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Artistic Style Novel View Synthesis Based on A Single Image

Artistic Style Novel View Synthesis Based on A Single Image

引用

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

作者： Tseng, Kuan-Wei Lee, Yao-Chih Chen, Chu-Song Natl Taiwan Univ Taipei Taiwan Tokyo Inst Technol Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent progress in 3D display technologies has raised the demand for stylized 3D digital content. Previous approaches either perform style transfer on stereoscopic image pairs or reconstruct 3D environment with multiple view images. In this paper, we propose a novel view stylization framework that can convert a single 2D image into multiple stylized views. It is a two-stage solution that contains view synthesis and neural style transfer. We estimate dense optical flow between the source and novel views so that the style transfer model can produce consistent results. Experimental results show that our method significantly improves the consistency among views compared to the baseline method.

关键词： computer vision Three-dimensional displays Stereo image processing conferences Computational modeling pattern recognition Optical flow

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LEOD: Label-Efficient Object Detection for Event Cameras

LEOD: Label-Efficient Object Detection for Event Cameras

引用

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

作者： Wu, Ziyi Gehrig, Mathias Lyu, Qing Liu, Xudong Gilitschenski, Igor Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada Univ Zurich Zurich Switzerland

ISBN: (纸本)9798350353006

Object detection with event cameras benefits from the sensor's low latency and high dynamic range. However, it is costly to fully label event streams for supervised training due to their high temporal resolution. To reduce this cost, we present LEOD, the first method for label-efficient event-based detection. Our approach unifies weakly- and semi-supervised object detection with a self-training mechanism. We first utilize a detector pre-trained on limited labels to produce pseudo ground truth on unlabeled events. Then, the detector is re-trained with both real and generated labels. Leveraging the temporal consistency of events, we run bi-directional inference and apply tracking-based post-processing to enhance the quality of pseudo labels. To stabilize training against label noise, we further design a soft anchor assignment strategy. We introduce new experimental protocols to evaluate the task of label-efficient event-based detection on Gen1 and 1Mpx datasets. LEOD consistently outperforms supervised baselines across various labeling ratios. For example, on Gen1, it improves mAP by 8.6% and 7.8% for RVT-S trained with 1% and 2% labels. On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels. LEOD maintains its effectiveness even when all labeled data are available, reaching new state-of-the-art results. Finally, we show that our method readily scales to improve larger detectors as well. Code: https://***/Wuziyi616/LEOD.

关键词： Event-based vision Object Detection Semi-supervised Learning Weakly-supervised Learning

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WorDepth: Variational Language Prior for Monocular Depth Estimation

WorDepth: Variational Language Prior for Monocular Depth Est...

引用

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

作者： Zeng, Ziyao Wang, Daniel Yang, Fengyu Park, Hyoungseob Wong, Alex Soatto, Stefano Lao, Dong Yale Univ New Haven CT 06511 USA Univ Calif Los Angeles Los Angeles CA USA

ISBN: (纸本)9798350353006

Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly ill-posed, i.e. spatial arrangements of objects described. We investigate the question of whether two inherently ambiguous modalities can be used in conjunction to produce metric-scaled reconstructions. To test this, we focus on monocular depth estimation, the problem of predicting a dense depth map from a single image, but with an additional text caption describing the scene. To this end, we begin by encoding the text caption as a mean and standard deviation;using a variational framework, we learn the distribution of the plausible metric reconstructions of 3D scenes corresponding to the text captions as a prior. To "select" a specific reconstruction or depth map, we encode the given image through a conditional sampler that samples from the latent space of the variational text encoder, which is then decoded to the output depth map. Our approach is trained alternatingly between the text and image branches: in one optimization step, we predict the mean and standard deviation from the text description and sample from a standard Gaussian, and in the other, we sample using a (image) conditional sampler. Once trained, we directly predict depth from the encoded text using the conditional sampler. We demonstrate our approach on indoor (NYUv2) and out-door (KITTI) scenarios, where we show that language can consistently improve performance in both. Code: https: //***/Adonis-galaxy/WorDepth.

关键词： Monocular Depth Estimation Variational Model vision-Language Model

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Why Object Detectors Fail: Investigating the Influence of the Dataset

Why Object Detectors Fail: Investigating the Influence of th...

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

作者： Miller, Dimity Goode, Georgia Bennie, Callum Moghadam, Peyman Jurdak, Raja Queensland Univ Technol Brisbane Qld Australia CSIRO Data61 Robot & Autonomous Syst Melbourne Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

A false negative in object detection describes an object that was not correctly localised and classified by a detector. In prior work, we introduced five 'false negative mechanisms' that identify the specific component inside the detector architecture that failed to detect the object. Using these mechanisms, we explore how different computer vision datasets and their inherent characteristics can influence object detector failures. Specifically, we investigate the false negative mechanisms of Faster R-CNN and RetinaNet across five computer vision datasets, namely Microsoft COCO, Pascal VOC, ExDark, ObjectNet, and COD10K. Our results show that object size and class influence the false negative mechanisms of object detectors. We also show that comparing the false negative mechanisms of a single object class across different datasets can highlight potentially unknown biases in datasets.

