio in remote sensing images, enabling the selection of highly representative samples for storage in a memory bank. The second component, the Query-based Active Domain Shift Control (ADSC) module, leverages the query vector, a key element for DETR-based detectors, to implement query active preselection and optimal transport matching, thus facilitating effective cross-domain knowledge transfer. Our method achieves optimal performance in domain incremental tasks across four remote sensing datasets, and ablation studies further validate the effectiveness of both components.
VideoSetDiff: Identifying and Reasoning Similarities and Differences in Similar Videos
Authors: Yue Qiu; Yanjun Sun; Takuma Yagi; Shusaku Egami; Natsuki Miyata; Ken Fukuda; Kensho Hara; Ryusuke Sagawa
Venue: ICCV 2025 (main)

Abstract: Recognizing subtle similarities and differences among sets of similar activities is central to many real-world applications, including skill acquisition, sports performance evaluation, and anomaly detection. Humans excel at such fine-grained analysis, which requires comprehensive video understanding and cross-video reasoning about action attributes, poses, positions, and emotional states. Yet existing video-based large language models typically address only single-video recognition, leaving their capacity for multi-video reasoning largely unexplored. We introduce VideoSetDiff, a curated dataset designed to test detail-oriented recognition across diverse activities, from subtle action attributes to viewpoint transitions. Our evaluation of current video-based LLMs on VideoSetDiff reveals critical shortcomings, particularly in fine-grained detail recognition and multi-video reasoning. To mitigate these issues, we propose an automatically generated dataset for instruction tuning alongside a novel multi-video recognition framework. While instruction tuning and specialized multi-video reasoning improve performance, all tested models remain far from satisfactory. These findings underscore the need for more robust video-based LLMs capable of handling complex multi-video tasks, enabling diverse real-world applications.
GRAB: A Challenging GRaph Analysis Benchmark for Large Multimodal Models
Authors: Jonathan Roberts; Kai Han; Samuel Albanie
Venue: ICCV 2025 (main)

Abstract: Large multimodal models (LMMs) have exhibited proficiencies across many visual tasks. Although numerous well-known benchmarks exist to evaluate model performance, they increasingly have insufficient headroom. As such, there is a pressing need for a new generation of benchmarks challenging enough for the next generation of LMMs. One area that LMMs show potential is graph analysis, specifically, the tasks an analyst might typically perform when interpreting figures such as estimating the mean, intercepts or correlations of functions and data series. In this work, we introduce GRAB, a graph analysis benchmark, fit for current and future frontier LMMs. Our benchmark is predominantly synthetic, ensuring high-quality, noise-free questions. GRAB is comprised of 3284 questions, covering five tasks and 23 graph properties. We evaluate 20 LMMs on GRAB, finding it to be a challenging benchmark, with the highest performing model attaining a score of just 21.0%. Finally, we conduct various ablations to investigate where the models succeed and struggle. We release GRAB and a lightweight GRAB-Lite to encourage progress in this important, growing domain.
Transparent Vision: A Theory of Hierarchical Invariant Representations
Authors: Shuren Qi; Yushu Zhang; Chao Wang; Zhihua Xia; Xiaochun Cao; Fenglei Fan
Venue: ICCV 2025 (main)

Abstract: Developing robust and interpretable vision systems is a crucial step towards trustworthy artificial intelligence. One promising paradigm is to design transparent structures, e.g., geometric invariance, for fundamental representations. However, such invariants exhibit limited discriminability, limiting their applications in larger-scale tasks. For this open problem, we conduct a systematic investigation of hierarchical invariance, exploring this topic from theoretical, practical, and application perspectives. At the theoretical level, we show how to construct discriminative invariants with a Convolutional Neural Network (CNN)-like hierarchical architecture, yet in a fully transparent manner. The general blueprint, specific definitions, invariant properties, and numerical implementations are provided. At the practical level, we discuss how to customize this transparent framework into a given task. With the over-completeness, discriminative features w.r.t. the task can be adaptively formed in a Neural Architecture Search (NAS)-like manner. We demonstrate the above arguments with accuracy, invariance, and efficiency results on laboratory-style classification experiments. Furthermore, at the application level, our representations are explored in real-world forensic tasks on adversarial perturbations and generated content. Such applications reveal that our invariants exhibit competitive discriminability even in the era of deep learning. For robust and interpretable vision tasks at larger scales, hierarchical invariant representations can be considered as an effective alternative to traditional CNNs and invariants.
Multi-View 3D Point Tracking
Authors: Frano Rajič; Haofei Xu; Marko Mihajlovic; Siyuan Li; Irem Demir; Emircan Gündoğdu; Lei Ke; Sergey Prokudin; Marc Pollefeys; Siyu Tang
Venue: ICCV 2025 (main)

