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Energy-based Latent Aligner for Incremental Learning

Energy-based Latent Aligner for Incremental Learning

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

作者： Joseph, K. J. Khan, Salman Khan, Fahad Shahbaz Anwer, Rao Muhammad Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden Aalto Univ Espoo Finland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks. This behavior emerges because the parameter updates optimized for the new tasks may not align well with the updates suitable for older tasks. The resulting latent representation mismatch causes forgetting. In this work, we propose ELI: Energy-based Latent Aligner for Incremental Learning, which first learns an energy manifold for the latent representations such that previous task latents will have low energy and the current task latents have high energy values. This learned manifold is used to counter the representational shift that happens during incremental learning. The implicit regularization that is offered by our proposed methodology can be used as a plug-and-play module in existing incremental learning methodologies. We validate this through extensive evaluation on CIFAR-100, ImageNet subset, ImageNet 1k and Pascal VOC datasets. We observe consistent improvement when ELI is added to three prominent methodologies in class-incremental learning, across multiple incremental settings. Further, when added to the state-of-the-art incremental object detector, ELI provides over 5% improvement in detection accuracy, corroborating its effectiveness and complementary advantage to the existing art. Code is available at: https://***/JosephKJ/ELI.

关键词： Manifolds Deep learning computer vision Codes Pipelines Object detection Detectors

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DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale Consistency

DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-s...

引用

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

作者： Yang, Zongxin Yu, Xin Yang, Yi Baidu Res Beijing Peoples R China Univ Technol Sydney ReLER Sydney NSW Australia

ISBN: (纸本)9781665445092

Compared to 2D object bounding-box labeling, it is very difficult for humans to annotate 3D object poses, especially when depth images of scenes are unavailable. This paper investigates whether we can estimate the object poses effectively when only RGB images and 2D object annotations are given. To this end, we present a two-step pose estimation framework to attain 6DoF object poses from 2D object bounding-boxes. In the first step, the framework learns to segment objects from real and synthetic data in a weakly-supervised fashion, and the segmentation masks will act as a prior for pose estimation. In the second step, we design a dual-scale pose estimation network, namely DSC-PoseNet, to predict object poses by employing a differential renderer. To be specific, our DSC-PoseNet firstly predicts object poses in the original image scale by comparing the segmentation masks and the rendered visible object masks. Then, we resize object regions to a fixed scale to estimate poses once again. In this fashion, we eliminate large scale variations and focus on rotation estimation, thus facilitating pose estimation. Moreover, we exploit the initial pose estimation to generate pseudo ground-truth to train our DSC-PoseNet in a self-supervised manner. The estimation results in these two scales are ensembled as our final pose estimation. Extensive experiments on widely-used benchmarks demonstrate that our method outperforms state-of-the-art models trained on synthetic data by a large margin and even is on par with several fully-supervised methods.

关键词： Training Image segmentation computer vision Three-dimensional displays Annotations Pose estimation Benchmark testing

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The Devil Is in the Details: Window-based Attention for Image Compression

The Devil Is in the Details: Window-based Attention for Imag...

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

作者： Zou, Renjie Song, Chunfeng Zhang, Zhaoxiang Chinese Acad Sci CASIA Inst Automat Natl Lab Pattern Recognit NLPR Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China HKISI CAS Ctr Artificial Intelligence & Robot Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Learned image compression methods have exhibited superior rate-distortion performance than classical image compression standards. Most existing learned image compression models are based on Convolutional Neural Networks (CNNs). Despite great contributions, a main drawback of CNN based model is that its structure is not designed for capturing local redundancy, especially the non-repetitive textures, which severely affects the reconstruction quality. Therefore, how to make MI use of both global structure and local texture becomes the core problem for learning-based image compression. Inspired by recent progresses of vision Transformer (ViT) and Swin Transformer, we found that combining the local-aware attention mechanism with the global-related feature learning could meet the expectation in image compression. In this paper, we first extensively study the effects of multiple kinds of attention mechanisms for local features learning, then introduce a more straightforward yet effective window-based local attention block. The proposed window-based attention is very flexible which could work as a plug-and-play component to enhance CNN and Transformer models. Moreover, we propose a novel Symmetrical TransFormer (STF) framework with absolute transformer blocks in the down-sampling encoder and up-sampling decoder. Extensive experimental evaluations have shown that the proposed method is effective and outperforms the state-of-the-art methods. The code is publicly available at https://***/Googolxx/STF.

