GenAI4ELab/papercli-papers huggingface dataset:
Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness
Authors: Qifan Yu; Zhebei Shen; Zhongqi Yue; Yang Wu; Bosheng Qin; Wenqiao Zhang; Yunfei Li; Juncheng Li; Siliang Tang; Yueting Zhuang
Venue: ICCV 2025 (main)

Abstract: Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to handle real-world tasks. However, the rapid expansion of visual instruction datasets introduces data redundancy, leading to excessive computational costs. We propose a collaborative framework, DataTailor, which leverages three key principles--informativeness, uniqueness, and representativeness--for effective data selection. We argue that a valuable sample should be informative of the task, non-redundant, and represent the sample distribution (i.e., not an outlier). We further propose practical ways to score against each principle, which automatically adapts to a given dataset without tedious hyperparameter tuning. Comprehensive experiments on various benchmarks demonstrate that DataTailor achieves 101.3% of the performance of full-data fine-tuning with only 15% of the data, significantly reducing computational costs while maintaining superior results. This exemplifies the "Less is More" philosophy in MLLM development. The code and data is available in this URL.
WIR3D: Visually-Informed and Geometry-Aware 3D Shape Abstraction
Authors: Richard Liu; Daniel Fu; Noah Tan; Itai Lang; Rana Hanocka
Venue: ICCV 2025 (main)

Abstract: In this work we present WIR3D, a technique for abstracting 3D shapes through a sparse set of visually meaningful curves in 3D. We optimize the parameters of Bezier curves such that they faithfully represent both the geometry and salient visual features (e.g. texture) of the shape from arbitrary viewpoints. We leverage the intermediate activations of a pre-trained foundation model (CLIP) to guide our optimization process. We divide our optimization into two phases: one for capturing the coarse geometry of the shape, and the other for representing fine-grained features. Our second phase supervision is spatially guided by a novel localized keypoint loss. This spatial guidance enables user control over abstracted features. We ensure fidelity to the original surface through a neural SDF loss, which allows the curves to be used as intuitive deformation handles. We successfully apply our method for shape abstraction over a broad dataset of shapes with varying complexity, geometric structure, and texture, and demonstrate downstream applications for feature control and shape deformation.
XTrack: Multimodal Training Boosts RGB-X Video Object Trackers
Authors: Yuedong Tan; Zongwei Wu; Yuqian Fu; Zhuyun Zhou; Guolei Sun; Eduard Zamfir; Chao Ma; Danda Paudel; Luc Van Gool; Radu Timofte
Venue: ICCV 2025 (main)

Abstract: Multimodal sensing has proven valuable for visual tracking, as different sensor types offer unique strengths in handling one specific challenging scene where object appearance varies. While a generalist model capable of leveraging all modalities would be ideal, development is hindered by data sparsity, typically in practice, only one modality is available at a time. Therefore, it is crucial to ensure and achieve that knowledge gained from multimodal sensing -- such as identifying relevant features and regions -- is effectively shared, even when certain modalities are unavailable at inference. We venture with a simple assumption: similar samples across different modalities have more knowledge to share than otherwise. To implement this, we employ a classifier with weak loss tasked with distinguishing between modalities. More specifically, if the classifier "fails" to accurately identify the modality of the given sample, this signals an opportunity for cross-modal knowledge sharing. Intuitively, knowledge transfer is facilitated whenever a sample from one modality is sufficiently close and aligned with another. Technically, we achieve this by routing samples from one modality to the expert of the others, within a mixture-of-experts framework designed for multimodal video object tracking. During the inference, the expert of the respective modality is chosen, which we show to benefit from the multimodal knowledge available during training, thanks to the proposed method. Through the exhaustive experiments that use only paired RGB-E, RGB-D, and RGB-T during training, we showcase the benefit of the proposed method for RGB-X tracker during inference, with an average +3% precision improvement over the current SOTA. The source code is publicly available at https://github.com/supertyd/XTrack.
FaceCraft4D: Animated 3D Facial Avatar Generation from a Single Image
Authors: Fei Yin; Mallikarjun B R; Chun-Han Yao; Rafal K. Mantiuk; Varun Jampani
Venue: ICCV 2025 (main)

