GenAI4ELab/papercli-papers huggingface dataset:
Progressive Test Time Energy Adaptation for Medical Image Segmentation
Authors: Xiaoran Zhang; Byung-Woo Hong; Hyoungseob Park; Daniel H. Pak; Anne-Marie Rickmann; Lawrence H. Staib; James S. Duncan; Alex Wong
Venue: ICCV 2025 (main)

Abstract: We propose a model-agnostic, progressive test-time energy adaptation approach for medical image segmentation. Maintaining model performance across diverse medical datasets is challenging, as distribution shifts arise from inconsistent imaging protocols and patient variations. Unlike domain adaptation methods that require multiple passes through target data--impractical in clinical settings--our approach adapts pretrained models progressively as they process test data. Our method leverages a shape energy model trained on source data, which assigns an energy score at the patch level to segmentation maps: low energy represents in-distribution (accurate) shapes, while high energy signals out-of-distribution (erroneous) predictions. By minimizing this energy score at test time, we refine the segmentation model to align with the target distribution. To validate effectiveness and adaptability, we evaluated our framework on eight public MRI (bSSFP, T1- and T2-weighted) and X-ray datasets spanning cardiac, spinal cord, and lung segmentation. We consistently outperform baselines both quantitatively and qualitatively. Project page is available at: \href https://voldemort108x.github.io/pttea_seg/  https://voldemort108x.github.io/pttea_seg/
Sequential Gaussian Avatars with Hierarchical Motion Context
Authors: Wangze Xu; Yifan Zhan; Zhihang Zhong; Xiao Sun
Venue: ICCV 2025 (main)

Abstract: The emergence of neural rendering has significantly advanced the rendering quality of 3D human avatars, with the recently popular 3DGS technique enabling real-time performance. However, SMPL-driven 3DGS human avatars still struggle to capture fine appearance details due to the complex mapping from pose to appearance during fitting. In this paper, we propose SeqAvatar, which excavates the explicit 3DGS representation to better model human avatars based on a hierarchical motion context. Specifically, we utilize a coarse-to-fine motion conditions that incorporate both the overall human skeleton and fine-grained vertex motions for non-rigid deformation. To enhance the robustness of the proposed motion conditions, we adopt a spatio-temporal multi-scale sampling strategy to hierarchically integrate more motion clues to model human avatars. Extensive experiments demonstrate that our method significantly outperforms 3DGS-based approaches and renders human avatars orders of magnitude faster than the latest NeRF-based models that incorporate temporal context, all while delivering performance that is at least comparable or even superior. Project page: https://zezeaaa.github.io/projects/SeqAvatar/
Beyond Losses Reweighting: Empowering Multi-Task Learning via the Generalization Perspective
Authors: Hoang Phan; Lam Tran; Quyen Tran; Ngoc Tran; Tuan Truong; Qi Lei; Nhat Ho; Dinh Phung; Trung Le
Venue: ICCV 2025 (main)

Abstract: Multi-task learning (MTL) trains deep neural networks to optimize several objectives simultaneously using a shared backbone, which leads to reduced computational costs, improved data efficiency, and enhanced performance through cross-task knowledge sharing. Although recent gradient manipulation techniques seek a common descent direction to benefit all tasks, conventional empirical loss minimization still leaves models prone to overfitting and gradient conflicts. To address this, we introduce a novel MTL framework that leverages weight perturbation to regulate gradient norms. thus improve generalization. By carefully modulating weight perturbations, our approach harmonizes task-specific gradients, reducing conflicts and encouraging more robust learning across tasks. Theoretical insights reveal that controlling the gradient norm through weight perturbation directly contributes to better generalization. Extensive experiments across diverse applications demonstrate that our method significantly outperforms existing gradient-based MTL techniques in terms of task performance and overall model robustness.
Zero-shot Inexact CAD Model Alignment from a Single Image
Authors: Pattaramanee Arsomngern; Sasikarn Khwanmuang; Matthias Nießner; Supasorn Suwajanakorn
Venue: ICCV 2025 (main)

