pwc-archive/papers-with-abstracts huggingface dataset:
TUMTraffic-VideoQA: A Benchmark for Unified Spatio-Temporal Video Understanding in Traffic Scenes
Authors: Xingcheng Zhou, Konstantinos Larintzakis, Hao Guo, Walter Zimmer, MingYu Liu, Hu Cao, Jiajie Zhang, Venkatnarayanan Lakshminarasimhan, Leah Strand, Alois C. Knoll
Date: 2025-02-04 00:00:00
Tasks: Autonomous Driving, Multiple-choice, Object, Question Answering, Traffic Object Detection, Video Question Answering, Video Understanding

We present TUMTraffic-VideoQA, a novel dataset and benchmark designed for spatio-temporal video understanding in complex roadside traffic scenarios. The dataset comprises 1,000 videos, featuring 85,000 multiple-choice QA pairs, 2,300 object captioning, and 5,700 object grounding annotations, encompassing diverse real-world conditions such as adverse weather and traffic anomalies. By incorporating tuple-based spatio-temporal object expressions, TUMTraffic-VideoQA unifies three essential tasks-multiple-choice video question answering, referred object captioning, and spatio-temporal object grounding-within a cohesive evaluation framework. We further introduce the TUMTraffic-Qwen baseline model, enhanced with visual token sampling strategies, providing valuable insights into the challenges of fine-grained spatio-temporal reasoning. Extensive experiments demonstrate the dataset's complexity, highlight the limitations of existing models, and position TUMTraffic-VideoQA as a robust foundation for advancing research in intelligent transportation systems. The dataset and benchmark are publicly available to facilitate further exploration.
Personalization Toolkit: Training Free Personalization of Large Vision Language Models
Authors: Soroush Seifi, Vaggelis Dorovatas, Daniel Olmeda Reino, Rahaf Aljundi
Date: 2025-02-04 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation, Visual Prompting

Large Vision Language Models (LVLMs) have significant potential to deliver personalized assistance by adapting to individual users' unique needs and preferences. Personalization of LVLMs is an emerging area that involves customizing models to recognize specific object instances and provide tailored responses. However, existing approaches rely on time-consuming test-time training for each user and object, rendering them impractical. This paper proposes a novel, training-free approach to LVLM personalization by leveraging pre-trained vision foundation models to extract distinct features, retrieval-augmented generation (RAG) techniques to recognize instances in the visual input, and visual prompting methods. Our model-agnostic vision toolkit enables flexible and efficient personalization without extensive retraining. We demonstrate state-of-the-art results, outperforming conventional training-based approaches and establish a new standard for LVLM personalization.
IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning
Authors: Quan Zhang, Yuxin Qi, Xi Tang, Jinwei Fang, Xi Lin, Ke Zhang, Chun Yuan
Date: 2025-02-04 00:00:00
Tasks: Image Manipulation, Image Manipulation Detection, Image Segmentation, Medical Image Segmentation, Prompt Learning, Semantic Segmentation

Using extensive training data from SA-1B, the Segment Anything Model (SAM) has demonstrated exceptional generalization and zero-shot capabilities, attracting widespread attention in areas such as medical image segmentation and remote sensing image segmentation. However, its performance in the field of image manipulation detection remains largely unexplored and unconfirmed. There are two main challenges in applying SAM to image manipulation detection: a) reliance on manual prompts, and b) the difficulty of single-view information in supporting cross-dataset generalization. To address these challenges, we develops a cross-view prompt learning paradigm called IMDPrompter based on SAM. Benefiting from the design of automated prompts, IMDPrompter no longer relies on manual guidance, enabling automated detection and localization. Additionally, we propose components such as Cross-view Feature Perception, Optimal Prompt Selection, and Cross-View Prompt Consistency, which facilitate cross-view perceptual learning and guide SAM to generate accurate masks. Extensive experimental results from five datasets (CASIA, Columbia, Coverage, IMD2020, and NIST16) validate the effectiveness of our proposed method.
Towards Consistent and Controllable Image Synthesis for Face Editing
Authors: Mengting Wei, Tuomas Varanka, Yante Li, Xingxun Jiang, Huai-Qian Khor, Guoying Zhao
Date: 2025-02-04 00:00:00
Tasks: Attribute, Denoising, Image Generation, Image Reconstruction

