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检索条件"任意字段=2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020"

共 11281 条记录，以下是71-80 订阅

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Honeybee: Locality-enhanced Projector for Multimodal LLM

Honeybee: Locality-enhanced Projector for Multimodal LLM

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Cha, Junbum Kang, Wooyoung Mun, Jonghwan Roh, Byungseok Kakao Brain Seongnam South Korea

ISBN: (纸本)9798350353006

In Multimodal Large Language Models (MLLMs), a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs, enabling profound visual understanding while harnessing the LLMs' robust capabilities. Despite the importance of the visual projector, it has been relatively less explored. In this study, we first identify two essential projector properties: (i) flexibility in managing the number of visual tokens, crucial for MLLMs' overall efficiency, and (ii) preservation of local context from visual features, vital for spatial understanding. Based on these findings, we propose a novel projector design that is both flexible and locality-enhanced, effectively satisfying the two desirable properties. Additionally, we present comprehensive strategies to effectively utilize multiple and multifaceted instruction datasets. Through extensive experiments, we examine the impact of individual design choices. Finally, our proposed MLLM, Honeybee, remarkably outperforms previous state-of-the-art methods across various benchmarks, including MME, MMBench, SEED-Bench, and LLaVA-Bench, achieving significantly higher efficiency. Code and models are available at https://github. com/kakaobrain/honeybee.

关键词： Multimodal LLM vision-Language

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Accurate Training Data for Occupancy Map Prediction in Automated Driving Using Evidence Theory

Accurate Training Data for Occupancy Map Prediction in Autom...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kaelble, Jonas Wirges, Sascha Tatarchenko, Maxim Ilg, Eddy Bosch Ctr Artificial Intelligence Renningen Germany Saarland Univ Saarbrucken Germany

ISBN: (纸本)9798350353013;9798350353006

Automated driving fundamentally requires knowledge about the surrounding geometry of the scene. Modern approaches use only captured images to predict occupancy maps that represent the geometry. Training these approaches requires accurate data that may be acquired with the help of LiDAR scanners. We show that the techniques used for current benchmarks and training datasets to convert LiDAR scans into occupancy grid maps yield very low quality, and subsequently present a novel approach using evidence theory that yields more accurate reconstructions. We demonstrate that these are superior by a large margin, both qualitatively and quantitatively, and that we additionally obtain meaningful uncertainty estimates. When converting the occupancy maps back to depth estimates and comparing them with the raw LiDAR measurements, our method yields a MAE improvement of 30% to 52% on nuScenes and 53% on Waymo over other occupancy ground-truth data. Finally, we use the improved occupancy maps to train a state-of-the-art occupancy prediction method and demonstrate that it improves the MAE by 25% on nuScenes.

关键词： Automated Driving computer vision Occupancy Prediction

来源：评论

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A Theory of Joint Light and Heat Transport for Lambertian Scenes

A Theory of Joint Light and Heat Transport for Lambertian Sc...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ramanagopal, Mani Narayanan, Sriram Sankaranarayanan, Aswin C. Narasimhan, Srinivasa G. Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350353006

We present a novel theory that establishes the relationship between light transport in visible and thermal infrared, and heat transport in solids. We show that heat generated due to light absorption can be estimated by modeling heat transport using a thermal camera. For situations where heat conduction is negligible, we analytically solve the heat transport equation to derive a simple expression relating the change in thermal image intensity to the absorbed light intensity and heat capacity of the material. Next, we prove that intrinsic image decomposition for Lambertian scenes becomes a well-posed problem if one has access to the absorbed light. Our theory generalizes to arbitrary shapes and unstructured illumination. Our theory is based on applying energy conservation principle at each pixel independently. We validate our theory using real-world experiments on diffuse objects made of different materials that exhibit both direct and global components (inter-reflections) of light transport under unknown complex lighting.