Abstract: We present a novel framework for generating high-quality, animatable 4D avatar from a single image. While recent advances have shown promising results in 4D avatar creation, existing methods either require extensive multiview data or struggle with geometry accuracy and identity consistency. To address these limitations, we propose a comprehensive system that leverages geometry, image, and video priors to create full-view, animatable avatars. Our approach first obtains initial coarse geometry through 3D-GAN inversion. Then, it enhances multiview textures using depth-guided warping signals for cross-view consistency with the help of the image diffusion model. To handle expression animation, we incorporate a video prior with synchronized driving signals across viewpoints. We further introduce a Consistent-Inconsistent training to effectively handle data inconsistencies during 4D reconstruction. Experimental results demonstrate that our method achieves superior quality compared to the prior art, while maintaining consistency across different viewpoints and expressions.
GenFlow3D: Generative Scene Flow Estimation and Prediction on Point Cloud Sequences
Authors: Hanlin Li; Wenming Weng; Yueyi Zhang; Zhiwei Xiong
Venue: ICCV 2025 (main)

Abstract: Scene flow provides the fundamental information of the scene dynamics. Existing scene flow estimation methods typically rely on the correlation between only a consecutive point cloud pair, which makes them limited to the instantaneous state of the scene and face challenges in real-world scenarios with factors like occlusion, noise, and diverse motion of background and foreground. In this paper, we study the joint sequential scene flow estimation and future scene flow prediction on point cloud sequences. The expanded sequential input introduces long-term and high-order motion information. We propose GenFlow3D, a recurrent neural network model which integrates diffusion in the decoder to better incorporate the two tasks and enhance the ability to extract general motion patterns. A transformer-based denoising network is adopted to help capture useful information. Depending on the input point clouds, discriminative condition signals are generated to guide the diffusion decoder to switch among different modes specific for scene flow estimation and prediction in a multi-scale manner. GenFlow3D is evaluated on the real-world datasets nuScenes and Argoverse 2, and demonstrates superior performance compared with the existing methods. Our code is available at https://github.com/ustc-hlli/GenFlow3D.
Attention to Neural Plagiarism: Diffusion Models Can Plagiarize Your Copyrighted Images!
Authors: Zihang Zou; Boqing Gong; Liqiang Wang
Venue: ICCV 2025 (main)

Abstract: In this paper, we highlight a critical threat posed by emerging neural models--data plagiarism. We demonstrate how modern neural models (e.g., diffusion models) can effortlessly replicate copyrighted images, even when protected by advanced watermarking techniques. To expose the vulnerability in copyright protection and facilitate future research, we propose a general approach regarding neural plagiarism that can either forge replicas of copyrighted data or introduce copyright ambiguity. Our method, based on "anchors and shims", employs inverse latents as anchors and finds shim perturbations that can gradually deviate the anchor latents, thereby evading watermark or copyright detection. By applying perturbation to the cross-attention mechanism at different timesteps, our approach induces varying degrees of semantic modifications in copyrighted images, making it to bypass protections ranging from visible trademarks, signatures to invisible watermarks. Notably, our method is a purely gradient-based search that requires no additional training or fine-tuning. Empirical experiments on MS-COCO and real-world copyrighted images show that diffusion models can replicate copyrighted images, underscoring the urgent need for countermeasures against neural plagiarism. Source code is available at: https://github.com/zzzucf/Neural-Plagiarism.
PBCAT: Patch-Based Composite Adversarial Training against Physically Realizable Attacks on Object Detection
Authors: Xiao Li; Yiming Zhu; Yifan Huang; Wei Zhang; Yingzhe He; Jie Shi; Xiaolin Hu
Venue: ICCV 2025 (main)