Current face editing methods mainly rely on GAN-based techniques, but recent focus has shifted to diffusion-based models due to their success in image reconstruction. However, diffusion models still face challenges in manipulating fine-grained attributes and preserving consistency of attributes that should remain unchanged. To address these issues and facilitate more convenient editing of face images, we propose a novel approach that leverages the power of Stable-Diffusion models and crude 3D face models to control the lighting, facial expression and head pose of a portrait photo. We observe that this task essentially involve combinations of target background, identity and different face attributes. We aim to sufficiently disentangle the control of these factors to enable high-quality of face editing. Specifically, our method, coined as RigFace, contains: 1) A Spatial Arrtibute Encoder that provides presise and decoupled conditions of background, pose, expression and lighting; 2) An Identity Encoder that transfers identity features to the denoising UNet of a pre-trained Stable-Diffusion model; 3) An Attribute Rigger that injects those conditions into the denoising UNet. Our model achieves comparable or even superior performance in both identity preservation and photorealism compared to existing face editing models.
High-Fidelity Human Avatars from Laptop Webcams using Edge Compute
Authors: Akash Haridas, Imran N. Junejo
Date: 2025-02-04 00:00:00

Applications of generating photo-realistic human avatars are many, however, high-fidelity avatar generation traditionally required expensive professional camera rigs and artistic labor, but recent research has enabled constructing them automatically from smartphones with RGB and IR sensors. However, these new methods still rely on the presence of high-resolution cameras on modern smartphones and often require offloading the processing to powerful servers with GPUs. Modern applications such as video conferencing call for the ability to generate these avatars from consumer-grade laptop webcams using limited compute available on-device. In this work, we develop a novel method based on 3D morphable models, landmark detection, photo-realistic texture GANs, and differentiable rendering to tackle the problem of low webcam image quality and edge computation. We build an automatic system to generate high-fidelity animatable avatars under these limitations, leveraging the neural compute capabilities of mobile chips.
A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation
Authors: Edward Ellis, Andrew Bulpitt, Nasim Parsa, Michael F Byrne, Sharib Ali
Date: 2025-02-04 00:00:00
Tasks: Image Segmentation, Representation Learning, Self-Supervised Learning, Semantic Segmentation

Ultrasound (US) imaging is clinically invaluable due to its noninvasive and safe nature. However, interpreting US images is challenging, requires significant expertise, and time, and is often prone to errors. Deep learning offers assistive solutions such as segmentation. Supervised methods rely on large, high-quality, and consistently labeled datasets, which are challenging to curate. Moreover, these methods tend to underperform on out-of-distribution data, limiting their clinical utility. Self-supervised learning (SSL) has emerged as a promising alternative, leveraging unlabeled data to enhance model performance and generalisability. We introduce a contrastive SSL approach tailored for B-mode US images, incorporating a novel Relation Contrastive Loss (RCL). RCL encourages learning of distinct features by differentiating positive and negative sample pairs through a learnable metric. Additionally, we propose spatial and frequency-based augmentation strategies for the representation learning on US images. Our approach significantly outperforms traditional supervised segmentation methods across three public breast US datasets, particularly in data-limited scenarios. Notable improvements on the Dice similarity metric include a 4% increase on 20% and 50% of the BUSI dataset, nearly 6% and 9% improvements on 20% and 50% of the BrEaST dataset, and 6.4% and 3.7% improvements on 20% and 50% of the UDIAT dataset, respectively. Furthermore, we demonstrate superior generalisability on the out-of-distribution UDIAT dataset with performance boosts of 20.6% and 13.6% compared to the supervised baseline using 20% and 50% of the BUSI and BrEaST training data, respectively. Our research highlights that domain-inspired SSL can improve US segmentation, especially under data-limited conditions.
VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
Authors: Hila Chefer, Uriel Singer, Amit Zohar, Yuval Kirstain, Adam Polyak, Yaniv Taigman, Lior Wolf, Shelly Sheynin
Date: 2025-02-04 00:00:00
Tasks: Motion Generation, motion prediction, Video Generation

