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Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings

Enriching ImageNet with Human Similarity Judgments and Psych...

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

作者： Roads, Brett D. Love, Bradley C. UCL Dept Expt Psychol London England

ISBN: (纸本)9781665445092

Advances in supervised learning approaches to object recognition flourished in part because of the availability of high-quality datasets and associated benchmarks. However, these benchmarks-such as ILSVRC-are relatively task-specific, focusing predominately on predicting class labels. We introduce a publicly-available dataset that embodies the task-general capabilities of human perception and reasoning. The Human Similarity Judgments extension to ImageNet (ImageNet-HSJ) is composed of a large set of human similarity judgments that supplements the existing ILSVRC validation set. The new dataset supports a range of task and performance metrics, including evaluation of unsupervised algorithms. We demonstrate two methods of assessment: using the similarity judgments directly and using a psychological embedding trained on the similarity judgments. This embedding space contains an order of magnitude more points (i.e., images) than previous efforts based on human judgments. We were able to scale to the MI 50,000 image ILSVRC validation set through a selective sampling process that used variational Bayesian inference and model ensembles to sample aspects of the embedding space that were most uncertain. To demonstrate the utility of ImageNet-HSJ, we used the similarity ratings and the embedding space to evaluate how well several popular models conform to human similarity judgments. One finding is that the more complex models that perform better on task-specific benchmarks do not better conform to human semantic judgments. In addition to the human similarity judgments, pre-trained psychological embeddings and code for inferring variational embeddings are made publicly available. ImageNet-HSJ supports the appraisal of internal representations and the development of more humanlike models.

关键词： Measurement computer vision Supervised learning Semantics Psychology Focusing Benchmark testing

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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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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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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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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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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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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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Learning Compositional Representation for 4D Captures with Neural ODE

Learning Compositional Representation for 4D Captures with N...

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

作者： Jiang, Boyan Zhang, Yinda Wei, Xingkui Xue, Xiangyang Fu, Yanwei Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Google Mountain View CA 94043 USA Fudan Univ Sch Data Sci MOE Frontiers Ctr Brain Sci Shanghai Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781665445092

Learning based representation has become the key to the success of many computer vision systems. While many 3D representations have been proposed, it is still an unaddressed problem how to represent a dynamically changing 3D object. In this paper, we introduce a compositional representation for 4D captures, i.e. a deforming 3D object over a temporal span, that disentangles shape, initial state, and motion respectively. Each component is represented by a latent code via a trained encoder. To model the motion, a neural Ordinary Differential Equation (ODE) is trained to update the initial state conditioned on the learned motion code, and a decoder takes the shape code and the updated state code to reconstruct the 3D model at each time stamp. lb this end, we propose an Identity Exchange Training (IET) strategy to encourage the network to learn effectively decoupling each component. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art deep learning based methods on 4D reconstruction, and significantly improves on various tasks, including motion transfer and completion.

关键词： Training Geometry Solid modeling computer vision Three-dimensional displays Codes Shape

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