关键词： Representation learning Image coding Redundancy Rate-distortion Transformer cores Transformers Decoding

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MnasNet: Platform-Aware Neural Architecture Search for Mobile 32

MnasNet: Platform-Aware Neural Architecture Search for Mobil...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Tan, Mingxing Chen, Bo Pang, Ruoming Vasudevan, Vijay Sandier, Mark Howard, Andrew Le, Quoc V. Google Brain Mountain View CA 94043 USA Google Inc Menlo Pk CA USA

ISBN: (纸本)9781728132938

Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts have been dedicated to design and improve mobile CNNs on all dimensions, it is very difficult to manually balance these trade-offs when there are so many architectural possibilities to consider. In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. Unlike previous work, where latency is considered via another, often inaccurate proxy (e.g., FLOPS), our approach directly measures real-world inference latency by executing the model on mobile phones. To further strike the right balance between flexibility and search space size, we propose a novel factorized hierarchical search space that encourages layer diversity throughout the network. Experimental results show that our approach consistently outperforms state-of-the-art mobile CNN models across multiple vision tasks. On the ImageNet classification task, our MnasNet achieves 75.2% top-1 accuracy with 78ms latency on a Pixel phone, which is 1.8x faster than MobileNetV2 with 0.5% higher accuracy and 2.3x faster than NASNet with 1.2% higher accuracy. Our MnasNet also achieves better mAP quality than MobileNets for COCO object detection.

关键词： Categorization Deep Learning recognition: Detection Retrieval

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Transductive Unbiased Embedding for Zero-Shot Learning 31

Transductive Unbiased Embedding for Zero-Shot Learning

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Song, Jie Shen, Chengchao Yang, Yezhou Liu, Yang Song, Mingli Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Arizona State Univ Tempe AZ USA Alibaba Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538664209

Most existing Zero-Shot Learning (ZSL) methods have the strong bias problem, in which instances of unseen (target) classes tend to be categorized as one of the seen (source) classes. So they yield poor performance after being deployed in the generalized ZSL settings. In this paper, we propose a straightforward yet effective method named Quasi-Fully Supervised Learning (QFSL) to alleviate the bias problem. Our method follows the way of transductive learning, which assumes that both the labeled source images and unlabeled target images are available for training. In the semantic embedding space, the labeled source images are mapped to several fixed points specified by the source categories, and the unlabeled target images are forced to be mapped to other points specified by the target categories. Experiments conducted on AwA2, CUB and SUN datasets demonstrate that our method outperforms existing state-of-the-art approaches by a huge margin of 9.3 similar to 24.5% following generalized ZSL settings, and by a large margin of 0.2 similar to 16.2% following conventional ZSL settings.

关键词： Visualization Semantics Training Target recognition Image recognition Data models Supervised learning

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Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification

Farewell to Mutual Information: Variational Distillation for...

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

作者： Tian, Xudong Zhang, Zhizhong Lin, Shaohui Qu, Yanyun Xie, Yuan Ma, Lizhuang East China Normal Univ Shanghai Peoples R China Xiamen Univ Xiamen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781665445092

The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy. Though IB principle has been applied to a wide range of applications, its optimization remains a challenging problem which heavily relies on the accurate estimation of mutual information. In this paper, we present a new strategy, Variational Self-Distillation (VSD), which provides a scalable, flexible and analytic solution to essentially fitting the mutual information but without explicitly estimating it. Under rigorously theoretical guarantee, VSD enables the IB to grasp the intrinsic correlation between representation and label for supervised training. Furthermore, by extending VSD to multi-view learning, we introduce two other strategies, Variational Cross-Distillation (VCD) and Variational Mutual-Learning (VML), which significantly improve the robustness of representation to viewchanges by eliminating view-specific and task-irrelevant information. To verify our theoretically grounded strategies, we apply our approaches to cross-modal person Re-ID, and conduct extensive experiments, where the superior performance against state-of-the-art methods are demonstrated. Our intriguing findings highlight the need to rethink the way to estimate mutual information.

关键词： Training Correlation Fitting Redundancy Estimation Robustness pattern recognition

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Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection

Bayesian Nonparametric Submodular Video Partition for Robust...

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

作者： Sapkota, Hitesh Yu, Qi Rochester Inst Technol Rochester NY 14623 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Multiple-instance learning (MIL) provides an effective way to tackle the video anomaly detection problem by modeling it as a weakly supervised problem as the labels are usually only available at the video level while missing for frames due to expensive labeling cost. We propose to conduct novel Bayesian non-parametric submodular video partition (BN-SVP) to significantly improve MIL model training that can offer a highly reliable solution for robust anomaly detection in practical settings that include outlier segments or multiple types of abnormal events. BN-SVP essentially performs dynamic non-parametric hierarchical clustering with an enhanced self-transition that groups segments in a video into temporally consistent and semantically coherent hidden states that can be naturally interpreted as scenes. Each segment is assumed to be generated through a non-parametric mixture process that allows variations of segments within the same scenes to accommodate the dynamic and noisy nature of many real-world surveillance videos. The scene and mixture component assignment of BN-SVP also induces a pairwise similarity among segments, resulting in non-parametric construction of a submodular set function. Integrating this function with an MIL loss effectively exposes the model to a diverse set of potentially positive instances to improve its training. A greedy algorithm is developed to optimize the submodular function and support efficient model training. Our theoretical analysis ensures a strong performance guarantee of the proposed algorithm. The effectiveness of the proposed approach is demonstrated over multiple real-world anomaly video datasets with robust detection performance.

