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ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning

ECKPN: Explicit Class Knowledge Propagation Network for Tran...

引用

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

作者： Chen, Chaofan Yang, Xiaoshan Xu, Changsheng Huang, Xuhui Ma, Zhe Univ Sci & Technol China USTC Sch Informat Sci & Technol Hefei Anhui Peoples R China Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit NLPR Beijing Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing Peoples R China Second Acad CASIC X Lab Beijing Peoples R China

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Recently, the transductive graph-based methods have achieved great success in the few-shot classification task. However, most existing methods ignore exploring the class-level knowledge that can be easily learned by humans from just a handful of samples. In this paper, we propose an Explicit Class Knowledge Propagation Network (ECKPN), which is composed of the comparison, squeeze and calibration modules, to address this problem. Specifically, we first employ the comparison module to explore the pairwise sample relations to learn rich sample representations in the instance-level graph. Then, we squeeze the instance-level graph to generate the class-level graph, which can help obtain the class-level visual knowledge and facilitate modeling the relations of different classes. Next, the calibration module is adopted to characterize the relations of the classes explicitly to obtain the more discriminative class-level knowledge representations. Finally, we combine the class-level knowledge with the instance-level sample representations to guide the inference of the query samples. We conduct extensive experiments on four few-shot classification benchmarks, and the experimental results show that the proposed ECKPN significantly outperforms the state-of-the art methods.

关键词： Visualization computer vision Art Computational modeling Knowledge representation Benchmark testing Calibration

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Difficulty Estimation with Action Scores for computer vision Tasks

Difficulty Estimation with Action Scores for Computer Vision...

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Octavio Arriaga Sebastian Palacio Matias Valdenegro-Toro University of Bremen German Research Center for Artificial Intelligence University of Groningen

As more machine learning models are now being applied in real world scenarios it has become crucial to evaluate their difficulties and biases. In this paper we present an unsupervised method for calculating a difficulty score based on the accumulated loss per epoch. Our proposed method does not require any modification to the model, neither any external supervision, and it can be easily applied to a wide range of machine learning tasks. We provide results for the tasks of image classification, image segmentation, and object detection. We compare our score against similar metrics and provide theoretical and empirical evidence of their difference. Furthermore, we show applications of our proposed score for detecting incorrect labels, and test for possible biases.

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Generalized Foggy-Scene Semantic Segmentation by Frequency Decoupling

Generalized Foggy-Scene Semantic Segmentation by Frequency D...

引用

ieee computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Qi Bi Shaodi You Theo Gevers Computer Vision Research Group University of Amsterdam Amsterdam The Netherlands

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Foggy-scene semantic segmentation (FSSS) is highly challenging due to the diverse effects of fog on scene properties and the limited training data. Existing research has mainly focused on domain adaptation for FSSS, which has practical limitations when dealing with new scenes. In our paper, we introduce domain-generalized FSSS, which can work effectively on unknown distributions without extensive training. To address domain gaps, we propose a frequency decoupling (FreD) approach that separates fog-related effects (amplitude) from scene semantics (phase) in feature representations. Our method is compatible with both CNN and vision Transformer backbones and outperforms existing approaches in various scenarios.

关键词： Training computer vision Frequency-domain analysis Semantic segmentation conferences Semantics Training data

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Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings

Combining Semantic Guidance and Deep Reinforcement Learning ...

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

作者： Singh, Jaskirat Zheng, Liang Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665445092

Generation of stroke-based non-photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research efforts have been focused on teaching machines "how to paint", in a manner similar to a human painter. However, the applicability of previous methods has been limited to datasets with little variation in position, scale and saliency of the foreground object. As a consequence, we find that these methods struggle to cover the granularity and diversity possessed by real world images. To this end, we propose a Semantic Guidance pipeline with 1) a bi-level painting procedure for learning the distinction between foreground and background brush strokes at training time. 2) We also introduce invariance to the position and scale of the foreground object through a neural alignment model, which combines object localization and spatial transformer networks in an end to end manner, to zoom into a particular semantic instance. 3) The distinguishing features of the in-focus object are then amplified by maximizing a novel guided backpropagation based focus reward. The proposed agent does not require any supervision on human stroke-data and successfully handles variations in foreground object attributes, thus, producing much higher quality canvases for the CUB-200 Birds [29] and Stanford Cars-196 [17] datasets. Finally, we demonstrate the further efficacy of our method on complex datasets with multiple foreground object instances by evaluating an extension of our method on the challenging Virtual-KITTI [2] dataset. Source code and models are available at https://***/1jsingh/semantic-guidance.

关键词： Backpropagation Training computer vision Semantics Pipelines Reinforcement learning Stroke (medical condition)

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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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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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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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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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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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