GenAI4ELab/papercli-papers huggingface dataset:
Scaling Transformer-Based Novel View Synthesis with Models Token Disentanglement and Synthetic Data
Authors: Nithin Gopalakrishnan Nair; Srinivas Kaza; Xuan Luo; Vishal M. Patel; Stephen Lombardi; Jungyeon Park
Venue: ICCV 2025 (main)

Abstract: Large transformer-based models have made significant progress in generalizable novel view synthesis (NVS) from sparse input views, generating novel viewpoints without the need for test-time optimization. However, these models are constrained by the limited diversity of publicly available scene datasets, making most real-world (in-the-wild) scenes out-of-distribution. To overcome this, we incorporate synthetic training data generated from diffusion models, which improves generalization across unseen domains. While synthetic data offers scalability, we identify artifacts introduced during data generation as a key bottleneck affecting reconstruction quality. To address this, we propose a token disentanglement process within the transformer architecture, enhancing feature separation and ensuring more effective learning. This refinement not only improves reconstruction quality over standard transformers but also enables scalable training with synthetic data. As a result, our method outperforms existing models on both in-dataset and cross-dataset evaluations, achieving state-of-the-art results across multiple benchmarks while significantly reducing computational costs.
FED-PsyAU: Privacy-Preserving Micro-Expression Recognition via Psychological AU Coordination and Dynamic Facial Motion Modeling
Authors: Jingting Li; Yu Qian; Lin Zhao; Su-Jing Wang
Venue: ICCV 2025 (main)

Abstract: Micro-expressions (MEs) are brief, low-intensity, often localized facial expressions. They could reveal genuine emotions individuals may attempt to conceal, valuable in contexts like criminal interrogation and psychological counseling. However, ME recognition (MER) faces challenges, such as small sample sizes and subtle features, which hinder efficient modeling. Additionally, real-world applications encounter ME data privacy issues, leaving the task of enhancing recognition across settings under privacy constraints largely unexplored. To address these issues, we propose a FED-PsyAU research framework. We begin with a psychological study on the coordination of upper and lower facial action units (AUs) to provide structured prior knowledge of facial muscle dynamics. We then develop a DPK-GAT network that combines these psychological priors with statistical AU patterns, enabling hierarchical learning of facial motion features from regional to global levels, effectively enhancing MER performance. Additionally, our federated learning framework advances MER capabilities across multiple clients without data sharing, preserving privacy and alleviating the limited-sample issue for each client. Extensive experiments on commonly-used ME databases demonstrate the effectiveness of our approach. Our implementation is publicly available at https://github.com/MELABIPCAS/FED-PsyAU.git.
SAFER: Sharpness Aware layer-selective Finetuning for Enhanced Robustness in vision transformers
Authors: Bhavna Gopal; Huanrui Yang; Mark Horton; Yiran Chen
Venue: ICCV 2025 (main)

Abstract: Vision transformers (ViTs) have become essential backbones in advanced computer vision applications and multi-modal foundation models. Despite their strengths, ViTs remain vulnerable to adversarial perturbations, comparable to or even exceeding the vulnerability of convolutional neural networks (CNNs). Furthermore, the large parameter count and complex architecture of ViTs make them particularly prone to adversarial overfitting, often compromising both clean and adversarial accuracy. This paper mitigates adversarial overfitting in ViTs through a novel, layer-selective fine-tuning approach: SAFER. Instead of optimizing the entire model, we identify and selectively fine-tune a small subset of layers most susceptible to overfitting, applying sharpness-aware minimization to these layers while freezing the rest of the model. Our method consistently enhances both clean and adversarial accuracy over baseline approaches. Typical improvements are around 5%, with some cases achieving gains as high as 20% across various ViT architectures and datasets.
ClearSight: Human Vision-Inspired Solutions for Event-Based Motion Deblurring
Authors: Xiaopeng Lin; Yulong Huang; Hongwei Ren; Zunchang Liu; Hongxiang Huang; Yue Zhou; Haotian Fu; Bojun Cheng
Venue: ICCV 2025 (main)

