GenAI4ELab/papercli-papers huggingface dataset:
UIPro: Unleashing Superior Interaction Capability For GUI Agents
Authors: Hongxin Li; Jingran Su; Jingfan Chen; Zheng Ju; Yuntao Chen; Qing Li; Zhaoxiang Zhang
Venue: ICCV 2025 (main)

Abstract: Building autonomous agents that perceive and operate graphical user interfaces (GUIs) like humans has long been a vision in the field of artificial intelligence. Central to these agents is the capability for GUI interaction, which involves GUI understanding and planning capabilities. Existing methods have tried developing GUI agents based on the multi-modal comprehension ability of vision-language models (VLMs). However, the limited scenario, insufficient size, and heterogeneous action spaces hinder the progress of building generalist GUI agents. To resolve these issues, this paper proposes UIPro, a novel generalist GUI agent trained with extensive multi-platform and multi-task GUI interaction data, coupled with a unified action space. We first curate a comprehensive dataset encompassing 20.6 million GUI understanding tasks to pre-train UIPro, granting it a strong GUI grounding capability, which is key to downstream GUI agent tasks. Subsequently, we establish a unified action space to harmonize heterogeneous GUI agent task datasets and produce a merged dataset to foster the action prediction ability of UIPro via continued fine-tuning. Experimental results demonstrate UIPro's superior performance across multiple GUI task benchmarks on various platforms, highlighting the effectiveness of our approach.
Class-Wise Federated Averaging for Efficient Personalization
Authors: Gyuejeong Lee; Daeyoung Choi
Venue: ICCV 2025 (main)

Abstract: Federated learning (FL) enables collaborative model training across distributed clients without centralizing data. However, existing approaches like Federated Averaging (FedAvg) often perform poorly with heterogeneous data distributions, failing to achieve personalization owing to their inability to capture class-specific information effectively. We propose Class-wise Federated Averaging (cwFedAvg), a novel personalized FL (PFL) framework that performs Federated Averaging for each class, to overcome the personalization limitations of FedAvg. cwFedAvg creates class-specific global models via weighted aggregation of local models using class distributions, and subsequently combines them to generate personalized local models. We further propose Weight Distribution Regularizer (WDR), which encourages deep networks to encode class-specific information efficiently by aligning empirical and approximated class distributions derived from output layer weights, to facilitate effective class-wise aggregation. Our experiments demonstrate the superior performance of cwFedAvg over existing PFL methods through efficient personalization while maintaining the communication cost of FedAvg and avoiding additional local training and pairwise computations.
StochasticSplats: Stochastic Rasterization for Sorting-Free 3D Gaussian Splatting
Authors: Shakiba Kheradmand; Delio Vicini; George Kopanas; Dmitry Lagun; Kwang Moo Yi; Mark Matthews; Andrea Tagliasacchi
Venue: ICCV 2025 (main)

Abstract: 3D Gaussian splatting (3DGS) is a popular radiance field method, with many application-specific extensions. Most variants rely on the same core algorithm: depth-sorting of Gaussian splats then rasterizing in primitive order. This ensures correct alpha compositing, but can cause rendering artifacts due to built-in approximations. Moreover, for a fixed representation, sorted rendering offers little control over render cost and visual fidelity. For example, and counter-intuitively, rendering a lower-resolution image is not necessarily faster. In this work, we address the above limitations by combining 3D Gaussian splatting with stochastic rasterization. Concretely, we leverage an unbiased Monte Carlo estimator of the volume rendering equation. This removes the need for sorting, and allows for accurate 3D blending of overlapping Gaussians. The number of Monte Carlo samples further imbues 3DGS with a way to trade off computation time and quality. We implement our method using OpenGL shaders, enabling efficient rendering on modern GPU hardware. At a reasonable visual quality, our method renders more than four times faster than sorted rasterization.
A0: An Affordance-Aware Hierarchical Model for General Robotic Manipulation
Authors: Rongtao Xu; Jian Zhang; Minghao Guo; Youpeng Wen; Haoting Yang; Min Lin; Jianzheng Huang; Zhe Li; Kaidong Zhang; Liqiong Wang; Yuxuan Kuang; Meng Cao; Feng Zheng; Xiaodan Liang
Venue: ICCV 2025 (main)

