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Layout-Guided Novel View Synthesis from a Single Indoor Panorama

Layout-Guided Novel View Synthesis from a Single Indoor Pano...

引用

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

作者： Xu, Jiale Zheng, Jia Xu, Yanyu Tang, Rui Gao, Shenghua ShanghaiTech Univ Shanghai Peoples R China Manycore KooLab Hangzhou Peoples R China ASTAR Inst High Performance Comp Singapore Singapore Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9781665445092

Existing view synthesis methods mainly focus on the perspective images and have shown promising results. However, due to the limited field-of-view of the pinhole camera, the performance quickly degrades when large camera movements are adopted. In this paper, we make the first attempt to generate novel views from a single indoor panorama and take the large camera translations into consideration. To tackle this challenging problem, we first use Convolutional Neural Networks (CNNs) to extract the deep features and estimate the depth map from the source-view image. Then, we leverage the room layout prior, a strong structural constraint of the indoor scene, to guide the generation of target views. More concretely, we estimate the room layout in the source view and transform it into the target viewpoint as guidance. Meanwhile, we also constrain the room layout of the generated target-view images to enforce geometric consistency. To validate the effectiveness of our method, we further build a large-scale photo-realistic dataset containing both small and large camera translations. The experimental results on our challenging dataset demonstrate that our method achieves state-of-the-art performance. The project page is at https://***/bluestyle97/PNVS.

关键词： computer vision Layout Transforms Cameras Feature extraction pattern recognition Convolutional neural networks

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Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks 31

Stochastic Downsampling for Cost-Adjustable Inference and Im...

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31st ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Kuen, Jason Kong, Xiangfei Lin, Zhe Wang, Gang Yin, Jianxiong See, Simon Tan, Yap-Peng Nanyang Technol Univ Singapore Singapore Adobe Res San Jose CA USA Alibaba AI Labs Hangzhou Zhejiang Peoples R China NVIDIA Santa Clara CA USA

ISBN: (纸本)9781538664209

It is desirable to train convolutional networks (CNNs) to run more efficiently during inference. In many cases however, the computational budget that the system has for inference cannot be known beforehand during training, or the inference budget is dependent on the changing real-time resource availability. Thus, it is inadequate to train just inference-efficient CNNs, whose inference costs are not adjustable and cannot adapt to varied inference budgets. We propose a novel approach for cost-adjustable inference in CNNs- Stochastic Downsampling Point (SDPoint). During training, SDPoint applies feature map downsampling to a random point in the layer hierarchy, with a random down sampling ratio. The different stochastic downsampling configurations known as SDPoint instances (of the same model) have computational costs different from each other, while being trained to minimize the same prediction loss. Sharing network parameters across different instances provides significant regularization boost. During inference, one may handpick a SDPoint instance that best fits the inference budget. The effectiveness of SDPoint, as both a cost-adjustable inference approach and a regularizes, is validated through extensive experiments on image classification.

关键词： Training Computational modeling Computational efficiency Stochastic processes Visualization Network architecture computer vision

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Facial expression recognition using encoded dynamic features

Facial expression recognition using encoded dynamic features

引用

ieee conference on computer vision and pattern recognition

作者： Yang, Peng Liu, Qingshan Cui, Xinyi Metaxas, Dimitris N. Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA

ISBN: (纸本)9781424422425

In this paper, we propose a novel framework for video-based facial expression recognition, which can handle the data with various time resolution including a single frame. We first use the haar-like features to represent facial appearance, due to their simplicity and effectiveness. Then we perform K-Means clustering on the facial appearance features to explore the intrinsic temporal patterns of each expression. Based on the temporal pattern models, we further map the facial appearance variations into dynamic binary patterns. Finally, boosting learning is performed to construct the expression classifiers. Compared to previous work, the dynamic binary patterns encode the intrinsic dynamics of expression, and our method makes no assumption on the time resolution of the data. Extensive experiments carried on the Cohn-Kanade database show the promising performance of the proposed method.

关键词： Face recognition

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DETReg: Unsupervised Pretraining with Region Priors for Object Detection

DETReg: Unsupervised Pretraining with Region Priors for Obje...

