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Visually Informed Binaural Audio Generation without Binaural Audios

Visually Informed Binaural Audio Generation without Binaural...

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

作者： Xu, Xudong Zhou, Hang Liu, Ziwei Dai, Bo Wang, Xiaogang Lin, Dahua Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Nanyang Technol Univ S Lab Singapore Singapore

ISBN: (纸本)9781665445092

Stereophonic audio, especially binaural audio, plays an essential role in immersive viewing environments. Recent research has explored generating visually guided stereophonic audios supervised by multi-channel audio collections. However, due to the requirement of professional recording devices, existing datasets are limited in scale and variety, which impedes the generalization of supervised methods in real-world scenarios. In this work, we propose PseudoBinaural, an effective pipeline that is free of binaural recordings. The key insight is to carefully build pseudo visual-stereo pairs with mono data for training. Specifically, we leverage spherical harmonic decomposition and head-related impulse response (HRIR) to identify the relationship between spatial locations and received binaural audios. Then in the visual modality, corresponding visual cues of the mono data are manually placed at sound source positions to form the pairs. Compared to fully-supervised paradigms, our binaural-recording-free pipeline shows great stability in cross-dataset evaluation and achieves comparable performance under subjective preference. Moreover, combined with binaural recordings, our method is able to further boost the performance of binaural audio generation under supervised settings(1).

关键词： Training Visualization computer vision Computational modeling Pipelines Audio recording Harmonic analysis

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Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline

Towards Fast and Accurate Real-World Depth Super-Resolution:...

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

作者： He, Lingzhi Zhu, Hongguang Li, Feng Bai, Huihui Cong, Runmin Zhang, Chunjie Lin, Chunyu Liu, Meiqin Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Beijing 100044 Peoples R China

ISBN: (纸本)9781665445092

Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR) is a practical and valuable task, which upscales the depth map into high-resolution (HR) space. However, limited by the lack of real-world paired low-resolution (LR) and HR depth maps, most existing methods use downsampling to obtain paired training samples. To this end, we first construct a large-scale dataset named "RGB-D-D", which can greatly promote the study of depth map SR and even more depth-related real-world tasks. The "D-D" in our dataset represents the paired LR and HR depth maps captured from mobile phone and Lucid Helios respectively ranging from indoor scenes to challenging outdoor scenes. Besides, we provide a fast depth map super-resolution (FDSR) baseline, in which the high-frequency component adaptively decomposed from RGB image to guide the depth map SR. Extensive experiments on existing public datasets demonstrate the effectiveness and efficiency of our network compared with the state-of-the-art methods. Moreover, for the real-world LR depth maps, our algorithm can produce more accurate HR depth maps with clearer boundaries and to some extent correct the depth value errors.

关键词： Training computer vision Superresolution Focusing Estimation Benchmark testing Mobile handsets

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DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation

DCNAS: Densely Connected Neural Architecture Search for Sema...

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

作者： Zhang, Xiong Xu, Hongmin Mo, Hong Tan, Jianchao Yang, Cheng Wang, Lei Ren, Wenqi Joyy Inc Singapore Singapore Kwai Inc AI Platform Bengaluru India Beihang Univ State Key Lab VR Beijing Peoples R China Chinese Acad Sci IIE SKLOIS Beijing Peoples R China

ISBN: (纸本)9781665445092

Existing NAS methods for dense image prediction tasks usually compromise on restricted search space or search on proxy task to meet the achievable computational demands. To allow as wide as possible network architectures and avoid the gap between realistic and proxy setting, we propose a novel Densely Connected NAS (DCNAS) framework, which directly searches the optimal network structures for the multi-scale representations of visual information, over a large-scale target dataset without proxy. Specifically, by connecting cells with each other using learnable weights, we introduce a densely connected search space to cover an abundance of mainstream network designs. Moreover, by combining both path-level and channel-level sampling strategies, we design a fusion module and mixture layer to reduce the memory consumption of ample search space, hence favoring the proxyless searching. Compared with contemporary works, experiments reveal that the proxyless searching scheme is capable of bridging the gap between searching and training environments. Further, DCNAS achieves new state-of-the-art performances on public semantic image segmentation benchmarks, including 84.3% on Cityscapes, and 86.9% on PASCAL VOC 2012. We also retain leading performances when evaluating the architecture on the more challenging ADE20K and PASCAL-Context dataset.