Abstract: Robotic manipulation faces critical challenges in understanding spatial affordances--the "where" and "how" of object interactions--essential for complex manipulation tasks like wiping a board or stacking objects. Existing methods, including modular-based and end-to-end approaches, often lack robust spatial reasoning capabilities. Unlike recent point-based and flow-based affordance methods that focus on dense spatial representations or trajectory modeling, we propose A0, a hierarchical affordance-aware diffusion model that decomposes manipulation task into high-level spatial affordance understanding and low-level action execution. A0 leverages the Embodiment-Agnostic Affordance Representation, which captures object-centric spatial affordances by predicting contact point and post-contact trajectories. A0 is pre-trained on 1 million contact points data and fine-tuned on annotated trajectories, enabling generalization across platforms. Key components include Position Offset Attention for motion-aware feature extraction and a Spatial Information Aggregation Layer for precise coordinate mapping. The model's output is executed by the action execution module. Experiments on multiple robotic systems (Franka, Kinova, Realman and Dobot) demonstrate A0's superior performance in complex tasks, showcasing its efficiency, flexibility, and real-world applicability.
FinMMR: Make Financial Numerical Reasoning More Multimodal, Comprehensive, and Challenging
Authors: Zichen Tang; Haihong E; Jiacheng Liu; Zhongjun Yang; Rongjin Li; Zihua Rong; Haoyang He; Zhuodi Hao; Xinyang Hu; Kun Ji; Ziyan Ma; Mengyuan Ji; Jun Zhang; Chenghao Ma; Qianhe Zheng; Yang Liu; Yiling Huang; Xinyi Hu; Qing Huang; Zijian Xie; Shiyao Peng
Venue: ICCV 2025 (main)

Abstract: We present FinMMR, a novel bilingual multimodal benchmark tailored to evaluate the reasoning capabilities of multimodal large language models (MLLMs) in financial numerical reasoning tasks. Compared to existing benchmarks, our work introduces three significant advancements. (1) Multimodality: We meticulously transform existing financial reasoning benchmarks, and construct novel questions from the latest Chinese financial research reports. FinMMR comprises 4.3K questions and 8.7K images spanning 14 categories, including tables, bar charts, and ownership structure charts. (2) Comprehensiveness: FinMMR encompasses 14 financial subdomains, including corporate finance, banking, and industry analysis, significantly exceeding existing benchmarks in financial domain knowledge breadth. (3) Challenge: Models are required to perform multi-step precise numerical reasoning by integrating financial knowledge with the understanding of complex financial images and text. The best-performing MLLM achieves only 51.4% accuracy on Hard problems. We believe that FinMMR will drive advancements in enhancing the reasoning capabilities of MLLMs in real-world scenarios.
A Tiny Change, A Giant Leap: Long-Tailed Class-Incremental Learning via Geometric Prototype Alignment
Authors: Xinyi Lai; Luojun Lin; Weijie Chen; Yuanlong Yu
Venue: ICCV 2025 (main)

Abstract: Long-Tailed Class-Incremental Learning (LT-CIL) remains a fundamental challenge due to biased gradient updates caused by highly imbalanced data distributions and the inherent stability-plasticity dilemma. These factors jointly degrade tail-class performance and exacerbate catastrophic forgetting. To tackle these issues, we propose Geometric Prototype Alignment (GPA), a model-agnostic approach that calibrates classifier learning dynamics via geometric feature-space alignment. GPA initializes classifier weights by projecting frozen class prototypes onto a unit hypersphere, thereby disentangling magnitude imbalance from angular discriminability. During incremental updates, a Dynamic Anchoring mechanism adaptively adjusts classifier weights to preserve geometric consistency, effectively balancing plasticity for new classes with stability for previously acquired knowledge. Integrated into state-of-the-art CIL frameworks such as LUCIR and DualPrompt, GPA yields substantial gains, improving average incremental accuracy by 6.11% and reducing forgetting rates by 6.38% on CIFAR100-LT. Theoretical analysis further demonstrates that GPA accelerates convergence by 2.7X and produces decision boundaries approaching Fisher-optimality. Our implementation is available at https://github.com/laixinyi023/Geometric-Prototype-Alignment.
X-Prompt: Generalizable Auto-Regressive Visual Learning with In-Context Prompting
Authors: Zeyi Sun; Ziyang Chu; Pan Zhang; Tong Wu; Yuhang Zang; Xiaoyi Dong; Yuanjun Xiong; Dahua Lin; Jiaqi Wang
Venue: ICCV 2025 (main)