Abstract: One practical approach to infer 3D scene structure from a single image is to retrieve a closely matching 3D model from a database and align it with the object in the image. Existing methods rely on supervised training with images and pose annotations, which limits them to a narrow set of object categories. To address this, we propose a weakly supervised 9-DoF alignment method for inexact 3D models that requires no scene-level pose annotations and generalizes to unseen categories. Our approach derives a novel feature space based on foundation features that ensure multi-view consistency and overcome symmetry ambiguities inherent in foundation features using a self-supervised triplet loss. Additionally, we introduce a texture-invariant pose refinement technique that performs dense alignment in normalized object coordinates, estimated through the enhanced feature space. We conduct extensive evaluations on the real-world ScanNet25k dataset, where our method outperforms SOTA weakly supervised baselines by +4.3% mean alignment accuracy and is the only weakly supervised approach to surpass the supervised ROCA by +2.7%. To assess generalization, we introduce SUN2CAD, a real-world test set with 20 novel object categories, where our method achieves SOTA results without prior training on them.
Vulnerability-Aware Spatio-Temporal Learning for Generalizable Deepfake Video Detection
Authors: Dat Nguyen; Marcella Astrid; Anis Kacem; Enjie Ghorbel; Djamila Aouada
Venue: ICCV 2025 (main)

Abstract: Detecting deepfake videos is highly challenging given the complexity of characterizing spatio-temporal artifacts. Most existing methods rely on binary classifiers trained using real and fake image sequences, therefore hindering their generalization capabilities to unseen generation methods. Moreover, with the constant progress in generative Artificial Intelligence (AI), deepfake artifacts are becoming imperceptible at both the spatial and the temporal levels, making them extremely difficult to capture. To address these issues, we propose a fine-grained deepfake video detection approach called FakeSTormer that enforces the modeling of subtle spatio-temporal inconsistencies while avoiding overfitting. Specifically, we introduce a multi-task learning framework that incorporates two auxiliary branches for explicitly attending artifact-prone spatial and temporal regions. Additionally, we propose a video-level data synthesis strategy that generates pseudo-fake videos with subtle spatio-temporal artifacts, providing high-quality samples and hand-free annotations for our additional branches. Extensive experiments on several challenging benchmarks demonstrate the superiority of our approach compared to recent state-of-the-art methods. The code is available at https://github.com/10Ring/FakeSTormer.
Efficient Autoregressive Shape Generation via Octree-Based Adaptive Tokenization
Authors: Kangle Deng; Hsueh-Ti Derek Liu; Yiheng Zhu; Xiaoxia Sun; Chong Shang; Kiran S. Bhat; Deva Ramanan; Jun-Yan Zhu; Maneesh Agrawala; Tinghui Zhou
Venue: ICCV 2025 (main)

Abstract: Many 3D generative models rely on variational autoencoders (VAEs) to learn compact shape representations. However, existing methods encode all shapes into a fixed-size token, disregarding the inherent variations in scale and complexity across 3D data. This leads to inefficient latent representations that can compromise downstream generation. We address this challenge by introducing Octree-based Adaptive Tokenization, a novel framework that adjusts the dimension of latent representations according to shape complexity. Our approach constructs an adaptive octree structure guided by a quadric-error-based subdivision criterion and allocates a shape latent vector to each octree cell using a query-based transformer. Building upon this tokenization, we develop an octree-based autoregressive generative model that effectively leverages these variable-sized representations in shape generation. Extensive experiments demonstrate that our approach reduces token counts by 50% compared to fixed-size methods while maintaining comparable visual quality. When using a similar token length, our method produces significantly higher-quality shapes. When incorporated with our downstream generative model, our method creates more detailed and diverse 3D content than existing approaches.
Cycle-Consistent Learning for Joint Layout-to-Image Generation and Object Detection
Authors: Xinhao Cai; Qiuxia Lai; Gensheng Pei; Xiangbo Shu; Yazhou Yao; Wenguan Wang
Venue: ICCV 2025 (main)