关键词： Training Image segmentation Visualization computer vision Semantics Memory management Network architecture

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Back-tracing Representative Points for Voting-based 3D Object Detection in Point Clouds

Back-tracing Representative Points for Voting-based 3D Objec...

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

作者： Cheng, Bowen Sheng, Lu Shi, Shaoshuai Yang, Ming Xu, Dong Beihang Univ Coll Software Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

3D object detection in point clouds is a challenging vision task that benefits various applications for understanding the 3D visual world. Lots of recent research focuses on how to exploit end-to-end trainable Hough voting for generating object proposals. However, the current voting strategy can only receive partial votes from the surfaces of potential objects together with severe outlier votes from the cluttered backgrounds, which hampers full utilization of the information from the input point clouds. Inspired by the back-tracing strategy in the conventional Hough voting methods, in this work, we introduce a new 3D object detection method, named as Back-tracing Representative Points Network (BRNet), which generatively back-traces the representative points from the vote centers and also revisits complementary seed points around these generated points, so as to better capture the fine local structural features surrounding the potential objects from the raw point clouds. Therefore, this bottom-up and then top-down strategy in our BRNet enforces mutual consistency between the predicted vote centers and the raw surface points and thus achieves more reliable and flexible object localization and class prediction results. Our BRNet is simple but effective, which significantly outperforms the state-of-the-art methods on two large-scale point cloud datasets, ScanNet V2 (+7.5% in terms of mAP@0.50) and SUN RGB-D (+4.7% in terms of mAP@0.50), while it is still lightweight and efficient.

关键词： Location awareness Visualization Three-dimensional displays Object detection Feature extraction pattern recognition Proposals

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Model-Aware Gesture-to-Gesture Translation

Model-Aware Gesture-to-Gesture Translation

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

作者： Hu, Hezhen Wang, Weilun Zhou, Wengang Zhao, Weichao Li, Houqiang Univ Sci & Technol China USTC EEIS Dept CAS Key Lab GIPAS Hefei Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei Peoples R China

ISBN: (纸本)9781665445092

Hand gesture-to-gesture translation is a significant and interesting problem, which serves as a key role in many applications, such as sign language production. This task involves fine-grained structure understanding of the mapping between the source and target gestures. Current works follow a data-driven paradigm based on sparse 2D joint representation. However, given the insufficient representation capability of 2D joints, this paradigm easily leads to blurry generation results with incorrect structure. In this paper, we propose a novel model-aware gesture-to-gesture translation framework, which introduces hand prior with hand meshes as the intermediate representation. To take full advantage of the structured hand model, we first build a dense topology map aligning the image plane with the encoded embedding of the visible hand mesh. Then, a transformation flow is calculated based on the correspondence of the source and target topology map. During the generation stage, we inject the topology information into generation streams by modulating the activations in a spatially-adaptive manner. Further, we incorporate the source local characteristic to enhance the translated gesture image according to the transformation flow. Extensive experiments on two benchmark datasets have demonstrated that our method achieves new state-of-the-art performance.

关键词： Measurement computer vision Production Gesture recognition Benchmark testing Streaming media Assistive technologies

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Boosting Video Representation Learning with Multi-Faceted Integration

Boosting Video Representation Learning with Multi-Faceted In...

