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CDFI: Compression-Driven Network Design for Frame Interpolation

CDFI: Compression-Driven Network Design for Frame Interpolat...

引用

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

作者： Ding, Tianyu Liang, Luming Zhu, Zhihui Zharkov, Ilya Johns Hopkins Univ Baltimore MD 21218 USA Microsoft Redmond WA 98052 USA Univ Denver Denver CO 80208 USA Microsoft Appl Sci Grp Redmond WA 98052 USA

ISBN: (纸本)9781665445092

DNN-based frame interpolation-that generates the intermediate frames given two consecutive frames-typically relies on heavy model architectures with a huge number of features, preventing them from being deployed on systems with limited resources, e.g., mobile devices. We propose a compression-driven network design for frame interpolation (CDFI), that leverages model pruning through sparsity-inducing optimization to significantly reduce the model size while achieving superior performance. Concretely, we first compress the recently proposed AdaCoF model and show that a 10x compressed AdaCoF performs similarly as its original counterpart;then we further improve this compressed model by introducing a multi-resolution warping module, which boosts visual consistencies with multi-level details. As a consequence, we achieve a significant performance gain with only a quarter in size compared with the original AdaCoE Moreover, our model performs favorably against other state-of-the-arts in a broad range of datasets. Finally, the proposed compression-driven framework is generic and can be easily transferred to other DNN-based frame interpolation algorithm.

关键词： Performance evaluation Interpolation Visualization computer vision Redundancy computer architecture Performance gain

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Partition-Guided GANs

Partition-Guided GANs

引用

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

作者： Armandpour, Mohammadreza Sadeghian, Ali Li, Chunyuan Zhou, Mingyuan Texas A&M Univ College Stn TX 77843 USA Univ Florida Gainesville FL 32611 USA Microsoft Res Redmond WA USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781665445092

Despite the success of Generative Adversarial Networks (GANs), their training suffers from several well-known problems, including mode collapse and difficulties learning a disconnected set of manifolds. In this paper, we break down the challenging task of learning complex high dimensional distributions, supporting diverse data samples, to simpler sub-tasks. Our solution relies on designing a partitioner that breaks the space into smaller regions, each having a simpler distribution, and training a different generator for each partition. This is done in an unsupervised manner without requiring any labels. We formulate two desired criteria for the space partitioner that aid the training of our mixture of generators: 1) to produce connected partitions and 2) provide a proxy of distance between partitions and data samples, along with a direction for reducing that distance. These criteria are developed to avoid producing samples from places with non-existent data density, and also facilitate training by providing additional direction to the generators. We develop theoretical constraints for a space partitioner to satisfy the above criteria. Guided by our theoretical analysis, we design an effective neural architecture for the space partitioner that empirically assures these conditions. Experimental results on various standard benchmarks show that the proposed unsupervised model outperforms several recent methods.

关键词： Training Manifolds computer vision computer architecture Generative adversarial networks Generators pattern recognition

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A Multimodal Approach Integrating Convolutional and Recurrent Neural Networks for Alzheimer’s Disease Temporal Progression Prediction

A Multimodal Approach Integrating Convolutional and Recurren...

引用

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

作者： Durga Supriya HL Swetha Mary Thomas Sowmya Kamath S National Institute of Technology Karnataka India Mangalore Karnataka India Dept. of Information Technology Healthcare Analytics and Language Engineering (HALE) Lab National Institute of Technology Karnataka Mangaluru India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Alzheimer’s Disease (AD) poses a substantial healthcare challenge marked by cognitive decline and a lack of definitive treatments. As the global population ages, the prevalence of AD escalates, underscoring the need for more advanced diagnostic techniques. Current single-modality methods have limitations, emphasizing the critical need for early detection and precise diagnosis to facilitate timely interventions and the development of effective therapies. We propose a novel multimodal medical diagnostic framework for AD employing a hybrid deep learning model. This framework integrates a 3D Convolutional Neural Network (CNN) to extract detailed intra-slice features from MRI volumes and a Long Short-Term Memory (LSTM) network to capture intricate inter-sequence patterns indicative of AD progression. By leveraging longitudinal MRI data alongside spatial, temporal, biomarkers, and cognitive scores, our framework significantly enhances diagnostic accuracy, particularly in the early stages of the disease. We incorporate Grad-CAM to enhance the interpretability of MRI-based diagnoses by highlighting relevant brain regions. This multimodal approach achieves a promising accuracy of 92.65%, outperforming state-of-the-art works by a margin of 6%.