Despite tremendous recent progress, generative video models still struggle to capture real-world motion, dynamics, and physics. We show that this limitation arises from the conventional pixel reconstruction objective, which biases models toward appearance fidelity at the expense of motion coherence. To address this, we introduce VideoJAM, a novel framework that instills an effective motion prior to video generators, by encouraging the model to learn a joint appearance-motion representation. VideoJAM is composed of two complementary units. During training, we extend the objective to predict both the generated pixels and their corresponding motion from a single learned representation. During inference, we introduce Inner-Guidance, a mechanism that steers the generation toward coherent motion by leveraging the model's own evolving motion prediction as a dynamic guidance signal. Notably, our framework can be applied to any video model with minimal adaptations, requiring no modifications to the training data or scaling of the model. VideoJAM achieves state-of-the-art performance in motion coherence, surpassing highly competitive proprietary models while also enhancing the perceived visual quality of the generations. These findings emphasize that appearance and motion can be complementary and, when effectively integrated, enhance both the visual quality and the coherence of video generation. Project website: https://hila-chefer.github.io/videojam-paper.github.io/
Graph-based Document Structure Analysis
Authors: Yufan Chen, Ruiping Liu, Junwei Zheng, Di Wen, Kunyu Peng, Jiaming Zhang, Rainer Stiefelhagen
Date: 2025-02-04 00:00:00
Tasks: Document Layout Analysis, Relation

When reading a document, glancing at the spatial layout of a document is an initial step to understand it roughly. Traditional document layout analysis (DLA) methods, however, offer only a superficial parsing of documents, focusing on basic instance detection and often failing to capture the nuanced spatial and logical relations between instances. These limitations hinder DLA-based models from achieving a gradually deeper comprehension akin to human reading. In this work, we propose a novel graph-based Document Structure Analysis (gDSA) task. This task requires that model not only detects document elements but also generates spatial and logical relations in form of a graph structure, allowing to understand documents in a holistic and intuitive manner. For this new task, we construct a relation graph-based document structure analysis dataset (GraphDoc) with 80K document images and 4.13M relation annotations, enabling training models to complete multiple tasks like reading order, hierarchical structures analysis, and complex inter-element relation inference. Furthermore, a document relation graph generator (DRGG) is proposed to address the gDSA task, which achieves performance with 57.6% at mAP$_g$@0.5 for a strong benchmark baseline on this novel task and dataset. We hope this graphical representation of document structure can mark an innovative advancement in document structure analysis and understanding. The new dataset and code will be made publicly available at https://yufanchen96.github.io/projects/GraphDoc.
Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction
Authors: Ruochen Li, Tanqiu Qiao, Stamos Katsigiannis, Zhanxing Zhu, Hubert P. H. Shum
Date: 2025-02-04 00:00:00
Tasks: Pedestrian Trajectory Prediction, Trajectory Prediction

Pedestrian trajectory prediction aims to forecast future movements based on historical paths. Spatial-temporal (ST) methods often separately model spatial interactions among pedestrians and temporal dependencies of individuals. They overlook the direct impacts of interactions among different pedestrians across various time steps (i.e., high-order cross-time interactions). This limits their ability to capture ST inter-dependencies and hinders prediction performance. To address these limitations, we propose UniEdge with three major designs. Firstly, we introduce a unified ST graph data structure that simplifies high-order cross-time interactions into first-order relationships, enabling the learning of ST inter-dependencies in a single step. This avoids the information loss caused by multi-step aggregation. Secondly, traditional GNNs focus on aggregating pedestrian node features, neglecting the propagation of implicit interaction patterns encoded in edge features. We propose the Edge-to-Edge-Node-to-Node Graph Convolution (E2E-N2N-GCN), a novel dual-graph network that jointly models explicit N2N social interactions among pedestrians and implicit E2E influence propagation across these interaction patterns. Finally, to overcome the limited receptive fields and challenges in capturing long-range dependencies of auto-regressive architectures, we introduce a transformer encoder-based predictor that enables global modeling of temporal correlation. UniEdge outperforms state-of-the-arts on multiple datasets, including ETH, UCY, and SDD.
Privacy Attacks on Image AutoRegressive Models
Authors: Antoni Kowalczuk, Jan Dubiński, Franziska Boenisch, Adam Dziedzic
Date: 2025-02-04 00:00:00
Tasks: Inference Attack, Membership Inference Attack