Abstract: We introduce the first data-driven multi-view 3D point tracker, designed to track arbitrary points in dynamic scenes using multiple camera views. Unlike existing monocular trackers, which struggle with depth ambiguities and occlusion, or prior multi-camera methods that require over 20 cameras and tedious per-sequence optimization, our feed-forward model directly predicts 3D correspondences using a practical number of cameras (e.g., four), enabling robust and accurate online tracking. Given known camera poses and either sensor-based or estimated multi-view depth, our tracker fuses multi-view features into a unified point cloud and applies k-nearest-neighbors correlation alongside a transformer-based update to reliably estimate long-range 3D correspondences, even under occlusion. We train on 5K synthetic multi-view Kubric sequences and evaluate on two real-world benchmarks--Panoptic Studio and DexYCB--achieving median trajectory errors of 3.1 cm and 2.0cm, respectively. Our method generalizes well to diverse camera setups of 1-8 views with varying vantage points and video lengths of 24-150 frames. By releasing our tracker alongside training and evaluation datasets, we aim to set a new standard for multi-view 3D tracking research and provide a practical tool for real-world applications. Project page: https://ethz-vlg.github.io/mvtracker.
InsideOut: Integrated RGB-Radiative Gaussian Splatting for Comprehensive 3D Object Representation
Authors: Jungmin Lee; Seonghyuk Hong; Juyong Lee; Jaeyoon Lee; Jongwon Choi
Venue: ICCV 2025 (main)

Abstract: We introduce InsideOut, an extension of 3D Gaussian splatting (3DGS) that bridges the gap between high-fidelity RGB surface details and subsurface X-ray structures. The fusion of RGB and X-ray imaging is invaluable in fields such as medical diagnostics, cultural heritage restoration, and manufacturing. We collect new paired RGB and X-ray data, perform hierarchical fitting to align RGB and X-ray radiative Gaussian splats, and propose an X-ray reference loss to ensure consistent internal structures. InsideOut effectively addresses the challenges posed by disparate data representations between the two modalities and limited paired datasets. This approach significantly extends the applicability of 3DGS, enhancing visualization, simulation, and non-destructive testing capabilities across various domains.
Splat-LOAM: Gaussian Splatting LiDAR Odometry and Mapping
Authors: Emanuele Giacomini; Luca Di Giammarino; Lorenzo De Rebotti; Giorgio Grisetti; Martin R. Oswald
Venue: ICCV 2025 (main)

Abstract: LiDARs provide accurate geometric measurements, making them valuable for ego-motion estimation and reconstruction tasks.Although its success, managing an accurate and lightweight representation of the environment still poses challenges.Both classic and NeRF-based solutions have to trade off accuracy over memory and processing times.In this work, we build on recent advancements in Gaussian Splatting methods to develop a novel \lidar odometry and mapping pipeline that exclusively relies on Gaussian primitives for its scene representation.Leveraging spherical projection, we drive the refinement of the primitives uniquely from LiDAR measurements.Experiments show that our approach matches the current registration performance, while achieving SOTA results for mapping tasks with minimal GPU requirements. This efficiency makes it a strong candidate for further exploration and potential adoption in real-time robotics estimation tasks.
LUSD: Localized Update Score Distillation for Text-Guided Image Editing
Authors: Worameth Chinchuthakun; Tossaporn Saengja; Nontawat Tritrong; Pitchaporn Rewatbowornwong; Pramook Khungurn; Supasorn Suwajanakorn
Venue: ICCV 2025 (main)