关键词： Accuracy Three-dimensional displays Recurrent neural networks Magnetic resonance imaging Feature extraction Spatial databases pattern recognition

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WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos

WOAD: Weakly Supervised Online Action Detection in Untrimmed...

引用

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

作者： Gao, Mingfei Zhou, Yingbo Xu, Ran Socher, Richard Xiong, Caiming Salesforce Res Palo Alto CA 94301 USA

ISBN: (纸本)9781665445092

Online action detection in untrimmed videos aims to identify an action as it happens, which makes it very important for real-time applications. Previous methods rely on tedious annotations of temporal action boundaries for training, which hinders the scalability of online action detection systems. We propose WOAD, a weakly supervised framework that can be trained using only video-class labels. WOAD contains two jointly-trained modules, i.e., temporal proposal generator (TPG) and online action recognizer (OAR). Supervised by video-class labels, TPG works offline and targets at accurately mining pseudo frame-level labels for OAR. With the supervisory signals from TPG, OAR learns to conduct action detection in an online fashion. Experimental results on THUMOS'14, ActivityNet1.2 and ActivityNet1.3 show that our weakly-supervised method largely outperforms weakly-supervised baselines and achieves comparable performance to the previous strongly-supervised methods. Beyond that, WOAD is flexible to leverage strong supervision when it is available. When strongly supervised, our method obtains the state-of-the-art results in the tasks of both online per-frame action recognition and online detection of action start.

关键词： Training computer vision Annotations Scalability Real-time systems Generators pattern recognition

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Neighborhood Contrastive Learning for Novel Class Discovery

Neighborhood Contrastive Learning for Novel Class Discovery

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

作者： Zhong, Zhun Fini, Enrico Roy, Subhankar Luo, Zhiming Ricci, Elisa Sebe, Nicu Univ Trento Trento Italy Xiamen Univ Xiamen Peoples R China Fdn Bruno Kessler Povo Italy

ISBN: (纸本)9781665445092

In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the peculiarities of NCD to build a new framework, named Neighborhood Contrastive Learning (NCL), to learn discriminative representations that are important to clustering performance. Our contribution is twofold. First, we find that a feature extractor trained on the labeled set generates representations in which a generic query sample and its neighbors are likely to share the same class. We exploit this observation to retrieve and aggregate pseudo-positive pairs with contrastive learning, thus encouraging the model to learn more discriminative representations. Second, we notice that most of the instances are easily discriminated by the network, contributing less to the contrastive loss. To overcome this issue, we propose to generate hard negatives by mixing labeled and unlabeled samples in the feature space. We experimentally demonstrate that these two ingredients significantly contribute to clustering performance and lead our model to outperform state-of-the-art methods by a large margin (e.g., clustering accuracy +13% on CIFAR-100 and +8% on ImageNet).

关键词： computer vision Aggregates Feature extraction pattern recognition Task analysis

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Deep Implicit Templates for 3D Shape Representation

Deep Implicit Templates for 3D Shape Representation

引用

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

作者： Zheng, Zerong Yu, Tao Dai, Qionghai Liu, Yebin Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781665445092

Deep implicit functions (DIFs), as a kind of 3D shape representation, are becoming more and more popular in the 3D vision community due to their compactness and strong representation power. However, unlike polygon mesh-based templates, it remains a challenge to reason dense correspondences or other semantic relationships across shapes represented by DIFs, which limits its applications in texture transfer, shape analysis and so on. To overcome this limitation and also make DIFs more interpretable, we propose Deep Implicit Templates, a new 3D shape representation that supports explicit correspondence reasoning in deep implicit representations. Our key idea is to formulate DIFs as conditional deformations of a template implicit function. To this end, we propose Spatial Warping LSTM, which decomposes the conditional spatial transformation into multiple point-wise transformations and guarantees generalization capability. Moreover, the training loss is carefully designed in order to achieve high reconstruction accuracy while learning a plausible template with accurate correspondences in an unsupervised manner. Experiments show that our method can not only learn a common implicit template for a collection of shapes, but also establish dense correspondences across all the shapes simultaneously without any supervision.

关键词： Training Geometry computer vision Three-dimensional displays Shape Semantics Network architecture

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VinVL: Revisiting Visual Representations in vision-Language Models

VinVL: Revisiting Visual Representations in Vision-Language ...