关键词： computer vision conferences Detectors Object detection computer architecture Market research pattern recognition

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Three Pillars improving vision Foundation Model Distillation for Lidar

Three Pillars improving Vision Foundation Model Distillation...

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

作者： Puy, Gilles Gidaris, Spyros Boulch, Alexandre Simeoni, Oriane Sautier, Corentin Perez, Patrick Bursucl, Andrei Marlet, Renaud Valeo ai Paris France Kyutai Paris France Univ Gustave Eiffel CNRS LIGM Ecole Ponts Marne La Vallee France

ISBN: (纸本)9798350353006

Self-supervised image backbones can be used to address complex 2D tasks (e.g., semantic segmentation, object discovery) very efficiently and with little or no downstream supervision. Ideally, 3D backbones for lidar should be able to inherit these properties after distillation of these powerful 2D features. The most recent methods for image-to-lidar distillation on autonomous driving data show promising results, obtained thanks to distillation methods that keep improving. Yet, we still notice a large performance gap when measuring by linear probing the quality of distilled vs fully supervised features. In this work, instead of focusing only on the distillation method, we study the effect of three pillars for distillation: the 3D backbone, the pretrained 2D backbone, and the pretraining 2D+3D dataset. In particular, thanks to our scalable distillation method named ScaLR, we show that scaling the 2D and 3D backbones and pretraining on diverse datasets leads to a substantial improvement of the feature quality. This allows us to significantly reduce the gap between the quality of distilled and fully-supervised 3D features, and to improve the robustness of the pretrained backbones to domain gaps and perturbations. The code is available at https://***/valeoai/ScaLR.

关键词： Semantic Segmentation

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Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Multi-Scale Aggregation and Anthropic Prior Knowledge

Teeth-SEG: An Efficient Instance Segmentation Framework for ...

引用

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

作者： Zou, Bo Wang, Shaofeng Liu, Hao Sun, Gaoyue Wang, Yajie Zuo, FeiFei Quan, Chengbin Zhaot, Youjian Tsinghua Univ Beijing Peoples R China Capital Med Univ Beijing Peoples R China Imperial Coll London London England LargeV Inc Beijing Peoples R China Tsinghua Univ Zhongguancun Lab Beijing Peoples R China

ISBN: (纸本)9798350353006

Teeth localization, segmentation, and labeling in 2D images have great potential in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, general instance segmentation frameworks are incompetent due to 1) the subtle differences between some teeth' shapes ( e.g., maxillary first premolar and second premolar), 2) the teeth's position and shape variation across subjects, and 3) the presence of abnormalities in the dentition (e.g., caries and edentulism). To address these problems, we propose a ViT-based framework named TeethSEG, which consists of stacked Multi-Scale Aggregation (MSA) blocks and an Anthropic Prior Knowledge (APK) layer. Specifically, to compose the two modules, we design a unique permutation-based upscaler to ensure high efficiency while establishing clear segmentation boundaries with multi-head self/cross-gating layers to emphasize particular semantics meanwhile maintaining the divergence between token embeddings. Besides, we collect the first open-sourced intraoral image dataset IO150K, which comprises over 150k intraoral photos, and all photos are annotated by orthodontists using a human-machine hybrid algorithm. Experiments on IO150K demonstrate that our TeethSEG outperforms the state-of-the-art segmentation models on dental image segmentation.

关键词： Image Segmentation Intra-Oral Image Medical vision Teeth Segmentation

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PAND: Precise Action recognition on Naturalistic Driving

PAND: Precise Action Recognition on Naturalistic Driving

引用

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

作者： Zhao, Hangyue Xiao, Yuchao Zhao, Yanyun Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Temporal action localization for untrimmed videos is a difficult problem in computer vision. It is challenge to infer the start and end of activity instances on small-scale datasets covering multi-view information accurately. In this paper, we propose an effective activity temporal localization and classification method to localize the temporal boundaries and predict the class label of activities for naturalistic driving. Our approach includes (i) a distraction behavior recognition and localization method in naturalistic driving videos on small-scale data sets, (ii) a strategy that uses multi-branch network to make full use of information from different channels, (iii)a post-processing method for selecting and correcting temporal range to ensure that our system finds accurate boundaries. In addition, the frame-level object detection information is also utilized. Extensive experiments prove the effectiveness of our method and we rank the 6th on the Test-A2 of the 6th AI City Challenge track 3.

关键词： Location awareness computer vision Urban areas Pipelines Object detection Data models pattern recognition

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OpenEQA: Embodied Question Answering in the Era of Foundation Models

OpenEQA: Embodied Question Answering in the Era of Foundatio...