Image autoregressive (IAR) models have surpassed diffusion models (DMs) in both image quality (FID: 1.48 vs. 1.58) and generation speed. However, their privacy risks remain largely unexplored. To address this, we conduct a comprehensive privacy analysis comparing IARs to DMs. We develop a novel membership inference attack (MIA) that achieves a significantly higher success rate in detecting training images (TPR@FPR=1%: 86.38% for IARs vs. 4.91% for DMs). Using this MIA, we perform dataset inference (DI) and find that IARs require as few as six samples to detect dataset membership, compared to 200 for DMs, indicating higher information leakage. Additionally, we extract hundreds of training images from an IAR (e.g., 698 from VAR-d30). Our findings highlight a fundamental privacy-utility trade-off: while IARs excel in generation quality and speed, they are significantly more vulnerable to privacy attacks. This suggests that incorporating techniques from DMs, such as per-token probability modeling using diffusion, could help mitigate IARs' privacy risks. Our code is available at https://github.com/sprintml/privacy_attacks_against_iars.
Uncertainty Quantification for Collaborative Object Detection Under Adversarial Attacks
Authors: Huiqun Huang, Cong Chen, Jean-Philippe Monteuuis, Jonathan Petit, Fei Miao
Date: 2025-02-04 00:00:00
Tasks: Adversarial Robustness, Autonomous Driving, Autonomous Vehicles, Conformal Prediction, Object, object-detection, Object Detection, Uncertainty Quantification

Collaborative Object Detection (COD) and collaborative perception can integrate data or features from various entities, and improve object detection accuracy compared with individual perception. However, adversarial attacks pose a potential threat to the deep learning COD models, and introduce high output uncertainty. With unknown attack models, it becomes even more challenging to improve COD resiliency and quantify the output uncertainty for highly dynamic perception scenes such as autonomous vehicles. In this study, we propose the Trusted Uncertainty Quantification in Collaborative Perception framework (TUQCP). TUQCP leverages both adversarial training and uncertainty quantification techniques to enhance the adversarial robustness of existing COD models. More specifically, TUQCP first adds perturbations to the shared information of randomly selected agents during object detection collaboration by adversarial training. TUQCP then alleviates the impacts of adversarial attacks by providing output uncertainty estimation through learning-based module and uncertainty calibration through conformal prediction. Our framework works for early and intermediate collaboration COD models and single-agent object detection models. We evaluate TUQCP on V2X-Sim, a comprehensive collaborative perception dataset for autonomous driving, and demonstrate a 80.41% improvement in object detection accuracy compared to the baselines under the same adversarial attacks. TUQCP demonstrates the importance of uncertainty quantification to COD under adversarial attacks.
Mosaic3D: Foundation Dataset and Model for Open-Vocabulary 3D Segmentation
Authors: Junha Lee, Chunghyun Park, Jaesung Choe, Yu-Chiang Frank Wang, Jan Kautz, Minsu Cho, Chris Choy
Date: 2025-02-04 00:00:00
Proceeding: CVPR 2025 1
Tasks: Contrastive Learning, Decoder, Image Segmentation, Instance Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

We tackle open-vocabulary 3D scene understanding by introducing a novel data generation pipeline and training framework. Our method addresses three critical requirements for effective training: precise 3D region segmentation, comprehensive textual descriptions, and sufficient dataset scale. By leveraging state-of-the-art open-vocabulary image segmentation models and region-aware Vision-Language Models, we develop an automatic pipeline that generates high-quality 3D mask-text pairs. Applying this pipeline to multiple 3D scene datasets, we create Mosaic3D-5.6M, a dataset of over 30K annotated scenes with 5.6M mask-text pairs, significantly larger than existing datasets. Building upon this data, we propose Mosaic3D, a foundation model combining a 3D encoder trained with contrastive learning and a lightweight mask decoder for open-vocabulary 3D semantic and instance segmentation. Our approach achieves state-of-the-art results on open-vocabulary 3D semantic and instance segmentation tasks including ScanNet200, Matterport3D, and ScanNet++, with ablation studies validating the effectiveness of our large-scale training data.
Revisiting Expected Possession Value in Football: Introducing a Benchmark, U-Net Architecture, and Reward and Risk for Passes
Authors: Thijs Overmeer, Tim Janssen, Wim P. M. Nuijten
Date: 2025-02-04 00:00:00