Abstract: In this paper, we propose a generation-detection cycle consistent (GDCC) learning framework that jointly optimizes both layout-to-image (L2I) generation and object detection (OD) tasks in an end-to-end manner. The key of GDCC lies in the inherent duality between the two tasks, where L2I takes all object boxes and labels as input conditions to generate images, and OD maps images back to these layout conditions. Specifically, in GDCC, L2I generation is guided by a layout translation cycle loss, ensuring that the layouts used to generate images align with those predicted from the synthesized images. Similarly, OD benefits from an image translation cycle loss, which enforces consistency between the synthesized images fed into the detector and those generated from predicted layouts. While current L2I and OD tasks benefit from large-scale annotated layout-image pairs, our GDCC enables more efficient use of annotation-free synthetic data, thereby further enhancing data efficiency. It is worth noting that our GDCC framework is computationally efficient thanks to the perturbative single-step sampling strategy and a priority timestep re-sampling strategy during training. Besides, GDCC preserves the architectures of L2I, OD models, and the generation pipeline within the framework, thus maintaining the original inference speed. Extensive experiments demonstrate that GDCC significantly improves the controllability of diffusion models and the accuracy of object detectors.
MUG: Pseudo Labeling Augmented Audio-Visual Mamba Network for Audio-Visual Video Parsing
Authors: Langyu Wang; Bingke Zhu; Yingying Chen; Yiyuan Zhang; Ming Tang; Jinqiao Wang
Venue: ICCV 2025 (main)

Abstract: The weakly-supervised audio-visual video parsing (AVVP) aims to predict all modality-specific events and locate their temporal boundaries. Despite significant progress, due to the limitations of the weakly-supervised and the deficiencies of the model architecture, existing methods are lacking in simultaneously improving both the segment-level prediction and the event-level prediction. In this work, we propose an audio-visual Mamba network with pseudo labeling aUGmentation (MUG) for emphasising the uniqueness of each segment and excluding the noise interference from the alternate modalities. Specifically, we annotate some of the pseudo-labels based on previous work. Using unimodal pseudo-labels, we perform cross-modal random combinations to generate new data, which can enhance the model's ability to parse various segment-level event combinations. For feature processing and interaction, we employ a audio-visual mamba network. The AV-Mamba enhances the ability to perceive different segments and excludes additional modal noise while sharing similar modal information. Our extensive experiments demonstrate that MUG improves state-of-the-art results on LLP dataset in all metrics ( e.g., gains of 2.1% and 1.2% in terms of visual Segment-level and audio Segment-level metrics). Our code is available at https://github.com/WangLY136/MUG.
LIFT: Latent Implicit Functions for Task- and Data-Agnostic Encoding
Authors: Amirhossein Kazerouni; Soroush Mehraban; Michael Brudno; Babak Taati
Venue: ICCV 2025 (main)

Abstract: Implicit Neural Representations (INRs) are proving to be a powerful paradigm in unifying task modeling across diverse data domains, offering key advantages such as memory efficiency and resolution independence. Conventional deep learning models are typically modality-dependent, often requiring custom architectures and objectives for different types of signals. However, existing INR frameworks frequently rely on global latent vectors or exhibit computational inefficiencies that limit their broader applicability. We introduce **LIFT**, a novel, high-performance framework that addresses these challenges by capturing multiscale information through meta-learning. LIFT leverages multiple parallel localized implicit functions alongside a hierarchical latent generator to produce unified latent representations that span local, intermediate, and global features. This architecture facilitates smooth transitions across local regions, enhancing expressivity while maintaining inference efficiency. Additionally, we introduce ReLIFT, an enhanced variant of LIFT that incorporates residual connections and expressive frequency encodings. With this straightforward approach, ReLIFT effectively addresses the convergence-capacity gap found in comparable methods, providing an efficient yet powerful solution to improve capacity and speed up convergence. Empirical results show that LIFT achieves state-of-the-art (SOTA) performance in generative modeling and classification tasks, with notable reductions in computational costs. Moreover, in single-task settings, the streamlined ReLIFT architecture proves effective in signal representations and inverse problem tasks.
Long-Context State-Space Video World Models
Authors: Ryan Po; Yotam Nitzan; Richard Zhang; Berlin Chen; Tri Dao; Eli Shechtman; Gordon Wetzstein; Xun Huang
Venue: ICCV 2025 (main)