Abstract: While diffusion models show promising results in image editing given a target prompt, achieving both prompt fidelity and background preservation remains difficult. Recent works have introduced score distillation techniques that leverage the rich generative prior of text-to-image diffusion models to solve this task without additional fine-tuning. However, these methods often struggle with tasks such as object insertion. Our investigation of these failures reveals significant variations in gradient magnitude and spatial distribution, making hyperparameter tuning highly input-specific or unsuccessful. To address this, we propose two simple yet effective modifications: attention-based spatial regularization and gradient filtering-normalization, both aimed at reducing these variations during gradient updates. Experimental results show our method outperforms state-of-the-art score distillation techniques in prompt fidelity, improving successful edits while preserving the background. Users also preferred our method over state-of-the-art techniques across three metrics, and by 58-64% overall.
ChartCap: Mitigating Hallucination of Dense Chart Captioning
Authors: Junyoung Lim; Jaewoo Ahn; Gunhee Kim
Venue: ICCV 2025 (main)

Abstract: Generating accurate, informative, and hallucination-free captions for charts remains challenging for vision language models, primarily due to the lack of large-scale, high-quality datasets of real-world charts. However, existing real-world chart datasets suffer from the inclusion of extraneous information that cannot be inferred from the chart and failure to sufficiently capture structural elements and key insights. Therefore, we introduce ChartCap, a large-scale dataset of 565K real-world chart images paired with type-specific, dense captions that exclude extraneous information and highlight both structural elements and key insights in detail. To build ChartCap, we design a four-stage pipeline that generates captions using only the discernible data from the chart and employ a cycle consistency-based human verification, which accelerates quality control without sacrificing accuracy. Additionally, we propose a novel metric, the Visual Consistency Score, which evaluates caption quality by measuring the similarity between the chart regenerated from a caption and the original chart, independent of reference captions. Extensive experiments confirms that models fine-tuned on ChartCap consistently generate more accurate and informative captions with reduced hallucinations, surpassing both open-source and proprietary models and even human-annotated captions.
RTMap: Real-Time Recursive Mapping with Change Detection and Localization
Authors: Yuheng Du; Sheng Yang; Lingxuan Wang; Zhenghua Hou; Chengying Cai; Zhitao Tan; Mingxia Chen; Shi-Sheng Huang; Qiang Li
Venue: ICCV 2025 (main)

Abstract: While recent online HD mapping methods relieve burdened offline pipelines and solve map freshness, they remain limited by perceptual inaccuracies, occlusion in dense traffic, and an inability to fuse multi-agent observations. We propose RTMap to enhance these single-traversal methods by persistently crowdsourcing a multi-traversal HD map as a self-evolutional memory. On onboard agents, RTMap simultaneously addresses three core challenges in an end-to-end fashion: (1) Uncertainty-aware positional modeling for HD map elements, (2) probabilistic-aware localization w.r.t. the crowdsourced prior-map, and (3) real-time detection for possible road structural changes. Experiments on several public autonomous driving datasets demonstrate our solid performance on both the prior-aided map quality and the localization accuracy, demonstrating our effectiveness of robustly serving downstream prediction and planning modules while gradually improving the accuracy and freshness of the crowdsourced prior-map asynchronously. Our source-code will be made publicly available at https://github.com/CN-ADLab/RTMap.
Scaling 3D Compositional Models for Robust Classification and Pose Estimation
Authors: Xiaoding Yuan; Guofeng Zhang; Prakhar Kaushik; Artur Jesslen; Adam Kortylewski; Alan Yuille
Venue: ICCV 2025 (main)

