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Text Grouping Adapter: Adapting Pre-Trained Text Detector for Layout Analysis

Text Grouping Adapter: Adapting Pre-Trained Text Detector fo...

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conference on computer vision and pattern recognition (CVPR)

作者： Tianci Bi Xiaoyi Zhang Zhizheng Zhang Wenxuan Xie Cuiling Lan Yan Lu Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Microsoft Research Asia

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Significant progress has been made in scene text detection models since the rise of deep learning, but scene text layout analysis, which aims to group detected text instances as paragraphs, has not kept pace. Previous works either treated text detection and grouping using separate models, or train a model from scratch while using a unified one. All of them have not yet made full use of the already well-trained text detectors and easily obtainable detection datasets. In this paper, we present Text Grouping Adapter (TGA), a module that can enable the utilization of various pretrained text detectors to learn layout analysis, allowing us to adopt a well-trained text detector right off the shelf or just fine-tune it efficiently. Designed to be compatible with various text detector architectures, TGA takes detected text regions and image features as universal inputs to as-semble text instance features. To capture broader contextual information for layout analysis, we propose to predict text group masks from text instance features by one-to-many assignment. Our comprehensive experiments demonstrate that, even with frozen pretrained models, incorporating our TGA into various pretrained text detectors and text spotters can achieve superior layout analysis performance, simultaneously inheriting generalized text detection ability from pretraining. In the case of full parameter fine-tuning, we can further improve layout analysis performance.

关键词： Deep learning Analytical models Adaptation models computer vision Layout Text detection Detectors

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Empowering Resampling Operation for Ultra-High-Definition Image Enhancement with Model-Aware Guidance

Empowering Resampling Operation for Ultra-High-Definition Im...

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conference on computer vision and pattern recognition (CVPR)

作者： Wei Yu Jie Huang Bing Li Kaiwen Zheng Qi Zhu Man Zhou Feng Zhao University of Science and Technology of China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Image enhancement algorithms have made remarkable advancements in recent years, but directly applying them to Ultra-high-definition (UHD) images presents intractable computational overheads. Therefore, previous straightforward solutions employ resampling techniques to reduce the resolution by adopting a “Downsampling-Enhancement-Upsampling” processing paradigm. However, this paradigm disentangles the resampling operators and inner enhancement algorithms, which results in the loss of information that is favored by the model, further leading to sub-optimal outcomes. In this paper, we propose a novel method of Learning Model-Aware Resampling (LMAR), which learns to customize resampling by extracting model-aware information from the UHD input image, under the guidance of model knowledge. Specifically, our method consists of two core designs, namely compensatory kernel estimation and steganographic resampling. At the first stage, we dynamically predict compensatory kernels tailored to the specific input and resampling scales. At the second stage, the image-wise compensatory information is derived with the compensatory kernels and embedded into the rescaled input images. This promotes the representation of the newly derived downscaled inputs to be more consistent with the full-resolution UHD inputs, as perceived by the model. Our LMAR enables model-aware and model-favored resampling while maintaining compatibility with existing resampling operators. Extensive experiments on multiple UHD image enhancement datasets and different backbones have shown consistent performance gains after correlating resizer and enhancer; e.g., up to 1.2dB PSNR gain for ×1.8 resampling scale on UHD-LOL4K. The code is available at https://***/YPatrickW/LMAR.

关键词： Interpolation computer vision Image resolution Scalability Computational modeling Estimation Performance gain

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HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely-Supervised 3D Object Detection

HINTED: Hard Instance Enhanced Detector with Mixed-Density F...

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conference on computer vision and pattern recognition (CVPR)

作者： Qiming Xia Wei Ye Hai Wu Shijia Zhao Leyuan Xing Xun Huang Jinhao Deng Xin Li Chenglu Wen Cheng Wang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University Xiamen China Section of Visual Computing and Interactive Media Texas A&M University Texas USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Current sparsely-supervised object detection methods largely depend on high threshold settings to derive high-quality pseudo labels from detector predictions. However, hard instances within point clouds frequently display incomplete structures, causing decreased confidence scores in their assigned pseudo-labels. Previous methods inevitably result in inadequate positive supervision for these instances. To address this problem, we propose a novel Hard INsTance Enhanced Detector (HINTED), for sparsely-supervised 3D object detection. Firstly, we design a self-boosting teacher (SBT) model to generate more potential pseudo-labels, enhancing the effectiveness of information transfer. Then, we introduce a mixed-density student (MDS) model to concentrate on hard instances during the training phase, thereby improving detection accuracy. Our extensive experiments on the KITTI dataset validate our method's superior performance. Compared with leading sparsely-supervised methods, HINTED significantly improves the detection performance on hard instances, no-tably outperforming fully-supervised methods in detecting challenging categories like cyclists. HINTED also significantly outperforms the state-of-the-art semi-supervised method on challenging categories. The code is available at https://***/xmuqimingxia/HINTED.