Abstract: Motion deblurring addresses the challenge of image blur caused by camera or scene movement. Event cameras provide motion information that is encoded in the asynchronous event streams. To efficiently leverage the temporal information of event streams, we employ Spiking Neural Networks (SNNs) for motion feature extraction and Artificial Neural Networks (ANNs) for color information processing. Due to the non-uniform distribution and inherent redundancy of event data, existing cross-modal feature fusion methods exhibit certain limitations. Inspired by the visual attention mechanism in the human visual system, this study introduces a bioinspired dual-drive hybrid network (BDHNet). Specifically, the Neuron Configurator Module (NCM) is designed to dynamically adjust neuron configurations based on cross-modal features, thereby focusing the spikes in blurry regions and adapting to varying blurry scenarios dynamically. Additionally, the Region of Blurry Attention Module (RBAM) is introduced to generate a blurry mask in an unsupervised manner, effectively extracting motion clues from the event features and guiding more accurate cross-modal feature fusion. Extensive subjective and objective evaluations demonstrate that our method outperforms current state-of-the-art methods on both synthetic and real-world datasets.
Event-aided Dense and Continuous Point Tracking: Everywhere and Anytime
Authors: Zhexiong Wan; Jianqin Luo; Yuchao Dai; Gim Hee Lee
Venue: ICCV 2025 (main)

Abstract: Recent point tracking methods have made great strides in recovering the trajectories of any point (especially key points) in long video sequences associated with large motions. However, the spatial and temporal granularities of point trajectories remain constrained by limited motion estimation accuracy and video frame rate. Leveraging the high temporal resolution and motion sensitivity of event cameras, we introduce event data for the first time to recover spatially dense and temporally continuous trajectories of every point at any time. Specifically, we define the dense and continuous point trajectory representation as estimating multiple control points of curves for each pixel and model the movement of sparse events triggered along continuous point trajectories. Building on this, we propose a novel multi-frame iterative streaming framework that first estimates local inter-frame motion representations from two consecutive frames with inter-frame events, then aggregates them into a global long-term motion representation to utilize input full video and event data with an arbitrary number of frames. Extensive experiments on simulated and real data demonstrate the significant improvement of our framework over state-of-the-art methods and the crucial role of introducing events to model continuous point trajectories.
Stable-Sim2Real: Exploring Simulation of Real-Captured 3D Data with Two-Stage Depth Diffusion
Authors: Mutian Xu; Chongjie Ye; Haolin Liu; Yushuang Wu; Jiahao Chang; Xiaoguang Han
Venue: ICCV 2025 (main)

Abstract: 3D data simulation aims to bridge the gap between simulated and real-captured 3D data, which is a fundamental problem for real-world 3D visual tasks. Most 3D data simulation methods inject predefined physical priors but struggle to capture the full complexity of real data. An optimal approach involves learning an implicit mapping from synthetic to realistic data in a data-driven manner, but progress in this solution has met stagnation in recent studies. This work explores a new solution path of data-driven 3D simulation, called Stable-Sim2Real, based on a novel two-stage depth diffusion model. The initial stage finetunes StableDiffusion to generate the residual between the real and synthetic paired depth, producing a stable but coarse depth, where some local regions may deviate from realistic patterns. To enhance this, both the synthetic and initial output depth are fed into a second-stage diffusion, where diffusion loss is adjusted to prioritize these distinct areas identified by a 3D discriminator. We provide a new benchmark scheme to evaluate 3D data simulation methods. Extensive experiments show that training the network with the 3D simulated data derived from our method significantly enhances performance in real-world 3D visual tasks. Moreover, the evaluation demonstrates the high similarity between our 3D simulated data and real-captured patterns.
Towards Real Unsupervised Anomaly Detection Via Confident Meta-Learning
Authors: Muhammad Aqeel; Shakiba Sharifi; Marco Cristani; Francesco Setti
Venue: ICCV 2025 (main)

