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Hyperdimensional computing as a framework for systematic aggregation of image descriptors

Hyperdimensional computing as a framework for systematic agg...

引用

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

作者： Neubert, Peer Schubert, Stefan Tech Univ Chemnitz Chemnitz Germany

ISBN: (纸本)9781665445092

Image and video descriptors are an omnipresent tool in computer vision and its application fields like mobile robotics. Many hand-crafted and in particular learned image descriptors are numerical vectors with a potentially (very) large number of dimensions. Practical considerations like memory consumption or time for comparisons call for the creation of compact representations. In this paper, we use hyperdimensional computing (HDC) as an approach to systematically combine information from a set of vectors in a single vector of the same dimensionality. HDC is a known technique to perform symbolic processing with distributed representations in numerical vectors with thousands of dimensions. We present a HDC implementation that is suitable for processing the output of existing and future (deep learning based) image descriptors. We discuss how this can be used as a framework to process descriptors together with additional knowledge by simple and fast vector operations. A concrete outcome is a novel HDC-based approach to aggregate a set of local image descriptors together with their image positions in a single holistic descriptor. The comparison to available holistic descriptors and aggregation methods on a series of standard mobile robotics place recognition experiments shows a 20% improvement in average performance and > 2x better worst-case performance compared to runner-up.

关键词： computer vision Systematics Aggregates Semantics Memory management Layout Tools

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Representative Forgery Mining for Fake Face Detection

Representative Forgery Mining for Fake Face Detection

引用

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

作者： Wang, Chengrui Deng, Weihong Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781665445092

Although vanilla Convolutional Neural Network (CNN) based detectors can achieve satisfactory performance on fake face detection, we observe that the detectors tend to seek forgeries on a limited region of face, which reveals that the detectors is short of understanding of forgery. Therefore, we propose an attention-based data augmentation framework to guide detector refine and enlarge its attention. Specifically, our method tracks and occludes the Top-N sensitive facial regions, encouraging the detector to mine deeper into the regions ignored before for more representative forgery. Especially, our method is simple-touse and can be easily integrated with various CNN models. Extensive experiments show that the detector trained with our method is capable to separately point out the representative forgery of fake faces generated by different manipulation techniques, and our method enables a vanilla CNN-based detector to achieve state-of-the-art performance without structure modification. Our code is available at https://***/crywang/RFM.

关键词： Codes Face recognition Refining Training data Data visualization Detectors Forgery

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Learning to Associate Every Segment for Video Panoptic Segmentation

Learning to Associate Every Segment for Video Panoptic Segme...

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

作者： Woo, Sanghyun Kim, Dahun Lee, Joon-Young Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

Temporal correspondence - linking pixels or objects across frames - is a fundamental supervisory signal for the video models. For the panoptic understanding of dynamic scenes, we further extend this concept to every segment. Specifically, we aim to learn coarse segment-level matching and fine pixel-level matching together. We implement this idea by designing two novel learning objectives. To validate our proposals, we adopt a deep siamese model and train the model to learn the temporal correspondence on two different levels (i.e., segment and pixel) along with the target task. At inference time, the model processes each frame independently without any extra computation and post-processing. We show that our per-frame inference model can achieve new state-of-the-art results on Cityscapes-VPS and VIPER datasets. Moreover, due to its high efficiency, the model runs in a fraction of time (3x) compared to the previous state-of-the-art approach.

关键词： Learning systems computer vision Computational modeling Linear programming pattern recognition Proposals Task analysis

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Few-shot Image Generation via Cross-domain Correspondence

Few-shot Image Generation via Cross-domain Correspondence

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

作者： Ojha, Utkarsh Li, Yijun Lu, Jingwan Efros, Alexei A. Lee, Yong Jae Shechtman, Eli Zhang, Richard Adobe Res San Jose CA 95110 USA Univ Calif Davis Davis CA 95616 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781665445092

Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large source domain for pretraining and transfer the diversity information from source to target. We propose to preserve the relative similarities and differences between instances in the source via a novel cross-domain distance consistency loss. To further reduce overfitting, we present an anchor-based strategy to encourage different levels of realism over different regions in the latent space. With extensive results in both photorealistic and non-photorealistic domains, we demonstrate qualitatively and quantitatively that our few-shot model automatically discovers correspondences between source and target domains and generates more diverse and realistic images than previous methods.

关键词： Training computer vision Image synthesis Computational modeling pattern recognition

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Look Before you Speak: Visually Contextualized Utterances

Look Before you Speak: Visually Contextualized Utterances

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

作者： Seo, Paul Hongsuck Nagrani, Arsha Schmid, Cordelia Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

While most conversational AI systems focus on textual dialogue only, conditioning utterances on visual context (when it's available) can lead to more realistic conversations. Unfortunately, a major challenge for incorporating visual context into conversational dialogue is the lack of large-scale labeled datasets. We provide a solution in the form of a new visually conditioned Future Utterance Prediction task. Our task involves predicting the next utterance in a video, using both visual frames and transcribed speech as context. By exploiting the large number of instructional videos online, we train a model to solve this task at scale, without the need for manual annotations. Leveraging recent advances in multimodal learning, our model consists of a novel co-attentional multimodal video transformer, and when trained on both textual and visual context, outperforms baselines that use textual inputs alone. Further, we demonstrate that our model trained for this task on unlabelled videos achieves state-of-the-art performance on a number of downstream VideoQA benchmarks such as MSRVTT-QA, MSVD-QA, ActivityNet-QA and How2QA.