关键词： Training Point cloud compression computer vision Three-dimensional displays Codes Accuracy Object detection

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Implicit Event-RGBD Neural SLAM

Implicit Event-RGBD Neural SLAM

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conference on computer vision and pattern recognition (CVPR)

作者： Delin Qu Chi Yan Dong Wang Jie Yin Qizhi Chen Dan Xu Yiting Zhang Bin Zhao Xuelong Li Fudan University Shanghai AI Laboratory Hong Kong University of Sciences and Technology Shanghai Jiao Tong University Northwestern Polytechnical University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Implicit neural SLAM has achieved remarkable progress recently. Nevertheless, existing methods face significant challenges in non-ideal scenarios, such as motion blur or lighting variation, which often leads to issues like convergence failures, localization drifts, and distorted mapping. To address these challenges, we propose EN-SLAM, the first event-RGBD implicit neural SLAM framework, which effectively leverages the high rate and high dynamic range advantages of event data for tracking and mapping. Specif-ically, EN-SLAM proposes a differentiable CRF (Camera Response Function) rendering technique to generate dis-tinct RGB and event camera data via a shared radiance field, which is optimized by learning a unified implicit representation with the captured event and RGBD supervision. Moreover, based on the temporal difference property of events, we propose a temporal aggregating optimization strategy for the event joint tracking and global bundle adjustment, capitalizing on the consecutive difference constraints of events, significantly enhancing tracking accuracy and robustness. Finally, we construct the simulated dataset DEV-Indoors and real captured dataset DEV-Reals containing 6 scenes, 17 sequences with practical motion blur and lighting changes for evaluations. Experimental results show that our method outperforms the SOTA methods in both tracking ATE and mapping ACC with a real-time 17 FPS in various challenging environments. Project page: https://***/EN-SLAM.

关键词： Location awareness Simultaneous localization and mapping Image analysis Tracking Lighting Cameras Rendering (computer graphics)

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Instance-level Expert Knowledge and Aggregate Discriminative Attention for Radiology Report Generation

Instance-level Expert Knowledge and Aggregate Discriminative...

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conference on computer vision and pattern recognition (CVPR)

作者： Shenshen Bu Taiji Li Yuedong Yang Zhiming Dai School of Computer Science and Engineering Sun Yat-sen University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Automatic radiology report generation can provide sub-stantial advantages to clinical physicians by effectively re-ducing their workload and improving efficiency. Despite the promising potential of current methods, challenges persist in effectively extracting and preventing degradation of prominent features, as well as enhancing attention on piv-otal regions. In this paper, we propose an Instance-level Expert Knowledge and Aggregate Discriminative Attention framework (EKAGen 1 1 https://***/hnjzbss/EKAGen) for radiology report generation. We convert expert reports into an embedding space and gener-ate comprehensive representations for each disease, which serve as Preliminary Knowledge Support (PKS). To prevent feature disruption, we select the representations in the em-bedding space with the smallest distances to P KS as Rec-tified Knowledge Support (RKS). Then, EKAGen diagnoses the diseases and retrieves knowledge from RKS, creating Instance-level Expert Knowledge (IEK) for each query image, boosting generation. Additionally, we introduce Ag-gregate Discriminative Attention Map (ADM), which uses weak supervision to create maps of discriminative regions that highlight pivotal regions. For training, we propose a Global Information Self-Distillation (GID) strategy, using an iteratively optimized model to distill global knowledge into EKAGen. Extensive experiments and analyses on IU X-Ray and MIMIC-CXR datasets demonstrate that EKAGen outperforms previous state-of-the-art methods.