Abstract: So-called unsupervised anomaly detection is better described as semi-supervised, as it assumes all training data are nominal. This assumption simplifies training but requires manual data curation, introducing bias and limiting adaptability. We propose Confident Meta-learning (CoMet), a novel training strategy that enables deep anomaly detection models to learn from uncurated datasets where nominal and anomalous samples coexist, eliminating the need for explicit filtering. Our approach integrates Soft Confident Learning, which assigns lower weights to low-confidence samples, and Meta-Learning, which stabilizes training by regularizing updates based on training-validation loss covariance. This prevents overfitting and enhances robustness to noisy data. CoMet is model-agnostic and can be applied to any anomaly detection method trainable via gradient descent. Experiments on MVTec-AD, VIADUCT, and KSDD2 with two state-of-the-art models demonstrate the effectiveness of our approach, consistently improving over the baseline methods, remaining insensitive to anomalies in the training set, and setting a new state-of-the-art across all datasets.
Multi-Schema Proximity Network for Composed Image Retrieval
Authors: Jiangming Shi; Xiangbo Yin; Yeyun Chen; Yachao Zhang; Zhizhong Zhang; Yuan Xie; Yanyun Qu
Venue: ICCV 2025 (main)

Abstract: Composed Image Retrieval (CIR) aims to retrieve a target image using a query that combines a reference image and a textual description, benefiting users to express their intent more effectively. Despite significant advances in CIR methods, two unresolved problems remain: 1) existing methods overlook multi-schema interaction due to the lack of fine-grained explicit visual supervision, which hinders the capture of complex correspondences, and 2) existing methods overlook noisy negative pairs formed by potential corresponding query-target pairs, which increases confusion. To address these problems, we propose a Multi-schemA Proximity Network (MAPNet) for CIR, consisting of two key components: Multi-Schema Interaction (MSI) and Relaxed Proximity Loss (RPLoss). Specifically, MSI leverages textual descriptions as an implicit guide to establish correspondences between multiple objects and attributes in the reference and target images, enabling multi-schema interactions. Then, RPLoss further aligns the query and target features while avoiding the poison of noisy negative pairs by denoising and reweighting strategy. Comprehensive experiments conducted on CIRR, FashionIQ, and LaSCo demonstrate that MAPNet achieves competitive results against state-of-the-art CIR methods.
Unified Video Generation via Next-Set Prediction in Continuous Domain
Authors: Zhanzhou Feng; Qingpei Guo; Xinyu Xiao; Ruihan Xu; Ming Yang; Shiliang Zhang
Venue: ICCV 2025 (main)

Abstract: Existing video generation strategies can be categorized into two categories, i.e., the diffusion and autoregressive (AR) methods. While AR methods achieves high efficiency by predicting the next token based on known visual cues, they generally fall short of diffusion models in terms of video quality. To bridge this gap, this paper introduces a novel continuous-domain next-set prediction strategy.Our approach groups related tokens to be generated into one single set, and simultaneously predicts their probability distributions, thereby better exploiting their spatial and temporal dependencies. Specifically, we propose two token partitioning strategies: Spatial Progressive Partitioning for image tokens and Temporal Next-Frame Partitioning for video tokens. Additionally, we construct a denoising sampler to generate outputs from the token set distribution within a continuous domain. This method unifies image and video generation under a cohesive next-set prediction framework.Experimental results indicate that our method achieves video quality comparable to recent diffusion models, while significantly reducing inference costs. Notably, our method surpasses the recent next token prediction approach Emu3, in video quality despite using approximately 90% fewer parameters. Visualizations further confirm the effectiveness of our method in capturing intricate details and movements, such as water droplets and facial expressions.All implementations will be released.
Latent-Reframe: Enabling Camera Control for Video Diffusion Models without Training
Authors: Zhenghong Zhou; Jie An; Jiebo Luo
Venue: ICCV 2025 (main)