Abstract: Recent advances in large language models have enabled task prompting for open-ended text generation. In the vision domain, a longstanding goal is developing models capable of general visual learning, encompassing tasks such as image generation, editing, low-level processing, and dense perception. Although recent efforts have aimed at building vision foundation models that support prompting, significant challenges remain, particularly in accurately comprehending visual prompts and addressing the ambiguity inherent in textual prompts. To address this, we introduce X-Prompt, a purely auto-regressive large vision-language model designed for generalizable visual learning via in-context prompting. X-Prompt can process visual and textual prompts as context, enabling precise task interpretation and accurate execution. A novel prompt-token fusion mechanism effectively extracts relevant task information from complex prompts while significantly reducing the token length. Additionally, a unified training strategy for text and image prediction enhances task awareness, enabling seamless adaptation to open-ended prompts. Extensive experiments demonstrate that X-Prompt effectively interprets in-context prompts and exhibits generalization across both in-domain and out-of-domain visual tasks, paving the way for future advancements in general visual learning.
HORT: Monocular Hand-held Objects Reconstruction with Transformers
Authors: Zerui Chen; Rolandos Alexandros Potamias; Shizhe Chen; Cordelia Schmid
Venue: ICCV 2025 (main)

Abstract: Reconstructing hand-held objects in 3D from monocular images remains a significant challenge in computer vision. Most existing approaches rely on implicit 3D representations, which produce overly smooth reconstructions and are time-consuming to generate explicit 3D shapes. While more recent methods directly reconstruct point clouds with diffusion models, the multi-step denoising makes high-resolution reconstruction inefficient. To address these limitations, we propose a transformer-based model to efficiently reconstruct dense 3D point clouds of hand-held objects. Our method follows a coarse-to-fine strategy, first generating a sparse point cloud from the image and progressively refining it into a dense representation using pixel-aligned image features. To enhance reconstruction accuracy, we integrate image features with 3D hand geometry to jointly predict the object point cloud and its pose relative to the hand. Our model is trained end-to-end for optimal performance. Experimental results on both synthetic and real datasets demonstrate that our method achieves state-of-the-art accuracy with much faster inference speed, while generalizing well to in-the-wild images.
Early Timestep Zero-Shot Candidate Selection for Instruction-Guided Image Editing
Authors: Joowon Kim; Ziseok Lee; Donghyeon Cho; Sanghyun Jo; Yeonsung Jung; Kyungsu Kim; Eunho Yang
Venue: ICCV 2025 (main)

Abstract: Despite recent advances in diffusion models, achieving reliable image generation and editing results remains challenging due to the inherent diversity induced by stochastic noise in the sampling process. Particularly, instruction-guided image editing with diffusion models offers user-friendly editing capabilities, yet editing failures, such as background distortion, frequently occur across different attempts. Users often resort to trial and error, adjusting seeds or prompts to achieve satisfactory results, which is inefficient.While seed selection methods exist for Text-to-Image (T2I) generation, they depend on external verifiers, limiting their applicability, and evaluating multiple seeds increases computational complexity, reducing practicality.To address this, we first establish a new multiple-seed-based image editing baseline using background consistency scores, achieving Best-of-N performance without supervision. Building on this, we introduce ELECT (Early-timestep Latent Evaluation for Candidate Selection), a zero-shot framework that selects reliable seeds by estimating background mismatches at early diffusion timesteps, identfying the seed that retains the background while modifying only the foreground. ELECT ranks seed candidates by a background inconsistency score, filtering unsuitable samples early based on background consistency while fully preserving editability.Beyond standalone seed selection, ELECT integrates into instruction-guided editing pipelines and extends to Multimodal Large-Language Models (MLLMs) for joint seed + prompt selection, further improving results when seed selection alone is insufficient. Experiments show that ELECT reduces computational costs (by 41% on average and up to 61%) while improving background consistency and instruction adherence, achieving around 40% success rates in previously failed cases--without any external supervision or training.
Identity Preserving 3D Head Stylization with Multiview Score Distillation
Authors: Bahri Batuhan Bilecen; Ahmet Berke Gökmen; Furkan Guzelant; Aysegul Dundar
Venue: ICCV 2025 (main)