关键词： Degradation Training Aggregates Pulmonary diseases MIMICs Medical services Radiology

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Global and Hierarchical Geometry Consistency Priors for Few-Shot NeRFs in Indoor Scenes

Global and Hierarchical Geometry Consistency Priors for Few-...

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conference on computer vision and pattern recognition (CVPR)

作者： Xiaotian Sun Qingshan Xu Xinjie Yang Yu Zang Cheng Wang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University School of Computer Science and Engineering Nanyang Technological University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

It is challenging for Neural Radiance Fields (NeRFs) in the few-shot setting to reconstruct high-quality novel views and depth maps in 360° outward-facing indoor scenes. The captured sparse views for these scenes usually contain large viewpoint variations. This greatly reduces the potential consistency between views, leading NeRFs to degrade a lot in these scenarios. Existing methods usually leverage pre-trained depth prediction models to improve NeRFs. However, these methods cannot guarantee geometry consistency due to the inherent geometry ambiguity in the pretrained models, thus limiting NeRFs' performance. In this work, we present p2 NeRF to capture global and hierarchical geometry consistency priors from pretrained models, thus facilitating few-shot NeRFs in 360° outward-facing indoor scenes. On the one hand, we propose a matching-based geometry warm-up strategy to provide global geometry consistency priors for NeRFs. This effectively avoids the overfitting of early training with sparse inputs. On the other hand, we propose a group depth ranking loss and ray weight mask regularization based on the monocular depth estimation model. This provides hierarchical geometry consistency priors for NeRFs. As a result, our approach can fully leverage the geometry consistency priors from pretrained models and help few-shot NeRFs achieve state-of-the-art performance on two challenging indoor datasets. Our code is released at https://***/XT5un/P2NeRF.

关键词： Geometry Training Limiting Shape Estimation Predictive models Neural radiance field

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GALA: Generating Animatable Layered Assets from a Single Scan

GALA: Generating Animatable Layered Assets from a Single Sca...

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conference on computer vision and pattern recognition (CVPR)

作者： Taeksoo Kim Byungjun Kim Shunsuke Saito Hanbyul Joo Seoul National University Codec Avatars Lab Meta

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present GALA, a framework that takes as input a single-layer clothed 3D human mesh and decomposes it into complete multi-layered 3D assets. The outputs can then be combined with other assets to create novel clothed human avatars with any pose. Existing reconstruction approaches often treat clothed humans as a single-layer of geometry and overlook the inherent compositionality of humans with hairstyles, clothing, and accessories, thereby limiting the utility of the meshes for down-stream applications. Decomposing a single-layer mesh into separate layers is a challenging task because it requires the synthesis of plausible geometry and texture for the severely occluded regions. Moreover, even with successful decomposition, meshes are not normalized in terms of poses and body shapes, failing coherent composition with novel identities and poses. To address these challenges, we propose to leverage the general knowledge of a pretrained 2D diffusion model as geometry and appearance prior for humans and other assets. We first separate the input mesh using the 3D surface segmentation extracted from multi-view 2D segmentations. Then we synthesize the missing geometry of different layers in both posed and canonical spaces using a novel pose-guided Score Distillation Sampling (SDS) loss. Once we complete inpainting high-fidelity 3D geometry, we also apply the same SDS loss to its texture to obtain the complete appearance including the initially occluded regions. Through a series of decomposition steps, we obtain multiple layers of 3D assets in a shared canonical space normalized in terms of poses and human shapes, hence supporting effortless composition to novel identities and reanimation with novel poses. Our experiments demonstrate the effectiveness of our approach for decomposition, canonicalization, and composition tasks compared to existing solutions. 1535

关键词： Geometry computer vision Three-dimensional displays Limiting Shape Avatars Clothing

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Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image

Holo-Relighting: Controllable Volumetric Portrait Relighting...