Abstract: Object detection plays a crucial role in many security-sensitive applications, such as autonomous driving and video surveillance. However, several recent studies have shown that object detectors can be easily fooled by physically realizable attacks, e.g., adversarial patches and recent adversarial textures, which pose realistic and urgent threats. Adversarial Training (AT) has been recognized as the most effective defense against adversarial attacks. While AT has been extensively studied in the l_infinity-bounded attack settings on classification models, AT against physically realizable attacks on object detectors has received limited exploration. Early attempts are only performed to defend against adversarial patches, leaving AT against a wider range of physically realizable attacks under-explored. In this work, we consider defending against various physically realizable attacks with a unified AT method. We propose PBCAT, a novel Patch-Based Composite Adversarial Training strategy. PBCAT optimizes the model by incorporating the combination of small-area gradient-guided adversarial patches and imperceptible global adversarial perturbations covering the entire image. With these designs, PBCAT has the potential to defend against not only adversarial patches but also unseen physically realizable attacks such as adversarial textures. Extensive experiments in multiple settings demonstrated that PBCAT significantly improved robustness against various physically realizable attacks over state-of-the-art defense methods. Notably, it improved the detection accuracy by 29.7% over previous defense methods under one recent adversarial texture attack.
TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models
Authors: Ruidong Chen; Honglin Guo; Lanjun Wang; Chenyu Zhang; Weizhi Nie; An-An Liu
Venue: ICCV 2025 (main)

Abstract: Recent advances in text-to-image diffusion models enable photorealistic image generation, but they also risk producing malicious content, such as NSFW images. To mitigate risk, concept erasure methods are studied to facilitate the model to unlearn specific concepts. However, current studies struggle to fully erase malicious concepts implicitly embedded in prompts (e.g., metaphorical expressions or adversarial prompts) while preserving the model's normal generation capability. To address this challenge, our study proposes TRCE, using a two-stage concept erasure strategy to achieve an effective trade-off between reliable erasure and knowledge preservation. Firstly, TRCE starts by erasing the malicious semantics implicitly embedded in textual prompts. By identifying a critical mapping objective(i.e., the [EoT] embedding), we optimize the cross-attention layers to map malicious prompts to contextually similar prompts but with safe concepts. This step prevents the model from being overly influenced by malicious semantics during the denoising process. Following this, considering the deterministic properties of the sampling trajectory of the diffusion model, TRCE further steers the early denoising prediction toward the safe direction and away from the unsafe one through contrastive learning, thus further avoiding the generation of malicious content. Finally, we conduct comprehensive evaluations of TRCE on multiple malicious concept erasure benchmarks, and the results demonstrate its effectiveness in erasing malicious concepts while better preserving the model's original generation ability.
PossLoss: A Reliable and Sensitive Facial Landmark Detection Loss Function
Authors: Qikui Zhu
Venue: ICCV 2025 (main)

Abstract: ate and locate the peak of the heatmap, while adaptively balancing the influence of landmarks and background pixels through self-weighting, addressing the extreme imbalance between landmarks and non-landmarks. More advanced is that our PossLoss is sample-sensitive, which can distinguish easy and hard landmarks and adaptively make the model focused more on hard landmarks. Moreover, it addresses the difficulty of accurately evaluating heatmap distribution, especially in addressing small errors due to peak mismatches. We analyzed and evaluated our PossLoss on three challenging facial landmark detection tasks. The experimental results show that our PossLoss significantly improves the performance of landmark detection and outperforms the state-of-the-art methods.
ChartPoint: Guiding MLLMs with Grounding Reflection for Chart Reasoning
Authors: Zhengzhuo Xu; SiNan Du; Yiyan Qi; Siwen Lu; Chengjin Xu; Chun Yuan; Jian Guo
Venue: ICCV 2025 (main)