Abstract: Precise camera pose control is crucial for video generation with diffusion models. Existing methods require fine-tuning with additional datasets containing paired videos and camera pose annotations, which are both data-intensive and computationally costly, and may disrupt the model's distribution learned from the training data. We introduce Latent-Reframe, which enables camera control in a pre-trained video diffusion model without fine-tuning. Unlike existing methods, Latent-Reframe operates during the sampling stage, maintaining efficiency while preserving the distribution learned during pretraining. Our approach reframes the latent code of video frames to align with the input camera trajectory through time-aware point clouds. Latent code inpainting and harmonization then refine the model's latent space, ensuring high-quality video generation. Latent-Reframe can be applied to both DiT- and UNet-based video diffusion models. Experimental results demonstrate that Latent-Reframe can achieve comparable or superior camera control precision and video quality to training-based methods, without the need for fine-tuning on additional datasets.
EditCLIP: Representation Learning for Image Editing
Authors: Qian Wang; Aleksandar Cvejić; Abdelrahman Eldesokey; Peter Wonka
Venue: ICCV 2025 (main)

Abstract: We introduce EditCLIP, a novel representation-learning approach for image editing. Our method learns a unified representation of edits by jointly encoding an input image and its edited counterpart, effectively capturing their transformation. To evaluate its effectiveness, we employ EditCLIP to solve two tasks: exemplar-based image editing and automated edit evaluation. In exemplar-based image editing, we replace text-based instructions in InstructPix2Pix with EditCLIP embeddings computed from a reference exemplar image pair. Experiments demonstrate that our approach outperforms state-of-the-art methods while being more efficient and versatile. For automated evaluation, EditCLIP assesses image edits by measuring the similarity between the EditCLIP embedding of a given image pair and either a textual editing instruction or the EditCLIP embedding of another reference image pair. Experiments show that EditCLIP aligns more closely with human judgments than existing CLIP-based metrics, providing a reliable measure of edit quality and structural preservation.
Efficient Event Camera Data Pretraining with Adaptive Prompt Fusion
Authors: Quanmin Liang; Qiang Li; Shuai Liu; Xinzi Cao; Jinyi Lu; Feidiao Yang; Wei Zhang; Kai Huang; Yonghong Tian
Venue: ICCV 2025 (main)

Abstract: Applying pretraining-finetuning paradigm to event cameras presents significant challenges due to the scarcity of large-scale event datasets and the inherently sparse nature of event data, which increases the risk of overfitting during extensive pretraining.In this paper, we explore the transfer of pretrained image knowledge to the domain of event cameras to address this challenge. The key to our approach lies in adapting event data representations to align with image pretrained models while simultaneously integrating spatiotemporal information and mitigating data sparsity. To achieve this, we propose a lightweight SpatioTemporal information fusion Prompting (STP) method, which progressively fuses the spatiotemporal characteristics of event data through a dynamic perception module with multi-scale spatiotemporal receptive fields, enabling compatibility with image pretrained models.STP enhances event data representation by capturing local information within a large receptive field and performing global information exchange along the temporal dimension. This strategy effectively reduces sparse regions in event data while refining fine-grained details, all while preserving its inherent spatiotemporal structure. Our method significantly outperforms previous state-of-the-art approaches across classification, semantic segmentation, and optical flow estimation tasks. For instance, it achieves a top-1 accuracy of 68.87% (+4.04%) on N-ImageNet with only 1/10 of the pretraining parameters and 1/3 of the training epochs.
Tiling artifacts and trade-offs of feature normalization in the segmentation of large biological images
Authors: Elena Buglakova; Anwai Archit; Edoardo D'Imprima; Julia Mahamid; Constantin Pape; Anna Kreshuk
Venue: ICCV 2025 (main)

Abstract: Segmentation of very large images is a common problem in microscopy, medical imaging or remote sensing. The problem is usually addressed by sliding window inference, which can theoretically lead to seamlessly stitched predictions. However, in practice many of the popular pipelines still suffer from tiling artifacts. We investigate the root cause of these issues and show that they stem from the normalization layers within the neural networks. We propose indicators to detect normalization issues and further explore the trade-offs between artifact-free and high-quality predictions, using three diverse microscopy datasets as examples. Finally, we propose to use BatchRenorm as the most suitable normalization strategy, which effectively removes tiling artifacts and enhances transfer performance, thereby improving the reusability of trained networks for new datasets. The code is available at https://github.com/kreshuklab/no_tiling_artifacts.
Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning
Authors: Yiyang Chen; Shanshan Zhao; Lunhao Duan; Changxing Ding; Dacheng Tao
Venue: ICCV 2025 (main)