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

作者： Majumdar, Arjun Ajay, Anurag Zhang, Xi Aohan Punya, Pranav Yenamandra, Sriram Henaff, Mikael Silwal, Sneha Mcvay, Paul Maksymets, Oleksandr Arnaud, Sergio Yadav, Karmesh Li, Qiyang Newman, Ben Sharma, Mohit Berges, Vincent Zhang, Shiqi Agrawal, Pulkit Bisk, Yonatan Batra, Dhruv Kalakrishnan, Mrinal Meier, Franziska Paxton, Chris Sax, Alexander Rajeswaran, Aravind Georgia Tech Atlanta GA 30332 USA MIT 77 Massachusetts Ave Cambridge MA 02139 USA SUNY Binghamton Binghamton NY USA Meta AI Menlo Pk CA USA Univ Calif Berkeley Berkeley CA USA CMU Pittsburgh PA USA Meta Fundamental AI Res FAIR Menlo Pk CA USA

ISBN: (纸本)9798350353006

We present a modern formulation of Embodied Question Answering (EQA) as the task of understanding an environment well enough to answer questions about it in natural language. An agent can achieve such an understanding by either drawing upon episodic memory, exemplified by agents on smart glasses, or by actively exploring the environment, as in the case of mobile robots. We accompany our formulation with OpenEQA - the first open-vocabulary benchmark dataset for EQA supporting both episodic memory and active exploration use cases. OpenEQA contains over 1600 high-quality human generated questions drawn from over 180 real-world environments. In addition to the dataset, we also provide an automatic LLM-powered evaluation protocol that has excellent correlation with human judgement. Using this dataset and evaluation protocol, we evaluate several state-of-the-art foundation models including GPT-4V, and find that they significantly lag behind human-level performance. Consequently, OpenEQA stands out as a straightforward, measurable, and practically relevant benchmark that poses a considerable challenge to current generation of foundation models. We hope this inspires and stimulates future research at the intersection of Embodied AI, conversational agents, and world models.

关键词： Embodied AI Embodied Question Answering vision-Language Models

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Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers

Panda-70M: Captioning 70M Videos with Multiple Cross-Modalit...

引用

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

作者： Chen, Tsai-Shien Siarohin, Aliaksandr Menapace, Willi Deyneka, Ekaterina Chao, Hsiang-wei Jeon, Byung Eun Fang, Yuwei Lee, Hsin-Ying Ren, Jian Yang, Ming-Hsuan Tulyakov, Sergey Snap Inc Santa Monica CA 90405 USA Univ Calif Merced Merced CA 95343 USA Univ Trento Trento Italy Snap Santa Monica CA USA

ISBN: (纸本)9798350353006

The quality of the data and annotation upper-bounds the quality of a downstream model. While there exist large text corpora and image-text pairs, high-quality video-text data is much harder to collect. First of all, manual labeling is more time-consuming, as it requires an annotator to watch an entire video. Second, videos have a temporal dimension, consisting of several scenes stacked together, and showing multiple actions. Accordingly, to establish a video dataset with high- quality captions, we propose an automatic approach leveraging multimodal inputs, such as textual video description, subtitles, and individual video frames. Specifically, we curate 3.8M high-resolution videos from the publicly available HD-VILA-100M dataset. We then split them into semantically consistent video clips, and apply multiple cross-modality teacher models to obtain captions for each video. Next, we finetune a retrieval model on a small subset where the best caption of each video is manually selected and then employ the model in the whole dataset to select the best caption as the annotation. In this way, we get 70M videos paired with high-quality text captions. We dub the dataset as Panda-70M. We show the value of the proposed dataset on three downstream tasks: video captioning, video and text retrieval, and text-driven video generation. The models trained on the proposed data score substantially better on the majority of metrics across all the tasks.

关键词： Multimodal learning Video captioning vision-language dataset

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Towards efficient feature sharing in MIMO architectures

Towards efficient feature sharing in MIMO architectures

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

作者： Sun, Remy Rame, Alexandre Masson, Clement Thome, Nicolas Cord, Matthieu Sorbonne Univ MLIA ISIR Paris France Conservatoire Natl Arts & Metiers CEDRIC Vertigo Paris France Thales Land & Air Syst Elancourt France Valeo Ai Paris France

ISBN: (纸本)9781665487399

Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, these architectures are wasteful in their use of parameters. Indeed, we highlight in this paper that the learned subnetwork fail to share even generic features which limits their applicability on smaller mobile and AR/VR devices. We posit this behavior stems from an ill-posed part of the multi-input multi-output framework. To solve this issue, we propose a novel unmixing step in MIMO architectures that allows subnetworks to properly share features. Preliminary experiments on CIFAR 100 show our adjustments allow feature sharing and improve model performance for small architectures.

关键词： Performance evaluation computer vision Impedance matching conferences Neural networks computer architecture pattern recognition

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