Abstract: Video diffusion models have recently shown promise for world modeling through autoregressive frame prediction conditioned on actions. However, they struggle to maintain long-term memory due to the high computational cost associated with processing extended sequences in attention layers. To overcome this limitation, we propose a novel architecture leveraging state-space models (SSMs) to extend temporal memory without compromising computational efficiency. Unlike previous approaches that retrofit SSMs for non-causal vision tasks, our method fully exploits the inherent advantages of SSMs in causal sequence modeling. Central to our design is a block-wise SSM scanning scheme, which strategically trades off spatial consistency for extended temporal memory, combined with dense local attention to ensure coherence between consecutive frames. We evaluate the long-term memory capabilities of our model through spatial retrieval and reasoning tasks over extended horizons. Experiments on Memory Maze and Minecraft datasets demonstrate that our approach surpasses baselines in preserving long-range memory, while maintaining practical inference speeds suitable for interactive applications.
Dataset Distillation via the Wasserstein Metric
Authors: Haoyang Liu; Yijiang Li; Tiancheng Xing; Peiran Wang; Vibhu Dalal; Luwei Li; Jingrui He; Haohan Wang
Venue: ICCV 2025 (main)

Abstract: Dataset Distillation (DD) aims to generate a compact synthetic dataset that enables models to achieve performance comparable to training on the full large dataset, significantly reducing computational costs. Drawing from optimal transport theory, we introduce WMDD (Wasserstein Metric-based Dataset Distillation), a straightforward yet powerful method that employs the Wasserstein metric to enhance distribution matching. We compute the Wasserstein barycenter of features from a pretrained classifier to capture essential characteristics of the original data distribution. By optimizing synthetic data to align with this barycenter in feature space and leveraging per-class BatchNorm statistics to preserve intra-class variations, WMDD maintains the efficiency of distribution matching approaches while achieving state-of-the-art results across various high-resolution datasets. Our extensive experiments demonstrate WMDD's effectiveness and adaptability, highlighting its potential for advancing machine learning applications at scale. Code is available at https://github.com/Liu-Hy/WMDD and website at https://liu-hy.github.io/WMDD/.
IRGPT: Understanding Real-world Infrared Image with Bi-cross-modal Curriculum on Large-scale Benchmark
Authors: Zhe Cao; Jin Zhang; Ruiheng Zhang
Venue: ICCV 2025 (main)

Abstract: Real-world infrared imagery presents unique challenges for vision-language models due to the scarcity of aligned text data and domain-specific characteristics. Although existing methods have advanced the field, their reliance on synthetic infrared images generated through style transfer from visible images, which limits their ability to capture the unique characteristics of the infrared modality. To address this, we propose IRGPT, the first multi-modal large language model for real-world infrared images, built upon a large-scale InfraRed-Text Dataset (IR-TD) comprising over 260K authentic image-text pairs. The proposed IR-TD dataset contains real infrared images paired with meticulously handcrafted texts, where the initial drafts originated from two complementary processes: (1) LLM-generated descriptions of visible images, and (2) rule-based descriptions of annotations. Furthermore, we introduce a bi-cross-modal curriculum transfer learning strategy that systematically transfers knowledge from visible to infrared domains by considering the difficulty scores of both infrared-visible and infrared-text. Evaluated on a benchmark of 9 tasks (e.g., recognition, grounding), IRGPT achieves state-of-the-art performance even compared with larger-scale models.
Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration
Authors: Katie Z Luo; Minh-Quan Dao; Zhenzhen Liu; Mark Campbell; Wei-Lun Chao; Kilian Q Weinberger; Ezio Malis; Vincent Fremont; Bharath Hariharan; Mao Shan; Stewart Worrall; Julie Stephany Berrio Perez
Venue: ICCV 2025 (main)

Abstract: Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X datasets are limited in scope, diversity, and quality. To address these gaps, we present Mixed Signals, a comprehensive V2X dataset featuring 45.1k point clouds and 240.6k bounding boxes collected from three connected autonomous vehicles (CAVs) equipped with two different configurations of LiDAR sensors, plus a roadside unit with dual LiDARs. Our dataset provides point clouds and bounding box annotations across 10 classes, ensuring reliable data for perception training. We provide detailed statistical analysis on the quality of our dataset and extensively benchmark existing V2X methods on it. Mixed Signals is ready-to-use, with precise alignment and consistent annotations across time and viewpoints. We hope our work advances research in the emerging, impactful field of V2X perception.
Exploring Weather-aware Aggregation and Adaptation for Semantic Segmentation under Adverse Conditions
Authors: Yuwen Pan; Rui Sun; Wangkai Li; Tianzhu Zhang
Venue: ICCV 2025 (main)