Abstract: 3D head stylization transforms realistic facial features into artistic representations, enhancing user engagement across applications such as gaming and virtual reality. While 3D-aware generators have made significant advancements, many 3D stylization methods primarily provide near-frontal views and struggle to preserve the unique identities of original subjects, often resulting in outputs that lack diversity and individuality. Leveraging the PanoHead model which provides 360-degree consistent renders, we propose a novel framework that employs negative log-likelihood distillation (LD) to enhance identity preservation and improve stylization quality. By integrating multi-view grid score and mirror gradients within the 3D GAN architecture and introducing a score rank weighing technique, our approach achieves substantial qualitative and quantitative improvements. Our findings not only advance the state of 3D head stylization but also provide valuable insights into effective distillation processes between diffusion models and GANs, focusing on the critical issue of identity preservation.
Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching
Authors: Yang Liu; Wentao Feng; Zhuoyao Liu; Shudong Huang; Jiancheng Lv
Venue: ICCV 2025 (main)

Abstract: Enabling Visual Semantic Models to effectively handle multi-view description matching has been a longstanding challenge. Existing methods typically learn a set of embeddings to find the optimal match for each view's text and compute similarity. However, the visual and text embeddings learned through these approaches have limited information capacity and are prone to interference from locally similar negative samples.To address this issue, we argue that the information capacity of embeddings is crucial and propose Dense-to-Sparse Feature Distilled Visual Semantic Embedding (D2S-VSE), which enhances the information capacity of sparse text by leveraging dense text distillation.Specifically, D2S-VSE is a two-stage framework. In the pre-training stage, we align images with dense text to enhance the information capacity of visual semantic embeddings.In the fine-tuning stage, we optimize two tasks simultaneously, distilling dense text embeddings to sparse text embeddings while aligning images and sparse texts, enhancing the information capacity of sparse text embeddings.Our proposed D2S-VSE model is extensively evaluated on the large-scale MS-COCO and Flickr30K datasets, demonstrating its superiority over recent state-of-the-art methods.
A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision
Authors: Chensheng Peng; Ido Sobol; Masayoshi Tomizuka; Kurt Keutzer; Chenfeng Xu; Or Litany
Venue: ICCV 2025 (main)

Abstract: We present a novel framework for training 3D image-conditioned diffusion models using only 2D supervision. Recovering 3D structure from 2D images is inherently ill-posed due to the ambiguity of possible reconstructions, making generative models a natural choice. However, most existing 3D generative models rely on full 3D supervision, which is impractical due to the scarcity of large-scale 3D datasets. To address this, we propose leveraging sparse-view supervision as a scalable alternative. While recent reconstruction models use sparse-view supervision with differentiable rendering to lift 2D images to 3D, they are predominantly deterministic, failing to capture the diverse set of plausible solutions and producing blurry predictions in uncertain regions. A key challenge in training 3D diffusion models with 2D supervision is that the standard training paradigm requires both the denoising process and supervision to be in the same modality. We address this by decoupling the noisy samples being denoised from the supervision signal, allowing the former to remain in 3D while the latter is provided in 2D. Our approach leverages suboptimal predictions from a deterministic image-to-3D model--acting as a "teacher"--to generate noisy 3D inputs, enabling effective 3D diffusion training without requiring full 3D ground truth. We validate our framework on both object-level and scene-level datasets, using two different 3D Gaussian Splat (3DGS) teachers. Our results show that our approach consistently improves upon these deterministic teachers, demonstrating its effectiveness in scalable and high-fidelity 3D generative modeling.
Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training
Authors: Weiwei Cao; Jianpeng Zhang; Zhongyi Shui; Sinuo Wang; Zeli Chen; Xi Li; Le Lu; Xianghua Ye; Qi Zhang; Tingbo Liang; Ling Zhang
Venue: ICCV 2025 (main)

