pwc-archive/papers-with-abstracts huggingface dataset:
SynTab-LLaVA: Enhancing Multimodal Table Understanding with Decoupled Synthesis
Authors: Bangbang Zhou, Zuan Gao, Zixiao Wang, Boqiang Zhang, Yuxin Wang, Zhineng Chen, Hongtao Xie
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Large Language Model

    Due to the limited scale of multimodal table understanding (MTU) data, model performance is constrained. A straightforward approach is to use multimodal large language models to obtain more samples, but this may cause hallucinations, generate incorrect sample pairs, and cost significantly.To address the above issues, we design a simple yet effective synthesis framework that consists of two independent steps: table image rendering and table question and answer (Q&A) pairs generation.We use table codes (HTML, LaTeX, Markdown) to synthesize images and generate Q&A pairs with large language model (LLM).This approach leverages LLM's high concurrency and low cost to boost annotation efficiency and reduce expenses. By inputting code instead of images, LLMs can directly access the content and structure of the table, reducing hallucinations in table understanding and improving the accuracy of generated Q&A pairs. Finally, we synthesize a large-scale MTU dataset, SynTab, containing 636K images and 1.8M samples costing within \200 in US dollars. We further introduce a generalist tabular multimodal model, SynTab-LLaVA. This model not only effectively extracts local textual content within the table but also enables global modeling of relationships between cells.SynTab-LLaVA achieves SOTA performance on 21 out of 24 in-domain and out-of-domain benchmarks, demonstrating the effectiveness and generalization of our method. The Code is available at \href https://github.com/bang123-box/SynTab-LLaVA  SynTab-LLaVA .
Doppelgangers and Adversarial Vulnerability
Authors: George Kamberov
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Many machine learning (ML) classifiers are claimed to outperform humans, but they still make mistakes that humans do not. The most notorious examples of such mistakes are adversarial visual metamers. This paper aims to define and investigate the phenomenon of adversarial Doppelgangers (AD), which includes adversarial visual metamers, and to compare the performance and robustness of ML classifiers to human performance.We find that AD are inputs that are close to each other with respect to a perceptual metric defined in this paper, and show that AD are qualitatively different from the usual adversarial examples. The vast majority of classifiers are vulnerable to AD and robustness-accuracy trade-offs may not improve them. Some classification problems may not admit any AD robust classifiers because the underlying classes are ambiguous.  We provide criteria that can be used to determine whether a classification problem is well defined or not; describe of an AD robust classifiers' structure and attributes; introduce and explore the notions of conceptual entropy and regions of conceptual ambiguity for classifiers that are vulnerable to AD attacks, along with methods to bound the AD fooling rate of an attack. We define the notion of classifiers that exhibit hyper-sensitive behavior, that is, classifiers whose only mistakes are adversarial Doppelgangers. Improving the AD robustness of hyper-sensitive classifiers proving accuracy.  We identify conditions guaranteeing that all classifiers with sufficiently high accuracy are hyper-sensitive.
Advancing Manga Analysis: Comprehensive Segmentation Annotations for the Manga109 Dataset
Authors: Minshan Xie, Jian Lin, Hanyuan Liu, Chengze Li, Tien-Tsin Wong
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

    Manga, a popular form of multimodal artwork, has traditionally been overlooked in deep learning advancements due to the absence of a robust dataset and comprehensive annotation. Manga segmentation is the key to the digital migration of manga. There exists a significant domain gap between the manga and the natural images, that fails most existing learning-based methods. To address this gap, we introduce an augmented segmentation annotation for the Manga109 dataset, a collection of 109 manga volumes, that offers intricate artworks in a rich variety of styles. We introduce a detailed annotation that extends beyond the original simple bounding boxes to the segmentation masks with pixel-level precision. It provides object category, location, and instance information that can be used for semantic segmentation and instance segmentation. We also provide a comprehensive analysis of our annotation dataset from various aspects. We further measure the improvement of the state-of-the-art segmentation model after training it with our augmented dataset. The benefits of this augmented dataset are profound, with the potential to significantly enhance manga analysis algorithms and catalyze the novel development in digital art processing and cultural analytics. This annotation, named MangaSeg, is publicly available at https://huggingface.co/datasets/MS92/MangaSegmentation.
HyperPose: Hypernetwork-Infused Camera Pose Localization and an Extended Cambridge Landmarks Dataset
Authors: Ron Ferens, Yosi Keller
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: regression