This paper introduces the first Expected Possession Value (EPV) benchmark and a new and improved EPV model for football. Through the introduction of the OJN-Pass-EPV benchmark, we present a novel method to quantitatively assess the quality of EPV models by using pairs of game states with given relative EPVs. Next, we attempt to replicate the results of Fern\'andez et al. (2021) using a dataset containing Dutch Eredivisie and World Cup matches. Following our failure to do so, we propose a new architecture based on U-net-type convolutional neural networks, achieving good results in model loss and Expected Calibration Error. Finally, we present an improved pass model that incorporates ball height and contains a new dual-component pass value model that analyzes reward and risk. The resulting EPV model correctly identifies the higher value state in 78% of the game state pairs in the OJN-Pass-EPV benchmark, demonstrating its ability to accurately assess goal-scoring potential. Our findings can help assess the quality of EPV models, improve EPV predictions, help assess potential reward and risk of passing decisions, and improve player and team performance.
Calibrated Multi-Preference Optimization for Aligning Diffusion Models
Authors: Kyungmin Lee, Xiaohang Li, Qifei Wang, Junfeng He, Junjie Ke, Ming-Hsuan Yang, Irfan Essa, Jinwoo Shin, Feng Yang, Yinxiao Li
Date: 2025-02-04 00:00:00
Proceeding: CVPR 2025 1

Aligning text-to-image (T2I) diffusion models with preference optimization is valuable for human-annotated datasets, but the heavy cost of manual data collection limits scalability. Using reward models offers an alternative, however, current preference optimization methods fall short in exploiting the rich information, as they only consider pairwise preference distribution. Furthermore, they lack generalization to multi-preference scenarios and struggle to handle inconsistencies between rewards. To address this, we present Calibrated Preference Optimization (CaPO), a novel method to align T2I diffusion models by incorporating the general preference from multiple reward models without human annotated data. The core of our approach involves a reward calibration method to approximate the general preference by computing the expected win-rate against the samples generated by the pretrained models. Additionally, we propose a frontier-based pair selection method that effectively manages the multi-preference distribution by selecting pairs from Pareto frontiers. Finally, we use regression loss to fine-tune diffusion models to match the difference between calibrated rewards of a selected pair. Experimental results show that CaPO consistently outperforms prior methods, such as Direct Preference Optimization (DPO), in both single and multi-reward settings validated by evaluation on T2I benchmarks, including GenEval and T2I-Compbench.
COCONut-PanCap: Joint Panoptic Segmentation and Grounded Captions for Fine-Grained Understanding and Generation
Authors: Xueqing Deng, Qihang Yu, Ali Athar, Chenglin Yang, Linjie Yang, Xiaojie Jin, Xiaohui Shen, Liang-Chieh Chen
Date: 2025-02-04 00:00:00
Tasks: Image Captioning, Panoptic Segmentation, Segmentation

This paper introduces the COCONut-PanCap dataset, created to enhance panoptic segmentation and grounded image captioning. Building upon the COCO dataset with advanced COCONut panoptic masks, this dataset aims to overcome limitations in existing image-text datasets that often lack detailed, scene-comprehensive descriptions. The COCONut-PanCap dataset incorporates fine-grained, region-level captions grounded in panoptic segmentation masks, ensuring consistency and improving the detail of generated captions. Through human-edited, densely annotated descriptions, COCONut-PanCap supports improved training of vision-language models (VLMs) for image understanding and generative models for text-to-image tasks. Experimental results demonstrate that COCONut-PanCap significantly boosts performance across understanding and generation tasks, offering complementary benefits to large-scale datasets. This dataset sets a new benchmark for evaluating models on joint panoptic segmentation and grounded captioning tasks, addressing the need for high-quality, detailed image-text annotations in multi-modal learning.