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

作者： Bar, Amir Wang, Xin Kantorov, Vadim Reed, Colorado J. Herzig, Roei Chechik, Gal Rohrbach, Anna Darrell, Trevor Globerson, Amir Tel Aviv Univ Tel Aviv Israel Berkeley AI Res Berkeley CA USA NVIDIA Santa Clara CA USA Bar Ilan Univ Ramat Gan Israel Microsoft Res Redmond WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture. Instead, we introduce DETReg, a new self-supervised method that pretrains the entire object detection network, including the object localization and embedding components. During pretraining, DETReg predicts object localizations to match the localizations from an unsupervised region proposal generator and simultaneously aligns the corresponding feature embeddings with embeddings from a self-supervised image encoder. We implement DETReg using the DETR family of detectors and show that it improves over competitive baselines when finetuned on COCO, PASCAL VOC, and Airbus Ship benchmarks. In low-data regimes, including semi-supervised and few-shot learning settings, DETReg establishes many state-of-the-art results, e.g., on COCO we see a +6.0 AP improvement for 10-shot detection and over 2 AP improvements when training with only 1% of the labels.(1)

关键词： Location awareness Training computer vision Object detection Detectors Transformers Generators

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Audiovisual Event Detection Towards Scene Understanding

Audiovisual Event Detection Towards Scene Understanding

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ieee-computer-Society conference on computer vision and pattern recognition Workshops

作者： Canton-Ferrer, C. Butko, T. Segura, C. Giro, X. Nadeu, C. Hernando, J. Casas, J. R. Tech Univ Catalonia Barcelona Spain

ISBN: (纸本)9781424439942

Acoustic events produced in meeting environments may contain useful information for perceptually aware interfaces and multimodal behavior analysis. In this paper a system to detect and recognize these events from a multimodal perspective is presented combining information from multiple cameras and microphones. First, spectral and temporal features are extracted from a single audio channel and spatial localization is achieved by exploiting cross-correlation among microphone arrays. Second, several video cues obtained from multi-person tracking, motion analysis, face recognition, and object detection provide the visual counterpart of the acoustic events to be detected. A multimodal data fusion at score level is carried out using two approaches: weighted mean average and fuzzy integral. Finally, a multimodal database containing a rich variety of acoustic events has been recorded including manual annotations of the data. A set of metrics allow assessing the performance of the presented algorithms. This dataset is made publicly available for research purposes.

关键词： Face recognition

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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and ...

引用

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

作者： Dai, Yuekun Zhang, Dafeng Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Yang, Peiqing Jin, Zhezhu Liu, Guanqun Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Samsung Res China Nanjing Peoples R China Nankai Univ Tianjin Peoples R China

ISBN: (纸本)9798350365474

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and cvpr 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024.

关键词： computer vision flare removal image restoration

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Neural Fields as Learnable Kernels for 3D Reconstruction

Neural Fields as Learnable Kernels for 3D Reconstruction

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

作者： Williams, Francis Gojcic, Zan Khamis, Sameh Zorin, Denis Bruna, Joan Fidler, Sanja Litany, Or NVIDIA Santa Clara CA 95051 USA NYU New York NY 10003 USA Swiss Fed Inst Technol Zurich Switzerland Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present Neural Kernel Fields: a novel method for reconstructing implicit 3D shapes based on a learned kernel ridge regression. Our technique achieves state-of-the-art results when reconstructing 3D objects and large scenes from sparse oriented points, and can reconstruct shape categories outside the training set with almost no drop in accuracy. The core insight of our approach is that kernel methods are extremely effective for reconstructing shapes when the chosen kernel has an appropriate inductive bias. We thus factor the problem ofshape reconstruction into two parts: (1) a backbone neural network which learns kernel parameters from data, and (2) a kernel ridge regression that fits the input points on -the -fly by solving a simple positive definite linear system using the learned kernel. As a result of this factorization, our reconstruction gains the benefits of data driven methods under sparse point density while maintaining interpolatory behavior, which converges to the ground truth shape as input sampling density increases. Our experiments demonstrate a strong generalization capability to objects outside the train -set category and scanned scenes.

关键词： Training Linear systems computer vision Three-dimensional displays Codes Shape Computational modeling

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BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation

BBAM: Bounding Box Attribution Map for Weakly Supervised Sem...