    In this work, we propose HyperPose, which utilizes hypernetworks in absolute camera pose regressors. The inherent appearance variations in natural scenes, attributable to environmental conditions, perspective, and lighting, induce a significant domain disparity between the training and test datasets. This disparity degrades the precision of contemporary localization networks. To mitigate this, we advocate for incorporating hypernetworks into single-scene and multiscene camera pose regression models. During inference, the hypernetwork dynamically computes adaptive weights for the localization regression heads based on the particular input image, effectively narrowing the domain gap. Using indoor and outdoor datasets, we evaluate the HyperPose methodology across multiple established absolute pose regression architectures. In particular, we introduce and share the Extended Cambridge Landmarks (ECL), which is a novel localization dataset, based on the Cambridge Landmarks dataset, showing it in multiple seasons with significantly varying appearance conditions. Our empirical experiments demonstrate that HyperPose yields notable performance enhancements for both single- and multi-scene architectures. We have made our source code, pre-trained models, and ECL dataset openly available.
FlexDrive: Toward Trajectory Flexibility in Driving Scene Gaussian Splatting Reconstruction and Rendering
Authors: Jingqiu Zhou, Lue Fan, Linjiang Huang, Xiaoyu Shi, Si Liu, Zhaoxiang Zhang, Hongsheng Li
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Driving scene reconstruction and rendering have advanced significantly using the 3D Gaussian Splatting.However, most prior research has focused on the rendering quality along a pre-recorded vehicle path and struggles to generalize to out-of-path viewpoints, which is caused by the lack of high-quality supervision in those out-of-path views. To address this issue, we introduce an Inverse View Warping technique to create compact and high-quality images as supervision for the reconstruction of the out-of-path views, enabling high-quality rendering results for those views.For accurate and robust inverse view warping, a depth bootstrap strategy is proposed to obtain on-the-fly dense depth maps during the optimization process, overcoming the sparsity and incompleteness of LiDAR depth data.Our method achieves superior in-path and out-of-path reconstruction and rendering performance on the widely used Waymo Open dataset.In addition, a simulator-based benchmark is proposed to obtain the out-of-path ground truth and quantitatively evaluate the performance of out-of-path rendering, where our method outperforms previous methods by a significant margin.
WildAvatar: Learning In-the-wild 3D Avatars from the Web
Authors: Zihao Huang, Shoukang Hu, Guangcong Wang, Tianqi Liu, Yuhang Zang, Zhiguo Cao, Wei Li, Ziwei Liu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1

    Existing research on avatar creation is typically limited to laboratory datasets, which require high costs against scalability and exhibit insufficient representation of the real world. On the other hand, the web abounds with off-the-shelf real-world human videos, but these videos vary in quality and require accurate annotations for avatar creation. To this end, we propose an automatic annotating pipeline with filtering protocols to curate these humans from the web. Our pipeline surpasses state-of-the-art methods on the EMDB benchmark, and the filtering protocols boost verification metrics on web videos. We then curate WildAvatar, a web-scale in-the-wild human avatar creation dataset extracted from YouTube, with 10,000+ different human subjects and scenes. WildAvatar is at least 10xricher than previous datasets for 3D human avatar creation and closer to the real world. To explore its potential, we demonstrate the quality and generalizability of avatar creation methods on WildAvatar. We will publicly release our code, data source links and annotations to push forward 3D human avatar creation and other related fields for real-world applications.
LLM-driven Multimodal and Multi-Identity Listening Head Generation
Authors: Peiwen Lai, Weizhi Zhong, Yipeng Qin, Xiaohang Ren, Baoyuan Wang, Guanbin Li
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Disentanglement, Response Generation