Abstract: Diffusion-based models, widely used in text-to-image generation, have proven effective in 2D representation learning. Recently, this framework has been extended to 3D self-supervised learning by constructing a conditional point generator for enhancing 3D representations. However, its performance remains constrained by the 3D diffusion model, which is trained on the available 3D datasets with limited size. We hypothesize that the robust capabilities of text-to-image diffusion models, particularly Stable Diffusion (SD), which is trained on large-scale datasets, can help overcome these limitations. To investigate this hypothesis, we propose PointSD, a framework that leverages the SD model for 3D self-supervised learning. By replacing the SD model's text encoder with a 3D encoder, we train a point-to-image diffusion model that allows point clouds to guide the denoising of rendered noisy images. With the trained point-to-image diffusion model, we use noise-free images as the input and point clouds as the condition to extract SD features. Next, we train a 3D backbone by aligning its features with these SD features, thereby facilitating direct semantic learning. Comprehensive experiments on downstream point cloud tasks and ablation studies demonstrate that the SD model can enhance point cloud self-supervised learning. Code is publicly available at https://github.com/wdttt/PointSD.
Bridging the Gap between Brain and Machine in Interpreting Visual Semantics: Towards Self-adaptive Brain-to-Text Decoding
Authors: Jiaxuan Chen; Yu Qi; Yueming Wang; Gang Pan
Venue: ICCV 2025 (main)

Abstract: Neural decoding has recently made significant progress in reconstructing images and text from brain activity, yet seeking biologically valid semantic alignment between artificial models and the brain remains challenging. Large pre-trained foundation models such as CLIP excel at capturing rich semantic details in complex visual scenes. In contrast, due to selective attention, only part of the visual semantics in the stimulus may be preferentially represented in the neural patterns when subjects view images. Past studies have generally assumed that stimulus images and their evoked brain recordings are strictly semantically equivalent, potentially leading to semantic misalignment between supervision signals and neural recordings. In order to address this, we propose a novel self-adaptive semantic decoding method (Mind-SA), designed to dynamically detect the regions within stimulus images that the brain actually focuses on and use them as supervision to guide brain-to-text reconstruction. We find that the proposed Mind-SA can be used to reduce the semantic gap between supervision signals (i.e., stimulus images) and neural representations, thus enabling the reconstruction model to focus on the parts that the brain actually perceives. Experiments demonstrate that Mind-SA improves the quality of neural representations and achieves the state-of-the-art brain-to-text performance. Our code is available at https://github.com/JxuanC/MindSA.
Doppler-Aware LiDAR-RADAR Fusion for Weather-Robust 3D Detection
Authors: Yujeong Chae; Heejun Park; Hyeonseong Kim; Kuk-Jin Yoon
Venue: ICCV 2025 (main)

Abstract: Robust 3D object detection across diverse weather con- ditions is crucial for safe autonomous driving, and RADAR is increasingly leveraged for its resilience in adverse weather. Recent advancements have explored 4D RADAR and LiDAR-RADAR fusion to enhance 3D perception capabilities, specifically targeting weather robustness. However, existing methods often handle Doppler in ways that are not well-suited for multi-modal settings or lack tailored encoding strategies, hindering effective feature fusion and performance. To address these shortcomings, we propose a novel Doppler-aware LiDAR-4D RADAR fusion (DLR-Fusion) framework for robust 3D object detection. We introduce a multi-path iterative interaction module that integrates LiDAR, RADAR power, and Doppler, enabling a structured feature fusion process. Doppler highlights dynamic regions, refining RADAR power and enhancing LiDAR features across multiple stages, improving detection confidence. Extensive experiments on the K-RADAR dataset demonstrate that our approach effectively exploits Doppler information, achieving state-of-the-art performance in both normal and adverse weather conditions.