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

作者： Lee, Jungbeom Yi, Jihun Shin, Chaehun Yoon, Sungroh Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Seoul Natl Univ ISRC INMC ASRI Seoul South Korea Seoul Natl Univ Inst Engn Res Seoul South Korea

ISBN: (纸本)9781665445092

Weakly supervised segmentation methods using bounding box annotations focus on obtaining a pixel-level mask from each box containing an object. Existing methods typically depend on a class-agnostic mask generator, which operates on the low-level information intrinsic to an image. In this work, we utilize higher-level information from the behavior of a trained object detector, by seeking the smallest areas of the image from which the object detector produces almost the same result as it does from the whole image. These areas constitute a bounding-box attribution map (BBAM), which identifies the target object in its bounding box and thus serves as pseudo ground-truth for weakly supervised semantic and instance segmentation. This approach significantly outperforms recent comparable techniques on both the PASCAL VOC and MS COCO benchmarks in weakly supervised semantic and instance segmentation. In addition, we provide a detailed analysis of our method, offering deeper insight into the behavior of the BBAM.

关键词： Image segmentation computer vision Annotations Semantics Detectors Benchmark testing Generators

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T2VLAD: Global-Local Sequence Alignment for Text-Video Retrieval

T2VLAD: Global-Local Sequence Alignment for Text-Video Retri...

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

作者： Wang, Xiaohan Zhu, Linchao Yang, Yi Zhejiang Univ Hangzhou Peoples R China Baidu Res Beijing Peoples R China Univ Technol Sydney ReLER Sydney NSW Australia

ISBN: (纸本)9781665445092

Text-video retrieval is a challenging task that aims to search relevant video contents based on natural language descriptions. The key to this problem is to measure text-video similarities in a joint embedding space. However, most existing methods only consider the global cross-modal similarity and overlook the local details. Some works incorporate the local comparisons through cross-modal local matching and reasoning. These complex operations introduce tremendous computation. In this paper, we design an efficient global-local alignment method. The multi-modal video sequences and text features are adaptively aggregated with a set of shared semantic centers. The local cross-modal similarities are computed between the video feature and text feature within the same center. This design enables the meticulous local comparison and reduces the computational cost of the interaction between each text-video pair. Moreover, a global alignment method is proposed to provide a global cross-modal measurement that is complementary to the local perspective. The global aggregated visual features also provide additional supervision, which is indispensable to the optimization of the learnable semantic centers. We achieve consistent improvements on three standard text-video retrieval benchmarks and outperform the state-of-the-art by a clear margin.

关键词： Visualization Semantics Video sequences Natural languages Benchmark testing Extraterrestrial measurements pattern recognition

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Recurring the Transformer for Video Action recognition

Recurring the Transformer for Video Action Recognition

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

作者： Yang, Jiewen Dong, Xingbo Liu, Liujun Zhang, Chao Shen, Jiajun Yu, Dahai TCL Corp Res HK Co Ltd Hong Kong Peoples R China Yonsei Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing video understanding approaches, such as 3D convolutional neural networks and Transformer-Based methods, usually process the videos in a clip-wise manner;hence huge GPU memory is needed and fixed-length video clips are usually required. To alleviate those issues, we introduce a novel Recurrent vision Transformer (RViT) framework based on spatial-temporal representation learning to achieve the video action recognition task. Specifically, the proposed RViT is equipped with an attention gate to build interaction between current frame input and previous hidden state, thus aggregating the global level interframe features through the hidden state temporally. RViT is executed recurrently to process a video by giving the current frame and previous hidden state. The RViT can capture both spatial and temporal features because of the attention gate and recurrent execution. Besides, the proposed RV1T can work on variant-length video clips properly without requiring large GPU memory thanks to the frame by frame processing flow Our experiment results demonstrate that RV1T can achieve state-of-the-art performance on various datasets for the video recognition task. Specifically, RV1T can achieve a top-I accuracy of 81.5% on Kinetics-400, 92.31% on Jester, 67.9% on Something-Something-V2, and an mAP accuracy of 66.1% on Charades.

关键词： Representation learning Visualization Three-dimensional displays Memory management Video sequences Graphics processing units Logic gates

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