    Generating natural listener responses in conversational scenarios is crucial for creating engaging digital humans and avatars. Recent work has shown that large language models (LLMs) can be effectively leveraged for this task, demonstrating remarkable capabilities in generating contextually appropriate listener behaviors. However, current LLM-based methods face two critical limitations: they rely solely on speech content, overlooking other crucial communication signals, and they entangle listener identity with response generation, compromising output fidelity and generalization. In this work, we present a novel framework that addresses these limitations while maintaining the advantages of LLMs. Our approach introduces a Multimodal-LM architecture that jointly processes speech content, acoustics, and speaker emotion, capturing the full spectrum of communication cues. Additionally, we propose an identity disentanglement strategy using instance normalization and adaptive instance normalization in a VQ-VAE framework, enabling high-fidelity listening head synthesis with flexible identity control. Extensive experiments demonstrate that our method significantly outperforms existing approaches in terms of response naturalness and fidelity, while enabling effective identity control without retraining.
Vision-Language Embodiment for Monocular Depth Estimation
Authors: Jinchang Zhang, Guoyu Lu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D Reconstruction, Depth Estimation, Monocular Depth Estimation, Scene Understanding

    Depth estimation is a core problem in robotic perception and vision tasks, but 3D reconstruction from a single image presents inherent uncertainties. Current depth estimation models primarily rely on inter-image relationships for supervised training, often overlooking the intrinsic information provided by the camera itself. We propose a method that embodies the camera model and its physical characteristics into a deep learning model, computing embodied scene depth through real-time interactions with road environments. The model can calculate embodied scene depth in real-time based on immediate environmental changes using only the intrinsic properties of the camera, without any additional equipment. By combining embodied scene depth with RGB image features, the model gains a comprehensive perspective on both geometric and visual details. Additionally, we incorporate text descriptions containing environmental content and depth information as priors for scene understanding, enriching the model's perception of objects. This integration of image and language -- two inherently ambiguous modalities -- leverages their complementary strengths for monocular depth estimation. The real-time nature of the embodied language and depth prior model ensures that the model can continuously adjust its perception and behavior in dynamic environments. Experimental results show that the embodied depth estimation method enhances model performance across different scenes.
Satellite to GroundScape - Large-scale Consistent Ground View Generation from Satellite Views
Authors: Ningli Xu, Rongjun Qin
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Denoising, Video Generation

    Generating consistent ground-view images from satellite imagery is challenging, primarily due to the large discrepancies in viewing angles and resolution between satellite and ground-level domains. Previous efforts mainly concentrated on single-view generation, often resulting in inconsistencies across neighboring ground views. In this work, we propose a novel cross-view synthesis approach designed to overcome these challenges by ensuring consistency across ground-view images generated from satellite views. Our method, based on a fixed latent diffusion model, introduces two conditioning modules: satellite-guided denoising, which extracts high-level scene layout to guide the denoising process, and satellite-temporal denoising, which captures camera motion to maintain consistency across multiple generated views. We further contribute a large-scale satellite-ground dataset containing over 100,000 perspective pairs to facilitate extensive ground scene or video generation. Experimental results demonstrate that our approach outperforms existing methods on perceptual and temporal metrics, achieving high photorealism and consistency in multi-view outputs. The project page is at https://gdaosu.github.io/sat2groundscape
Seeing Speech and Sound: Distinguishing and Locating Audio Sources in Visual Scenes
Authors: Hyeonggon Ryu, Seongyu Kim, Joon Son Chung, Arda Senocak
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Cross-Modal Retrieval, Disentanglement, Visual Grounding