Abstract: Deep learning algorithms for object classification and 3D object pose estimation lack robustness to out-of-distribution factors such as synthetic stimuli, changes in weather conditions, and partial occlusion. Recently, a class of Neural Mesh Models have been developed where objects are represented in terms of 3D meshes with learned features at the vertices. These models have shown robustness in small-scale settings, involving 10 objects, but it is unclear that they can be scaled up to 100s of object classes. The main problem is that their training involves contrastive learning among the vertices of all object classes, which scales quadratically with the number of classes. We present a strategy which exploits the compositionality of the objects, i.e. the independence of the feature vectors of the vertices, which greatly reduces the training time while also improving the performance of the algorithms. We first restructure the per-vertex contrastive learning into contrasting within class and between classes. Then we propose a process that dynamically decouples the contrast between classes which are rarely confused, and enhances the contrast between the vertices of classes that are most confused. Our large-scale 3D compositional model not only achieves state-of-the-art performance on the task of predicting classification and pose estimation simultaneously, surpassing the Neural Mesh Models and standard DNNs, but is also more robust to out-of-distribution testing including occlusion, weather conditions, synthetic data, and generalization to unknown classes.
MultiVerse: A Multi-Turn Conversation Benchmark for Evaluating Large Vision and Language Models
Authors: Young-Jun Lee; Byung-Kwan Lee; Jianshu Zhang; Yechan Hwang; Byungsoo Ko; Han-Gyu Kim; Dongyu Yao; Xuankun Rong; Eojin Joo; Seung-Ho Han; Bowon Ko; Ho-Jin Choi
Venue: ICCV 2025 (main)

Abstract: Vision-and-Language Models (VLMs) have shown impressive capabilities on single-turn benchmarks, yet real-world applications often demand more intricate multi-turn dialogues. Existing multi-turn datasets (e.g, MMDU, ConvBench) only partially capture the breadth and depth of conversational scenarios encountered by users. In this work, we introduce MultiVerse, a novel multi-turn conversation benchmark featuring 647 dialogues--each averaging four turns--derived from a diverse set of 12 popular VLM evaluation benchmarks. With 484 tasks and 484 interaction goals, MultiVerse covers a wide range of topics, from factual knowledge and perception to advanced reasoning tasks such as mathematics and coding. To facilitate robust assessment, we propose a checklist-based evaluation method that leverages GPT-4o as the automated evaluator, measuring performance across 37 key aspects, including perceptual accuracy, linguistic clarity, and factual correctness. We evaluate 18 VLMs on MultiVerse, revealing that even the strongest models (e.g., GPT-4o) achieve only a 50% success rate in complex multi-turn conversations, highlighting the dataset's challenging nature. Notably, we find that providing full dialogue context significantly enhances performance for smaller or weaker models, emphasizing the importance of in-context learning. We believe MultiVerse is a landscape of evaluating multi-turn interaction abilities for VLMs.
Contact-Aware Refinement of Human Pose Pseudo-Ground Truth via Bioimpedance Sensing
Authors: Maria-Paola Forte; Nikos Athanasiou; Giulia Ballardini; Jan Ulrich Bartels; Katherine J. Kuchenbecker; Michael J. Black
Venue: ICCV 2025 (main)

Abstract: Capturing accurate 3D human pose in the wild would provide valuable data for training motion-generation and pose-estimation methods. While video-based estimation approaches have become increasingly accurate, they often fail in common scenarios involving self-contact, such as a hand touching the face. In contrast, wearable bioimpedance sensing can cheaply and unobtrusively measure ground-truth skin-to-skin contact. Consequently, we propose a novel framework that combines visual pose estimators with bioimpedance sensing to capture the 3D pose of people by taking self-contact into account. Our method, BioTUCH, initializes the pose using an off-the-shelf estimator and introduces contact-aware pose optimization during measured self-contact: reprojection error and deviations from the input estimate are minimized while enforcing vertex proximity constraints. We validate our approach using a new dataset of synchronized RGB video, bioimpedance measurements, and 3D motion capture. Testing with three input pose estimators, we demonstrate an average of 11.7% improvement in reconstruction accuracy. We also present a miniature wearable bioimpedance sensor that enables efficient large-scale collection of contact-aware training data for improving pose estimation and generation using BioTUCH. Code and data are available at biotuch.is.tue.mpg.de