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

作者： Qiu, Zhaofan Yao, Ting Ngo, Chong-Wah Zhang, Xiao-Ping Wu, Dong Mei, Tao JD AI Res Beijing Peoples R China Singapore Management Univ Singapore Singapore Ryerson Univ Toronto ON Canada

ISBN: (纸本)9781665445092

Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in the video representation that biases to only one facet depending on the training dataset. There is no study yet on how to learn a video representation from multifaceted labels, and whether multifaceted information is helpful for video representation learning. In this paper, we propose a new learning framework, MUlti-Faceted Integration (MUFI), to aggregate facets from different datasets for learning a representation that could reflect the full spectrum of video content. Technically, MUFI formulates the problem as visual-semantic embedding learning, which explicitly maps video representation into a rich semantic embedding space, and jointly optimizes video representation from two perspectives. One is to capitalize on the intra-facet supervision between each video and its own label descriptions, and the second predicts the "semantic representation" of each video from the facets of other datasets as the inter-facet supervision. Extensive experiments demonstrate that learning 3D CNN via our MUFI framework on a union of four large-scale video datasets plus two image datasets leads to superior capability of video representation. The prelearnt 3D CNN with MUFI also shows clear improvements over other approaches on several downstream video applications. More remarkably, MUFI achieves 98.1%/80.9% on UCF101/HMDB51 for action recognition and 101.5% in terms of CIDEr-D score on MSVD for video captioning.

关键词： Training computer vision Three-dimensional displays Aggregates Semantics Feature extraction Boosting

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Learning to Filter: Siamese Relation Network for Robust Tracking

Learning to Filter: Siamese Relation Network for Robust Trac...

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

作者： Cheng, Siyuan Zhong, Bineng Li, Guorong Liu, Xin Tang, Zhenjun Li, Xianxian Wang, Jing Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Huaqiao Univ Dept Comp Sci & Technol Xiamen Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China Seetatech Technol Beijing Peoples R China

ISBN: (纸本)9781665445092

Despite the great success of Siamese-based trackers, their performance under complicated scenarios is still not satisfying, especially when there are distractors. To this end, we propose a novel Siamese relation network, which introduces two efficient modules, i.e. Relation Detector (RD) and Refinement Module (RM). RD performs in a meta-learning way to obtain a learning ability to filter the distractors from the background while RM aims to effectively integrate the proposed RD into the Siamese framework to generate accurate tracking result. Moreover, to further improve the discriminability and robustness of the tracker, we introduce a contrastive training strategy that attempts not only to learn matching the same target but also to learn how to distinguish the different objects. Therefore, our tracker can achieve accurate tracking results when facing background clutters, fast motion, and occlusion. Experimental results on five popular benchmarks, including VOT2018, VOT2019, OTB100, LaSOT, and UAV123, show that the proposed method is effective and can achieve stateof-the-art results.

关键词： Training Matched filters computer vision Target tracking Detectors Benchmark testing Gain measurement

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Detecting Omissions in Geographic Maps through computer vision 7

Detecting Omissions in Geographic Maps through Computer Visi...

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7th International conference on Multimedia Analysis and pattern recognition, MAPR 2024

作者： Nguyen, Phuc Do, Anh Hoai, Minh VinAI Research Viet Nam Ministry of Information and Communications Viet Nam

ISBN: (纸本)9798350368437

This paper explores the application of computer vision technologies to the analysis of maps, an area with substantial historical, cultural, and political significance. Our focus is on developing and evaluating a method for automatically identifying maps that depict specific regions and feature landmarks with designated names, a task that involves complex challenges due to the diverse styles and methods used in map creation. We address three main subtasks: differentiating maps from non-maps, verifying the accuracy of the region depicted, and confirming the presence or absence of particular landmark names through advanced text recognition techniques. Our approach utilizes a Convolutional Neural Network and transfer learning to differentiate maps from non-maps, verify the accuracy of depicted regions, and confirm landmark names through advanced text recognition. We also introduce the VinMap dataset, containing annotated map images of Vietnam, to train and test our method. Experiments on this dataset demonstrate that our technique achieves F1-score of 85.51% for identifying maps excluding specific territorial landmarks. This result suggests practical utility and indicates areas for future improvement. https://***/VinAIResearch/VinMap © 2024 ieee.