关键词： Visualization computer vision Annotations Manuals Benchmark testing Transformers pattern recognition

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Generative Interventions for Causal Learning

Generative Interventions for Causal Learning

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

作者： Mao, Chengzhi Cha, Augustine Gupta, Amogh Wang, Hao Yang, Junfeng Vondrick, Carl Columbia Univ New York NY 10027 USA Rutgers State Univ New Brunswick NJ USA

ISBN: (纸本)9781665445092

We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts. Discriminative models often learn naturally occurring spurious correlations, which cause them to fail on images outside of the training distribution. In this paper;we show that we can steer generative models to manufacture interventions on features caused by confounding factors. Experiments, visualizations, and theoretical results show this method learns robust representations more consistent with the underlying causal relationships. Our approach improves performance on multiple datasets demanding out-of-distribution generalization, and we demonstrate state-of-the-art performance generalizing from ImageNet to ObjectNet dataset.

关键词： Training Visualization computer vision Correlation Computational modeling Control systems pattern recognition

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Variational Pedestrian Detection

Variational Pedestrian Detection

引用

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

作者： Zhang, Yuang He, Huanyu Li, Jianguo Li, Yuxi See, John Lin, Weiyao Shanghai Jiao Tong Univ Shanghai Peoples R China Ant Grp Hangzhou Peoples R China Heriot Watt Univ Putrajaya Malaysia

ISBN: (纸本)9781665445092

Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods. In this paper, we develop a unique perspective of pedestrian detection as a variational inference problem. We formulate a novel and efficient algorithm for pedestrian detection by modeling the dense proposals as a latent variable while proposing a customized Auto-Encoding Variational Bayes (AEVB) algorithm. Through the optimization of our proposed algorithm, a classical detector can be fashioned into a variational pedestrian detector. Experiments conducted on CrowdHuman and CityPersons datasets show that the proposed algorithm serves as an efficient solution to handle the dense pedestrian detection problem for the case of single-stage detectors. Our method can also be flexibly applied to two-stage detectors, achieving notable performance enhancement.

关键词： computer vision Computational modeling Detectors Object detection Propulsion Inference algorithms pattern recognition

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Conditional Bures Metric for Domain Adaptation

Conditional Bures Metric for Domain Adaptation

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

作者： Luo, You-Wei Ren, Chuan-Xian Sun Yat Sen Univ Sch Math Guangzhou Guangdong Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9781665445092

As a vital problem in classification-oriented transfer, unsupervised domain adaptation (UDA) has attracted widespread attention in recent years. Previous UDA methods assume the marginal distributions of different domains are shifted while ignoring the discriminant information in the label distributions. This leads to classification performance degeneration in real applications. In this work, we focus on the conditional distribution shift problem which is of great concern to current conditional invariant models. We aim to seek a kernel covariance embedding for conditional distribution which remains yet unexplored. Theoretically, we propose the Conditional Kernel Bures (CKB) metric for characterizing conditional distribution discrepancy, and derive an empirical estimation for the CKB metric without introducing the implicit kernel feature map. It provides an interpretable approach to understand the knowledge transfer mechanism. The established consistency theory of the empirical estimation provides a theoretical guarantee for convergence. A conditional distribution matching network is proposed to learn the conditional invariant and discriminative features for UDA. Extensive experiments and analysis show the superiority of our proposed model.

关键词： Measurement computer vision Analytical models Adaptation models Computational modeling Estimation pattern recognition

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Rethinking and Improving the Robustness of Image Style Transfer

Rethinking and Improving the Robustness of Image Style Trans...

引用

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

作者： Wang, Pei Li, Yijun Vasconcelos, Nuno UC La Jolla CA 92093 USA Adobe Res Los Altos CA USA

ISBN: (纸本)9781665445092

Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the visual style of an image. Surprisingly, however, this stylization quality is not robust and often degrades significantly when applied to features from more advanced and lightweight networks, such as those in the ResNet family. By performing extensive experiments with different network architectures, we find that residual connections, which represent the main architectural difference between VGG and ResNet, produce feature maps of small entropy, which are not suitable for style transfer. To improve the robustness of the ResNet architecture, we then propose a simple yet effective solution based on a softmax transformation of the feature activations that enhances their entropy. Experimental results demonstrate that this small magic can greatly improve the quality of stylization results, even for networks with random weights. This suggests that the architecture used for feature extraction is more important than the use of learned weights for the task of style transfer.

关键词： Visualization Smoothing methods Correlation computer architecture Network architecture Feature extraction Robustness

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Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food

Nutrition5k: Towards Automatic Nutritional Understanding of ...

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

作者： Thames, Quin Karpur, Arjun Norris, Wade Xia, Fangting Panait, Liviu Weyand, Tobias Sim, Jack Google Res Mountain View CA 94043 USA Percept Labs Great Falls VA USA

ISBN: (纸本)9781665445092

Understanding the nutritional content of food from visual data is a challenging computer vision problem, with the potential to have a positive and widespread impact on public health. Studies in this area are limited to existing datasets in the field that lack sufficient diversity or labels required for training models with nutritional understanding capability. We introduce Nutrition5k, a novel dataset of 5k diverse, real world food dishes with corresponding video streams, depth images, component weights, and high accuracy nutritional content annotation. We demonstrate the potential of this dataset by training a computer vision algorithm capable of predicting the caloric and macronutrient values of a complex, real world dish at an accuracy that outperforms professional nutritionists. Further we present a baseline for incorporating depth sensor data to improve nutrition predictions. We release Nutrition5k in the hope that it will accelerate innovation in the space of nutritional understanding.

关键词： Training computer vision Visualization Technological innovation Annotations Estimation Streaming media

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