    We present a unified model capable of simultaneously grounding both spoken language and non-speech sounds within a visual scene, addressing key limitations in current audio-visual grounding models. Existing approaches are typically limited to handling either speech or non-speech sounds independently, or at best, together but sequentially without mixing. This limitation prevents them from capturing the complexity of real-world audio sources that are often mixed. Our approach introduces a "mix-and-separate" framework with audio-visual alignment objectives that jointly learn correspondence and disentanglement using mixed audio. Through these objectives, our model learns to produce distinct embeddings for each audio type, enabling effective disentanglement and grounding across mixed audio sources.Additionally, we created a new dataset to evaluate simultaneous grounding of mixed audio sources, demonstrating that our model outperforms prior methods. Our approach also achieves state-of-the-art performance in standard segmentation and cross-modal retrieval tasks, highlighting the benefits of our mix-and-separate approach.
Minimizing Labeled, Maximizing Unlabeled: An Image-Driven Approach for Video Instance Segmentation
Authors: Fangyun Wei, Jinjing Zhao, Kun Yan, Chang Xu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Instance Segmentation, Semantic Segmentation, Video Instance Segmentation

    Traditional video instance segmentation (VIS) models rely on extensive per-frame video annotations, which are both time-consuming and costly. In this paper, we present MinMaxVIS, a novel VIS framework that reduces the dependency on fully labeled video datasets by utilizing a small set of labeled images from the target domain along with a large volume of general-domain, unlabeled images. MinMaxVIS operates in three stages: first, a preliminary segmentation model is trained on the small labeled set from the target domain; this model then retrieves relevant instances from the unlabeled dataset to build a high-quality pseudo-labeled set, ensuring a rich content alignment with the target domain while avoiding the inefficiencies of large-scale semi-supervised learning across the entire unlabeled set. Finally, we train MinMaxVIS on a combination of labeled and pseudo-labeled data, addressing challenges such as noise in pseudo-labels and instance association across frames. To simulate object continuity, we augment static images to create paired frames, allowing MinMaxVIS to capture instance associations effectively. MinMaxVIS outperforms the prior image-driven approach, MinVIS, achieving superior mAP scores with significantly reduced labeled data. For instance, MinMaxVIS with a Swin-L backbone attains 62.2 mAP on YouTube-VIS 2019 using only 2% labeled data and additional unlabeled images from SA-1B. This surpasses MinVIS, which uses the same backbone trained on fully labeled YouTube-VIS 2019, by 0.6 mAP.
SDBF: Steep-Decision-Boundary Fingerprinting for Hard-Label Tampering Detection of DNN Models
Authors: Xiaofan Bai, Shixin Li, Xiaojing Ma, Bin Benjamin Zhu, Dongmei Zhang, Linchen Yu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Sensitivity

    Cloud-based AI systems offer significant benefits but also introduce vulnerabilities, making deep neural network (DNN) models susceptible to malicious tampering. This tampering may involve harmful behavior injection or resource reduction, compromising model integrity and performance. To detect model tampering, hard-label fingerprinting techniques generate sensitive samples to probe and reveal tampering. Existing fingerprinting methods are mainly based on gradient-defined sensitivity decision boundary, with the latter showing a manifest superior detection performance. However, all existing fingerprinting methods either suffer from insufficient sensitivity or incur high computational costs. In this paper, we theoretically analyze the black-box co-optimal tampering detection sensitivity of fingerprint samples in the context of decision boundary and gradient-defined sensitivity. Based on this, we further propose Steep-Decision-Boundary Fingerprinting (SDBF), a novel lightweight approach for hard-label tampering detection that inherently and efficiently combines the strengths of existing fingerprinting techniques. SDBF places fingerprint samples near the steep decision boundary, where the outputs of samples are inherently highly sensitive to tampering. We also design a Max Boundary Coverage Strategy (MBCS), which enhances samples'  diversity over the decision boundary. Theoretical analysis and extensive experimental results show that SDBF outperforms existing SOTA hard-label fingerprinting methods in both sensitivity and efficiency.
V^2Dial: Unification of Video and Visual Dialog via Multimodal Experts
Authors: Adnen Abdessaied, Anna Rohrbach, Marcus Rohrbach, Andreas Bulling
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Contrastive Learning, Text Retrieval, Video-Text Retrieval, Visual Dialog, Visual Question Answering (VQA)