关键词： Convolutional neural networks

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EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation

EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupe...

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

作者： Jiao, Yang Tran, Trac D. Shi, Guangming Xidian Univ Xian Peoples R China Johns Hopkins Univ Baltimore MD 21218 USA Xidian Guangzhou Inst Technol Guangzhou Peoples R China

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

This paper addresses the challenging unsupervised scene flow estimation problem by jointly learning four low-level vision sub-tasks: optical flow F, stereo-depth D, camera pose P and motion segmentation S. Our key insight is that the rigidity of the scene shares the same inherent geometrical structure with object movements and scene depth. Hence, rigidity from S can be inferred by jointly coupling F, D and P to achieve more robust estimation. To this end, we propose a novel scene flow framework named EffiScene with efficient joint rigidity learning, going beyond the existing pipeline with independent auxiliary structures. In EffiScene, we first estimate optical flow and depth at the coarse level and then compute camera pose by Perspectiven-Points method. To jointly learn local rigidity, we design a novel Rigidity From Motion (RfM) layer with three principal components: (i) correlation extraction;(ii) boundary learning;and (iii) outlier exclusion. Final outputs are fused based on the rigid map M-R from RfM at finer levels. To efficiently train EffiScene, two new losses L-bn(d) and L-unc are designed to prevent trivial solutions and to regularize the flow boundary discontinuity. Extensive experiments on scene flow benchmark KITTI show that our method is effective and significantly improves the state-of-the-art ap-proaches for all sub-tasks, i.e. optical flow (5.19 -> 4.20), depth estimation (3.78 -> 3.46), visual odometry (0.012 -> 0.011) and motion segmentation (0.57 -> 0.62).

关键词： Couplings computer vision Image motion analysis Motion segmentation Estimation Benchmark testing Cameras

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A Real-Time Local Binary pattern-based Face recognition System on Open CV using Boosted Cascade of Simple Features 13

A Real-Time Local Binary Pattern-based Face Recognition Syst...

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13th ieee International conference on Communication Systems and Network Technologies, CSNT 2024

作者： Choudhary, Sarswati Pareyani, Silky Kourav, Sateesh Gyan Ganga College of Technology Department of Electronics and Communication Engineering Jabalpur India Department of Electronics and Communication Engineering MP Jabalpur India

ISBN: (纸本)9798350305463

Face recognition has grabbed the interest of biometrics, pattern recognition, and computer vision scientists. Face detection in photos has grown more significant during the last 10 years. Using OpenCV and a boosted cascade of basic features based on Local Binary pattern (LBP), we created a real-time face recognition system. Pre-processing the photographs improves their quality and normalizes them. You may wish to resize the photographs to a uniform size, convert them to grayscale, and do any other preparatory procedures that are required. Replace file locations and parameters with values appropriate to your dataset and project structure. The pre-processing step involves segmentation, filtering, thresholding, and picture quality enhancement. The BCSF Technique Face recognition is a critical stage in the recognition process. A Faces Database Each face in the database comprises an RTLBP computation and a correlation. The identity vectors in the covariance matrix are derived using a collection of fingerprint photos. Metrics of Performance The recognition method takes 10.18 seconds and has a 99.93% success rate. Face recognition and Annotation on Multiple Faces Several faces can be recognized and annotated in a single run using traditional approaches. The detector runs in real-time at a frame rate of fifteen frames per second. It is not dependent on visual differences or skin color recognition. The name of the identified individual is shown in the upper left corner of a highlighted rectangle enclosing the face. Methods Evaluation summarizes the benefits and drawbacks of common strategies. This article looks into a variety of novel facial recognition systems. © 2024 ieee.

关键词： Local binary pattern

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