引用

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

作者： Zhang, Pengchuan Li, Xiujun Hu, Xiaowei Yang, Jianwei Zhang, Lei Wang, Lijuan Choi, Yejin Gao, Jianfeng Microsoft Corp Redmond WA 98052 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781665445092

This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used bottom-up and top-down model [2], the new model is bigger, better-designed for VL tasks, and pretrained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter significantly in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model OSCAR [20], and utilize an improved approach OSCAR+ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks. Our results show that the new visual features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. Code, models and pre-extracted features are released at https://***/pzzhang/VinVL.

关键词： Training Visualization Computational modeling Object detection Benchmark testing Feature extraction Transformers

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Meta Batch-Instance Normalization for Generalizable Person Re-Identification

Meta Batch-Instance Normalization for Generalizable Person R...

引用

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

作者： Choi, Seokeon Kim, Taekyung Jeong, Minki Park, Hyoungseob Kim, Changick Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9781665445092

Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, generalizable Re-ID has recently attracted growing attention. Many existing methods have employed an instance normalization technique to reduce style variations, but the loss of discriminative information could not be avoided. In this paper, we propose a novel generalizable Re-ID framework, named Meta Batch-Instance Normalization (MetaBIN). Our main idea is to generalize normalization layers by simulating unsuccessful generalization scenarios beforehand in the meta-learning pipeline. To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers. Moreover, we diversify the virtual simulations via our meta-train loss accompanied by a cyclic inner-updating manner to boost generalization capability. After all, the MetaBIN framework prevents our model from overfitting to the given source styles and improves the generalization capability to unseen domains without additional data augmentation or complicated network design. Extensive experimental results show that our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark and the cross-domain Re-ID problem. The source code is available at: https://***/bismex/MetaBIN.

关键词： computer vision Codes Computational modeling Pipelines Benchmark testing Data models pattern recognition

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Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time

Semi-Supervised 3D Hand-Object Poses Estimation with Interac...

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

作者： Liu, Shaowei Jiang, Hanwen Xu, Jiarui Liu, Sifei Wang, Xiaolong Univ Calif San Diego La Jolla CA 92093 USA NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Estimating 3D hand and object pose from a single image is an extremely challenging problem: hands and objects are often self-occluded during interactions, and the 3D annotations are scarce as even humans cannot directly label the ground-truths from a single image perfectly. To tackle these challenges, we propose a unified framework for estimating the 3D hand and object poses with semi-supervised learning. We build a joint learning framework where we perform explicit contextual reasoning between hand and object representations. Going beyond limited 3D annotations in a single image, we leverage the spatial-temporal consistency in large-scale hand-object videos as a constraint for generating pseudo labels in semi-supervised learning. Our method not only improves hand pose estimation in challenging real-world dataset, but also substantially improve the object pose which has fewer ground-truths per instance. By training with large-scale diverse videos, our model also generalizes better across multiple out-of-domain datasets. Project page and code: https://***/Semi-Hand-Object.

关键词： Training computer vision Three-dimensional displays Annotations Filtering Pose estimation Semisupervised learning

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Regularizing Neural Networks via Adversarial Model Perturbation

Regularizing Neural Networks via Adversarial Model Perturbat...

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

作者： Zheng, Yaowei Zhang, Richong Mao, Yongyi Beihang Univ BDBC Beijing Peoples R China Beihang Univ SKLSDE Beijing Peoples R China Univ Ottawa Sch EECS Ottawa ON Canada

ISBN: (纸本)9781665445092

Effective regularization techniques are highly desired in deep learning for alleviating overfitting and improving generalization. This work proposes a new regularization scheme, based on the understanding that the flat local minima of the empirical risk cause the model to generalize better. This scheme is referred to as adversarial model perturbation (AMP), where instead of directly minimizing the empirical risk, an alternative "AMP loss" is minimized via SGD. Specifically, the AMP loss is obtained from the empirical risk by applying the "worst" norm-bounded perturbation on each point in the parameter space. Comparing with most existing regularization schemes, AMP has strong theoretical justifications, in that minimizing the AMP loss can be shown theoretically to favour flat local minima of the empirical risk. Extensive experiments on various modern deep architectures establish AMP as a new state of the art among regularization schemes.

关键词： Deep learning computer vision Codes Perturbation methods Computational modeling Neural networks Tools

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