Abstract: Semantic segmentation under adverse conditions is crucial for ensuring robust and accurate visual perception in challenging weather conditions. The distinct characteristics of extreme scenarios hinder traditional segmentation paradigms, highlighting the necessity for tailored approaches for adverse weathers. Due to the scarcity of labeled data in such scenarios, the unsupervised domain adaptation paradigm is commonly utilized to leverage knowledge from normal weather conditions. Although existing methods strive to absorb information from labeled normal weather data and unlabeled adverse condition images, they face significant challenges due to weather unawareness and severe feature heterogeneity, thus struggling to effectively parse scenes under adverse conditions. In this paper, we propose a novel weather-aware aggregation and adaptation network that leverages characteristic knowledge to achieve weather homogenization and enhance scene perception. Specifically, we introduce amplitude prompt aggregation to capture essential characteristics from the Fourier frequency domain that are indicative of different weather conditions. Additionally, we employ weather heterogeneity adaptation to mitigate the inter-domain heterogeneity, thereby achieving feature homogenization across diverse environments. Extensive experimental results on multiple challenging benchmarks demonstrate that our method achieves consistent improvements for semantic segmentation under adverse conditions.
OracleFusion: Assisting the Decipherment of Oracle Bone Script with Structurally Constrained Semantic Typography
Authors: Caoshuo Li; Zengmao Ding; Xiaobin Hu; Bang Li; Donghao Luo; AndyPian Wu; Chaoyang Wang; Chengjie Wang; Taisong Jin; Seven Shu; Yunsheng Wu; Yongge Liu; Rongrong Ji
Venue: ICCV 2025 (main)

Abstract: As one of the earliest ancient languages, Oracle Bone Script (**OBS**) encapsulates the cultural records and intellectual expressions of ancient civilizations. Despite the discovery of approximately 4,500 OBS characters, only about 1,600 have been deciphered. The remaining undeciphered ones, with their complex structure and abstract imagery, pose significant challenges for interpretation. To address these challenges, this paper proposes a novel two-stage semantic typography framework, named **OracleFusion**. In the first stage, this approach leverages the Multimodal Large Language Model (MLLM) with enhanced Spatial Awareness Reasoning (SAR) to analyze the glyph structure of the OBS character and perform visual localization of key components. In the second stage, we introduce Oracle Structural Vector Fusion (**OSVF**), incorporating glyph structure constraints and glyph maintenance constraints to ensure the accurate generation of semantically enriched vector fonts. This approach preserves the objective integrity of the glyph structure, offering visually enhanced representations that assist experts in deciphering OBS. Extensive qualitative and quantitative experiments demonstrate that OracleFusion outperforms state-of-the-art baseline models in terms of semantics, visual appeal, and glyph maintenance, significantly enhancing both readability and aesthetic quality. Furthermore, OracleFusion provides expert-like insights on unseen oracle characters, making it a valuable tool for advancing the decipherment of OBS.
Streaming VideoLLMs for Real-Time Procedural Video Understanding
Authors: Dibyadip Chatterjee; Edoardo Remelli; Yale Song; Bugra Tekin; Abhay Mittal; Bharat Bhatnagar; Necati Cihan Camgoz; Shreyas Hampali; Eric Sauser; Shugao Ma; Angela Yao; Fadime Sener
Venue: ICCV 2025 (main)

Abstract: We introduce ProVideLLM, an end-to-end framework for real-time procedural video understanding. ProVideLLM integrates a multimodal cache configured to store two types of tokens -- verbalized text tokens, which provide compressed textual summaries of long-term observations, and visual tokens, encoded with DETR-QFormer to capture fine-grained details from short-term observations. This design reduces token count by 22xover existing methods when representing one hour of long-term observations while effectively encoding fine-granularity of the present. By interleaving these tokens in our multimodal cache, ProVideLLM achieves sub-linear scaling of memory and compute with video length, ensuring per-frame streaming inference at 10 FPS and streaming dialogue at 25 FPS, with a minimal 2GB GPU memory footprint. ProVideLLM also sets new state-of-the-art results on six procedural tasks across four datasets.