Abstract: Vision-language pre-training (VLP) has great potential for developing multifunctional and general medical diagnostic capabilities. However, aligning medical images with a low signal-to-noise ratio (SNR) to reports with a high SNR presents a semantic density gap, leading to visual alignment bias. In this paper, we propose boosting vision semantic density to improve alignment effectiveness. On the one hand, we enhance visual semantics through disease-level vision contrastive learning, which strengthens the model's ability to differentiate between normal and abnormal samples for each anatomical structure. On the other hand, we introduce an anatomical normality modeling method to model the distribution of normal samples for each anatomy, leveraging VQ-VAE for reconstructing normal vision embeddings in latent space. This process amplifies abnormal signals by leveraging distribution shifts in abnormal samples, enhancing the model's perception and discrimination of abnormal attributes. The enhanced visual representation effectively captures the diagnostic-relevant semantics, facilitating more efficient and accurate alignment with the diagnostic report. We conduct extensive experiments on two chest CT datasets, CT-RATE and Rad-ChestCT, and an abdominal CT dataset, MedVL-69K, and comprehensively evaluate the diagnosis performance across multiple tasks in the chest and abdominal CT scenarios, achieving state-of-the-art zero-shot performance. Notably, our method achieved an average AUC of 84.9% across 54 diseases in 15 organs, significantly surpassing existing methods. Additionally, we demonstrate the superior transfer learning capabilities of our pre-trained model.
GAP: Gaussianize Any Point Clouds with Text Guidance
Authors: Weiqi Zhang; Junsheng Zhou; Haotian Geng; Wenyuan Zhang; Yu-Shen Liu
Venue: ICCV 2025 (main)

Abstract: 3D Gaussian Splatting (3DGS) has demonstrated its advantages in achieving fast and high-quality rendering. As point clouds serve as a widely-used and easily accessible form of 3D representation, bridging the gap between point clouds and Gaussians becomes increasingly important. Recent studies have explored how to convert the colored points into Gaussians, but directly generating Gaussians from colorless 3D point clouds remains an unsolved challenge. In this paper, we propose GAP, a novel approach that gaussianizes raw point clouds into high-fidelity 3D Gaussians with text guidance. Our key idea is to design a multi-view optimization framework that leverages a depth-aware image diffusion model to synthesize consistent appearances across different viewpoints. To ensure geometric accuracy, we introduce a surface-anchoring mechanism that effectively constrains Gaussians to lie on the surfaces of 3D shapes during optimization. Furthermore, GAP incorporates a diffuse-based inpainting strategy that specifically targets at completing hard-to-observe regions. We evaluate GAP on the Point-to-Gaussian generation task across varying complexity levels, from synthetic point clouds to challenging real-world scans, and even large-scale scenes. Project Page: https://weiqi-zhang.github.io/GAP.
Flow4Agent: Long-form Video Understanding via Motion Prior from Optical Flow
Authors: Ruyang Liu; Shangkun Sun; Haoran Tang; Wei Gao; Ge Li
Venue: ICCV 2025 (main)

Abstract: Long-form video understanding has always been a challenging problem due to the significant redundancy in both temporal and spatial contents. This challenge is further exacerbated by the limited context length of Multimodal Large Language Models (MLLMs). To address this issue, many previous works have attempted to extract key video information, where the "key" is typically semantic-aware and heavily dependent on the CLIP model as prior. In this paper, we propose Flow4Agent, a novel framework that pioneeringly incorporates motion priors from optical flow to facilitate LLM-based long video understanding. Flow4Agent mitigates the redundancy in long videos at both temporal and spatial levels through two core modules: Temporal Granularity Optimization (TGO) adaptively refines frame-level hierarchies, which first leverages coarse flow priors to group similar visual contents and then applies semantic priors to filter out highly irrelevant scene information. Motion Token Pruning (MTP) further refines the intra-frame visual representations, pruning high-redundancy video tokens using fine-grained optical flow information. Extensive experiments demonstrate that our Flow4Agent outperforms existing methods across a wide range of video MLLM benchmarks, especially for hour-level video understanding tasks, achieving 64.7% on Video-MME, 71.4% on MLVU and 60.4% on LongVideoBench.