关键词： Training Upper bound Surveillance Bayes methods pattern recognition Partitioning algorithms Noise measurement

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Synthesizing 3D Shapes from Silhouette Image Collections using Multi-projection Generative Adversarial Networks 32

Synthesizing 3D Shapes from Silhouette Image Collections usi...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Xiao Dong, Yue Peers, Pieter Tong, Xin Univ Sci & Technol China Hefei Peoples R China Microsoft Res Asia Beijing Peoples R China Coll William & Mary Williamsburg VA 23187 USA

ISBN: (纸本)9781728132938

We present a new weakly supervised learning-based method for generating novel category-specific 3D shapes from unoccluded image collections. Our method is weakly supervised and only requires silhouette annotations from unoccluded, category-specific objects. Our method does not require access to the object's 3D shape, multiple observations per object from different views, intra-image pixel correspondences, or any view annotations. Key to our method is a novel multi-projection generative adversarial network (MP-GAN) that trains a 3D shape generator to be consistent with multiple 2D projections of the 3D shapes, and without direct access to these 3D shapes. This is achieved through multiple discriminators that encode the distribution of 2D projections of the 3D shapes seen from a different views. Additionally, to determine the view information for each silhouette image, we also train a view prediction network on visualizations of 3D shapes synthesized by the generator. We iteratively alternate between training the generator and training the view prediction network. We validate our multi-projection GAN on both synthetic and real image datasets. Furthermore, we also show that multi-projection GANs can aid in learning other high-dimensional distributions from lower dimensional training datasets, such as material-class specific spatially varying reflectance properties from images.

关键词： 3D from Multiview and Sensors Physics-based vision and Shape-from-X vision + Graphics

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Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion

Improving Multi-Target Multi-Camera Tracking by Track Refine...

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

作者： Specker, Andreas Florin, Lucas Cormier, Mickael Beyerer, Juergen Karlsruhe Inst Technol Karlsruhe Germany Fraunhofer IOSB Karlsruhe Germany Fraunhofer Ctr Machine Learning St Augustin Germany

ISBN: (纸本)9781665487399

Multi-camera tracking of vehicles on a city-wide level is a core component of modern traffic monitoring systems. For this task, single-camera tracking failures are the most common causes of errors concerning automatic multi-target multi-camera tracking systems. To address these problems, we propose several modules that aim at improving single-camera tracklets, e.g., appearance-based tracklet splitting, single-camera clustering, and track completion. After these track refinement steps, hierarchical clustering is used to associate the enhanced single-camera tracklets. During this stage, we leverage vehicle re-identification features as well as prior knowledge about the scene's topology. Last, the proposed track completion strategy is adopted for the cross-camera association task to obtain the final multi-camera tracks. Our method proves itself competitive: With it, we achieved 4th place in track 1 of the 2022 AI City Challenge.

关键词： computer vision conferences Urban areas Topology pattern recognition Task analysis Artificial intelligence

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CapDet: Unifying Dense Captioning and Open-World Detection Pretraining

CapDet: Unifying Dense Captioning and Open-World Detection P...

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

作者： Long, Yanxin Wen, Youpeng Han, Jianhua Xu, Hang Ren, Pengzhen Zhang, Wei Zhao, Shen Liang, Xiaodan Sun Yat Sen Univ Shenzhen Campus Shenzhen Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada MBZUAI Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

Benefiting from large-scale vision-language pre-training on image-text pairs, open-world detection methods have shown superior generalization ability under the zero-shot or few-shot detection settings. However, a pre-defined category space is still required during the inference stage of existing methods and only the objects belonging to that space will be predicted. To introduce a "real" open-world detector, in this paper, we propose a novel method named CapDet to either predict under a given category list or directly generate the category of predicted bounding boxes. Specifically, we unify the open-world detection and dense caption tasks into a single yet effective framework by introducing an additional dense captioning head to generate the region-grounded captions. Besides, adding the captioning task will in turn benefit the generalization of detection performance since the captioning dataset covers more concepts. Experiment results show that by unifying the dense caption task, our CapDet has obtained significant performance improvements (e.g., +2.1% mAP on LVIS rare classes) over the baseline method on LVIS (1203 classes). Besides, our CapDet also achieves state-of-the-art performance on dense captioning tasks, e.g., 15.44% mAP on VG V1.2 and 13.98% on the VG-COCO dataset.

关键词： detection recognition: Categorization retrieval

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