关键词： heat transport intrinsic image decomposition light transport physics-based vision

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Making vision Transformers Truly Shift-Equivariant

Making Vision Transformers Truly Shift-Equivariant

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Rojas-Gomez, Renan A. Lim, Teck-Yian Do, Minh N. Yeh, Raymond A. UIUC Dept Elect Engn Urbana IL 61801 USA UIUC VinUni Illinois Smart Hlth Ctr Urbana IL USA Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)9798350353013;9798350353006

In the field of computer vision, vision Transformers (ViTs) have emerged as a prominent deep learning architecture. Despite being inspired by Convolutional Neural Networks (CNNs), ViTs are susceptible to small spatial shifts in the input data - they lack shift-equivariance. To address this shortcoming, we introduce novel data-adaptive designs for each of the ViT modules that break shift-equivariance, such as tokenization, self-attention, patch merging, and positional encoding. With our proposed modules, we achieve perfect circular shift-equivariance across four prominent ViT architectures: Swin, SwinV2, CvT, and MViTv2. Additionally, we leverage our design to further enhance consistency under standard shifts. We evaluate our adaptive ViT models on image classification and semantic segmentation tasks. Our models achieve competitive performance across three diverse datasets, showcasing perfect (100%) circular shift consistency while improving standard shift consistency.(1)

关键词： shift equivariance shift invariance vision transformers

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SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection

SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object D...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Mingxuan Hayes, Tyler L. Ricci, Elisa Csurka, Gabriela Volpi, Riccardo Univ Trento Trento Italy NAVER LABS Europe Meylan France Fdn Bruno Kessler Trento Italy

ISBN: (纸本)9798350353006

Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference. However, our initial investigation indicates that existing OvOD detectors exhibit significant variability when dealing with vocabularies across various semantic granularities, posing a concern for real-world deployment. To this end, we introduce Semantic Hierarchy Nexus (SHiNe), a novel classifier that uses semantic knowledge from class hierarchies. It runs offline in three steps: i) it retrieves relevant super-/sub-categories from a hierarchy for each target class;ii) it integrates these categories into hierarchy-aware sentences;iii) it fuses these sentence embeddings to generate the nexus classifier vector. Our evaluation on various detection benchmarks demonstrates that SHiNe enhances robustness across diverse vocabulary granularities, achieving up to +31.9% mAP50 with ground truth hierarchies, while retaining improvements using hierarchies generated by large language models. Moreover, when applied to open-vocabulary classification on ImageNet-1k, SHiNe improves the CLIP zero-shot baseline by +2.8% accuracy. SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector, without incurring additional computational overhead during inference. The code is open source.

关键词： Object Detection Open-vocabulary vision-Language

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DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes

DrivingGaussian: Composite Gaussian Splatting for Surroundin...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhou, Xiaoyu Lin, Zhiwei Shan, Xiaojun Wang, Yongtao Sun, Deqing Yang, Ming-Hsuan Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China Google Res Mountain View CA USA Univ Calif Merced Merced CA USA

ISBN: (纸本)9798350353006

We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects, we first sequentially and progressively model the static background of the entire scene with incremental static 3D Gaussians. We then leverage a composite dynamic Gaussian graph to handle multiple moving objects, individually reconstructing each object and restoring their accurate positions and occlusion relationships within the scene. We further use a LiDAR prior for Gaussian Splatting to reconstruct scenes with greater details and maintain panoramic consistency. DrivingGaussian outperforms existing methods in dynamic driving scene reconstruction and enables photorealistic surround-view synthesis with high-fidelity and multi-camera consistency. Our project page is at: https://***/VDIGPKU/DrivingGaussian.

关键词： 3D Reconstruction 3D vision Autonomous Driving

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Cinematic Behavior Transfer via NeRF-based Differentiable Filming

Cinematic Behavior Transfer via NeRF-based Differentiable Fi...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Jiang, Xuekun Rao, Anyi Wang, Jingbo Lin, Dahua Dai, Bo Shanghai AI Lab Shanghai Peoples R China Stanford Univ Stanford CA 94305 USA Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350353013;9798350353006

In the evolving landscape of digital media and video production, the precise manipulation and reproduction of visual elements like camera movements and character actions are highly desired. Existing SLAM methods face limitations in dynamic scenes and human pose estimation often focuses on 2D projections, neglecting 3D statuses. To address these issues, we first introduce a reverse filming behavior estimation technique. It optimizes camera trajectories by leveraging NeRF as a differentiable renderer and refining SMPL tracks. We then introduce a cinematic transfer pipeline that is able to transfer various shot types to a new 2D video or a 3D virtual environment. The incorporation of 3D engine workflow enables superior rendering and control abilities, which also achieves a higher rating in the user study.