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conference on computer vision and pattern recognition (CVPR)

作者： Yiqun Mei Yu Zeng He Zhang Zhixin Shu Xuaner Zhang Sai Bi Jianming Zhang HyunJoon Jung Vishal M. Patel Johns Hopkins University Adobe Inc

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

At the core of portrait photography is the search for ideal lighting and viewpoint. The process often requires advanced knowledge in photography and an elaborate studio setup. In this work, we propose Holo-Relighting, a volumetric relighting method that is capable of synthesizing novel viewpoints, and novel lighting from a single image. Holo-Relighting leverages the pretrained 3D GAN (EG3D) to reconstruct geometry and appearance from an input portrait as a set of 3D-aware features. We design a relighting module conditioned on a given lighting to process these features, and predict a relit 3D representation in the form of a tri-plane, which can render to an arbitrary viewpoint through volume rendering. Besides viewpoint and lighting control, Holo-Relighting also takes the head pose as a condition to enable head-pose-dependent lighting effects. With these novel designs, Holo-Relighting can generate complex non-Lambertian lighting effects (e.g., specular highlights and cast shadows) without using any explicit physical lighting priors. We train Holo-Relighting with data captured with a light stage, and propose two data-rendering techniques to improve the data quality for training the volumetric relighting system. Through quantitative and qualitative experiments, we demonstrate Holo-Relighting can achieve state-of-the-arts relighting quality with better photorealism, 3D consistency and controllability.

关键词： Training Photorealism Three-dimensional displays Head Lighting Rendering (computer graphics) Controllability

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Weakly Supervised Monocular 3D Detection with a Single-View Image

Weakly Supervised Monocular 3D Detection with a Single-View ...

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conference on computer vision and pattern recognition (CVPR)

作者： Xueying Jiang Sheng Jin Lewei Lu Xiaoqin Zhang Shijian Lu S-Lab Nanyang Technological University Sensetime Research College of Computer Science and Technology Zhejiang University of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Monocular 3D detection (M3D) aims for precise 3D object localization from a single-view image which usually involves labor-intensive annotation of 3D detection boxes. Weakly supervised M3D has recently been studied to obviate the 3D annotation process by leveraging many existing 2D annotations, but it often requires extra training data such as LiDAR point clouds or multi-view images which greatly degrades its applicability and usability in various applications. We propose SKD-WM3D, a weakly supervised monocular 3D detection framework that exploits depth information to achieve M3D with a single-view image exclusively without any 3D annotations or other training data. One key design in SKD-WM3D is a self-knowledge distillation framework, which transforms image features into 3D-like representations by fusing depth information and effectively mitigates the inherent depth ambiguity in monocular scenarios with little computational overhead in inference. In addition, we design an uncertainty-aware distillation loss and a gradient-targeted transfer modulation strategy which facilitate knowledge acquisition and knowledge transfer, respectively. Extensive experiments show that SKD-WM3D surpasses the state-of-the-art clearly and is even on par with many fully supervised methods.

关键词： Point cloud compression Three-dimensional displays Laser radar Annotations Knowledge acquisition Training data Modulation

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TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding

TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understan...

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conference on computer vision and pattern recognition (CVPR)

作者： Zhihao Zhang Shengcao Cao Yu-Xiong Wang Xi'an Jiaotong University University of Illinois Urbana-Champaign

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding, and motivates multi-modal learning approaches which transfer learned knowledge from data-abundant 2D image and language modalities to 3D shapes. However, even though the image and language representations have been aligned by cross-modal models like CLIP, we find that the image modality fails to contribute as much as the language in existing multi-modal 3D representation learning methods. This is attributed to the domain shift in the 2D images and the distinct focus of each modality. To more effectively leverage both modalities in the pre-training, we introduce TriAdapter Multi-Modal Learning (TAMM) - a novel two-stage learning approach based on three synergistic adapters. First, our CLIP Image Adapter mitigates the domain gap between 3D-rendered images and natural images, by adapting the visual representations of CLIP for synthetic image-text pairs. Subsequently, our Dual Adapters decouple the 3D shape representation space into two complementary sub-spaces: one focusing on visual attributes and the other for semantic understanding, which ensure a more comprehensive and effective multi-modal pre-training. Extensive experiments demonstrate that TAMM consistently enhances 3D representations for a wide range of 3D encoder architectures, pre-training datasets, and downstream tasks. Notably, we boost the zero-shot classification accuracy on Objaverse-LVIS from 46.8% to 50.7%, and improve the 5-way 10-shot linear probing classification accuracy on ModelNet40 from 96.1% to 99.0%. Project page: https://***/tamm-page.

关键词： Representation learning Visualization Solid modeling computer vision Three-dimensional displays Accuracy Shape

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