Abstract: Multimodal Large Language Models (MLLMs) have emerged as powerful tools for chart comprehension. However, they heavily rely on extracted content via OCR, which leads to numerical hallucinations when chart textual annotations are sparse. While existing methods focus on scaling instructions, they fail to address the fundamental challenge, i.e., reasoning with visual perception. In this paper, we identify a critical observation: MLLMs exhibit weak grounding in chart elements and proportional relationships, as evidenced by their inability to localize key positions to match their reasoning. To bridge this gap, we propose PointCoT, which integrates reflective interaction into chain-of-thought reasoning in charts. By prompting MLLMs to generate bounding boxes and re-render charts based on location annotations, we establish connections between textual reasoning steps and visual grounding regions. We further introduce an automated pipeline to construct ChartPoint-SFT-62k, a dataset featuring 19.2K high-quality chart samples with step-by-step CoT, bounding box, and re-rendered visualizations. Leveraging this data, we develop two instruction-tuned models, ChartPoint_Q2 and ChartPoint_Q2.5, which outperform state-of-the-art across several chart benchmarks, e.g., +5.04% on ChartBench.
StealthAttack: Robust 3D Gaussian Splatting Poisoning via Density-Guided Illusions
Authors: Bo-Hsu Ke; You-Zhe Xie; Yu-Lun Liu; Wei-Chen Chiu
Venue: ICCV 2025 (main)

Abstract: 3D scene representation methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have significantly advanced novel view synthesis. As these methods become prevalent, addressing their vulnerabilities becomes critical. We analyze 3DGS robustness against image-level poisoning attacks and propose a novel density-guided poisoning method. Our method strategically injects Gaussian points into low-density regions identified via Kernel Density Estimation (KDE), embedding viewpoint-dependent illusory objects clearly visible from poisoned views while minimally affecting innocent views. Additionally, we introduce an adaptive noise strategy to disrupt multi-view consistency, further enhancing attack effectiveness. We propose a KDE-based evaluation protocol to assess attack difficulty systematically, enabling objective benchmarking for future research. Extensive experiments demonstrate our method's superior performance compared to state-of-the-art techniques.
LightCity: An Urban Dataset for Outdoor Inverse Rendering and Reconstruction under Multi-illumination Conditions
Authors: Jingjing Wang; Qirui Hu; Chong Bao; Yuke Zhu; Hujun Bao; Zhaopeng Cui; Guofeng Zhang
Venue: ICCV 2025 (main)

Abstract: We propose an outdoor scene dataset and propose a series of benchmarks based on it.Inverse rendering in urban scenes is pivotal for applications like autonomous driving and digital twins, yet it faces significant challenges due to complex illumination conditions, including multi-illumination and indirect light and shadow effects.However, these challenges' effects on intrinsic decomposition and 3D reconstruction are not explored due to the lack of appropriate datasets. In this paper, we present LightCity, a novel high-quality synthetic urban dataset featuring diverse illumination conditions with realistic indirect light and shadow effects.LightCity encompasses over 300 sky maps with highly controllable illumination, varying scales with both street-level and aerial perspectives over 50K images, and rich properties such as depth, normal, and material components, light and indirect light, etc.Besides, we leverage LightCity to benchmark three fundamental tasks in the urban environments and conduct a comprehensive analysis of these benchmarks, laying a robust foundation for advancing related research.
On the Generalization of Representation Uncertainty in Earth Observation
Authors: Spyros Kondylatos; Nikolaos Ioannis Bountos; Dimitrios Michail; Xiao Xiang Zhu; Gustau Camps-Valls; Ioannis Papoutsis
Venue: ICCV 2025 (main)

Abstract: Recent advances in Computer Vision have introduced the concept of pretrained representation uncertainty, enabling zero-shot uncertainty estimation. This holds significant potential for Earth Observation (EO), where trustworthiness is critical, yet the complexity of EO data poses challenges to uncertainty-aware methods. In this work, we investigate the generalization of representation uncertainty in EO, considering the domain's unique semantic characteristics. We pretrain uncertainties on large EO datasets and propose an evaluation framework to assess their zero-shot performance in multi-label classification and segmentation EO tasks. Our findings reveal that, unlike uncertainties pretrained on natural images, EO-pretraining exhibits strong generalization across unseen EO domains, geographic locations, and target granularities, while maintaining sensitivity to variations in ground sampling distance. We demonstrate the practical utility of pretrained uncertainties showcasing their alignment with task-specific uncertainties in downstream tasks, their sensitivity to real-world EO image noise, and their ability to generate spatial uncertainty estimates out-of-the-box. Initiating the discussion on representation uncertainty in EO, our study provides insights into its strengths and limitations, paving the way for future research in the field. Code and weights are available at: https://github.com/Orion-AI-Lab/EOUncertaintyGeneralization.
Region-aware Anchoring Mechanism for Efficient Referring Visual Grounding
Authors: Shuyi Ouyang; Ziwei Niu; Hongyi Wang; Yen-Wei Chen; Lanfen Lin
Venue: ICCV 2025 (main)