    We present V2Dial - a novel expert-based model specifically geared towards simultaneously handling image and video input data for multimodal conversational tasks. Current multimodal models primarily focus on simpler tasks (e.g., VQA, VideoQA, video-text retrieval) and often neglect the more challenging conversational counterparts, such as video and visual/image dialog. Moreover, works on both conversational tasks evolved separately from each other despite their apparent similarities limiting their applicability potential. To this end, we propose to unify both tasks using a single model that for the first time jointly learns the spatial and temporal features of images and videos by routing them through dedicated experts and aligns them using matching and contrastive learning techniques. Furthermore, we systemically study the domain shift between the two tasks by investigating whether and to what extent these seemingly related tasks can mutually benefit from their respective training data. Extensive evaluations on the widely used video and visual dialog datasets of AVSD and VisDial show that our model achieves new state-of-the-art results across four benchmarks both in zero-shot and fine-tuning settings.
Distilled Prompt Learning for Incomplete Multimodal Survival Prediction
Authors: Yingxue Xu, Fengtao Zhou, Chenyu Zhao, Yihui Wang, Can Yang, Hao Chen
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Prediction, Prompt Learning, Survival Prediction

    The integration of multimodal data including pathology images and gene profiles is widely applied in precise survival prediction. Despite recent advances in multimodal survival models, collecting complete modalities for multimodal fusion still poses a significant challenge, hindering their application in clinical settings. Current approaches tackling incomplete modalities often fall short, as they typically compensate for only a limited part of the knowledge of missing modalities. To address this issue, we propose a Distilled Prompt Learning framework (DisPro) to utilize the strong robustness of Large Language Models (LLMs) to missing modalities, which employs two-stage prompting for compensation of comprehensive information for missing modalities. In the first stage, Unimodal Prompting (UniPro) distills the knowledge distribution of each modality, preparing for supplementing modality-specific knowledge of the missing modality in the subsequent stage. In the second stage, Multimodal Prompting (MultiPro) leverages available modalities as prompts for LLMs to infer the missing modality, which provides modality-common information. Simultaneously, the unimodal knowledge acquired in the first stage is injected into multimodal inference to compensate for the modality-specific knowledge of the missing modality. Extensive experiments covering various missing scenarios demonstrated the superiority of the proposed method. The code is available at https://github.com/Innse/DisPro.
Customized Condition Controllable Generation for Video Soundtrack
Authors: Fan Qi, Kunsheng Ma, Changsheng Xu
Date: 2025-01-01 00:00:00
Proceeding: CVPR 2025 1
Tasks: Audio Synthesis

    Recent advances in latent diffusion models (LDMs) have enabled data-driven paradigms for video soundtrack generation, improving multimodal alignment capabilities. However, current two-stage frameworks--which separately optimize audio-visual correspondence and conditional audio synthesis--fundamentally limit joint modeling of dynamic acoustic properties. In this paper, we propose a novel framework for generating video soundtracks that simultaneously produces music and sound effect tailored to the video content. Our method incorporates a Contrastive Visual-Sound-Music pretraining process that maps these modalities into a unified feature space, enhancing the model's ability to capture intricate audio dynamics. We design Spectrum Divergence Masked Attention for Unet to differentiate between the unique characteristics of sound effect and music. We utilize Score-guided Noise Iterative Optimization to provide musicians with customizable control during the generation process. Extensive evaluations on the FilmScoreDB and SymMV&HIMV datasets demonstrate that our approach significantly outperforms state-of-the-art baselines in both subjective and objective assessments, highlighting its potential as a robust tool for video soundtrack generation.