关键词： Three dimensional computer graphics

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View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network

View-decoupled Transformer for Person Re-identification unde...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Quan Wang, Lei Patel, Vishal M. Xie, Xiaohua Lai, Jianhuang Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Pazhou Lab HuangPu Guangzhou Guangdong Peoples R China Guangdong Prov Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Beijing Peoples R China Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD USA

ISBN: (纸本)9798350353006

Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras, such as ground-ground matching. However, as a more practical scenario, aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID, the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features, namely hierarchical subtractive separation and orthogonal loss, where the former separates these two features inside the VDT, and the latter constrains these two to be independent. In addition, we contribute a large-scale AGPReID dataset called CARGO, consisting of five/eight aerial/ground cameras, 5,000 identities, and 108,563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID, surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID, keeping the same magnitude of computational complexity. Our project is available at https://***/LinlyAC/VDT-AGPReID.

关键词： image recognition person re-identification transformer

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GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D Scene Understanding

GSNeRF: Generalizable Semantic Neural Radiance Fields with E...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chou, Zi-Ting Huang, Sheng-Yu Liu, I-Jieh Wang, Yu-Chiang Frank Natl Taiwan Univ Grad Inst Commun Engn Taipei Taiwan NVIDIA Taipei Taiwan

ISBN: (纸本)9798350353006

Utilizing multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a popular research topic in 3D vision. In this work, we introduce a Generalizable Semantic Neural Radiance Fields ( GSNeRF), which uniquely takes image semantics into the synthesis process so that both novel view image and the associated semantic maps can be produced for unseen scenes. Our GSNeRF is composed of two stages: Semantic GeoReasoning and Depth-Guided Visual rendering. The former is able to observe multi- view image inputs to extract semantic and geometry features from a scene. Guided by the resulting image geometry information, the latter performs both image and semantic rendering with improved performances. Our experiments not only confirm that GSNeRF performs favorably against prior works on both novel-view image and semantic segmentation synthesis but the effectiveness of our sampling strategy for visual rendering is further verified.

关键词： 3D computer vision generalizable nerf NeRF segmentation

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Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching

Hybrid Functional Maps for Crease-Aware Non-Isometric Shape ...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Bastian, Lennart Xie, Yizheng Navab, Nassir Laehner, Zorah Tech Univ Munich Munich Germany Univ Siegen Siegen Germany Univ Bonn Bonn Germany Lamarr Inst Bonn Germany

ISBN: (纸本)9798350353013;9798350353006

Non-isometric shape correspondence remains a fundamental challenge in computer vision. Traditional methods using Laplace-Beltrami operator (LBO) eigenmodes face limitations in characterizing high-frequency extrinsic shape changes like bending and creases. We propose a novel approach of combining the non-orthogonal extrinsic basis of eigenfunctions of the elastic thin-shell hessian with the intrinsic ones of the LBO, creating a hybrid spectral space in which we construct functional maps. To this end, we present a theoretical framework to effectively integrate non-orthogonal basis functions into descriptor- and learning-based functional map methods. Our approach can be incorporated easily into existing functional map pipelines across varying applications and can handle complex deformations beyond isometries. We show extensive evaluations across various supervised and unsupervised settings and demonstrate significant improvements. Notably, our approach achieves up to 15% better mean geodesic error for non-isometric correspondence settings and up to 45% improvement in scenarios with topological noise. Code is available at: https://***/

关键词： computer vision Functional Maps Non-isometric Shape Correspondence Shape Matching Topological Noise

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