Abstract: Referring Visual Grounding (RVG) tasks revolve around utilizing vision-language interactions to incorporate object information from language expressions, thereby enabling targeted object detection or segmentation within images. Transformer-based methods have enabled effective interaction through attention mechanisms, achieving notable performance in RVG tasks. However, existing strategies for RVG, which involve direct interaction between visual and linguistic features, face three key challenges: (i) tendency to focus on a single target, (ii) insufficient control over linguistic noise, and (iii) high computational cost. To address these challenges, we propose a Region-aware Anchoring Mechanism (RaAM) that mediates vision-language interactions. In RaAM, region-aware anchors engage in alternating interactions with vision and language modalities, acting as indicators for object presence across different regions within the image. RaAM (i) directs attention to multiple target regions for better localization, (ii) reduces cross-modal redundancy by using anchors as buffers, and (iii) lowers time complexity. In addition, we design region and pixel level loss functions to enhance object presence assessment and edge precision. We evaluate our RaAM-RVG on four benchmark datasets and integrate RaAM into various models by replacing their interaction design. Results show that RaAM outperforms state-of-the-art methods with lower computational cost.
Mind the Cost of Scaffold! Benign Clients May Even Become Accomplices of Backdoor Attack
Authors: Xingshuo Han; Xuanye Zhang; Xiang Lan; Haozhao Wang; Shengmin Xu; Shen Ren; Jason Zeng; Ming Wu; Michael Heinrich; Tianwei Zhang
Venue: ICCV 2025 (main)

Abstract: By using a control variate to calibrate the local gradient of each client, Scaffold has been widely known as a powerful solution to mitigate the impact of data heterogeneity in Federated Learning. Although Scaffold achieves significant performance improvements, we show that this superiority is at the cost of increased security vulnerabilities. Specifically, this paper presents BadSFL, the first backdoor attack targeting Scaffold, which turns benign clients into accomplices to amplify the attack effect. The core idea of BadSFL is to uniquely tamper with the control variate to subtly steer benign clients' local gradient updates towards the attacker's poisoned direction, effectively turning them into unwitting accomplices, significantly enhancing the backdoor persistence. Additionally, BadSFL leverages a GAN-enhanced poisoning strategy to enrich the attacker's dataset, maintaining high accuracy on both benign and backdoored samples while remaining stealthy. Extensive experiments demonstrate that BadSFL achieves superior attack durability, maintaining effectiveness for over 60 global rounds--lasting up to three times longer than existing baselines even after ceasing malicious model injections.
MV-Adapter: Multi-View Consistent Image Generation Made Easy
Authors: Zehuan Huang; Yuan-Chen Guo; Haoran Wang; Ran Yi; Lizhuang Ma; Yan-Pei Cao; Lu Sheng
Venue: ICCV 2025 (main)

Abstract: Existing multi-view image generation methods often make invasive modifications to pre-trained text-to-image (T2I) models and require full fine-tuning, leading to high computational costs and degradation in image quality due to scarce high-quality 3D data. This paper introduces MV-Adapter, an efficient and versatile adapter that enhances T2I models and their derivatives without altering the original network structure or feature space. To efficiently model the 3D geometric knowledge within the adapter, we introduce innovative designs that include duplicated self-attention layers and parallel attention architecture, enabling the adapter to inherit the powerful priors of the pre-trained models to model the novel 3D knowledge. Moreover, we present a unified condition encoder that seamlessly integrates camera parameters and geometric information, facilitating applications such as text- and image-based 3D generation and texturing. MV-Adapter achieves multi-view generation at 768 resolution on Stable Diffusion XL (SDXL), and demonstrates adaptability and versatility. It can also be extended to arbitrary view generation, enabling broader applications. We demonstrate that MV-Adapter sets a new quality standard for multi-view image generation, and opens up new possibilities due to its efficiency, adaptability and versatility.