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MIST: Multiple Instance Spatial Transformer

MIST: Multiple Instance Spatial Transformer

引用

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

作者： Angles, Baptiste Jin, Yuhe Kornblith, Simon Tagliasacchi, Andrea Yi, Kwang Moo Univ Victoria Victoria BC Canada Univ British Columbia Vancouver BC Canada Google Res Mountain View CA USA

ISBN: (纸本)9781665445092

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts. The network learns to extract the most significant K patches, and feeds these patches to a task-specific network - e.g., auto-encoder or classifier - to solve a domain specific problem. The challenge in training such a network is the non-differentiable top-K selection process. To address this issue, we lift the training optimization problem by treating the result of top-K selection as a slack variable, resulting in a simple, yet effective, multi-stage training. Our method is able to learn to detect recurring structures in the training dataset by learning to reconstruct images. It can also learn to localize structures when only knowledge on the occurrence of the object is provided, and in doing so it outperforms the state-of-the-art.

关键词： Training Location awareness Transformers Image decomposition pattern recognition Proposals Task analysis

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Pose-Guided Human Animation from a Single Image in the Wild

Pose-Guided Human Animation from a Single Image in the Wild

引用

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

作者： Yoon, Jae Shin Liu, Lingjie Golyanik, Vladislav Sarkar, Kripasindhu Park, Hyun Soo Theobalt, Christian Univ Minnesota Minneapolis MN 55455 USA Max Planck Inst Informat SIC Saarbrucken Germany

ISBN: (纸本)9781665445092

We present a new pose transfer method for synthesizing a human animation from a single image of a person controlled by a sequence of body poses. Existing pose transfer methods exhibit significant visual artifacts when applying to a novel scene, resulting in temporal inconsistency and failures in preserving the identity and textures of the person. To address these limitations, we design a compositional neural network that predicts the silhouette, garment labels, and textures. Each modular network is explicitly dedicated to a subtask that can be learned from the synthetic data. At the inference time, we utilize the trained network to produce a unified representation of appearance and its labels in UV coordinates, which remains constant across poses. The unified representation provides an incomplete yet strong guidance to generating the appearance in response to the pose change. We use the trained network to complete the appearance and render it with the background. With these strategies, we are able to synthesize human animations that can preserve the identity and appearance of the person in a temporally coherent way without any fine-tuning of the network on the testing scene. Experiments show that our method outperforms the state-of-the-arts in terms of synthesis quality, temporal coherence, and generalization ability.

关键词： Visualization computer vision Clothing Neural networks Coherence Animation pattern recognition

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Point Cloud Upsampling via Disentangled Refinement

Point Cloud Upsampling via Disentangled Refinement

引用

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

作者： Li, Ruihui Li, Xianzhi Heng, Pheng-Ann Fu, Chi-Wing Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uniformity and proximity-to-surface, and possibly amending small holes, all in a single network. After revisiting the task, we propose to disentangle the task based on its multi-objective nature and formulate two cascaded sub-networks, a dense generator and a spatial refiner. The dense generator infers a coarse but dense output that roughly describes the underlying surface, while the spatial refiner further fine-tunes the coarse output by adjusting the location of each point. Specifically, we design a pair of local and global refinement units in the spatial refiner to evolve a coarse feature map. Also, in the spatial refiner, we regress a per-point offset vector to further adjust the coarse outputs in fine scale. Extensive qualitative and quantitative results on both synthetic and real-scanned datasets demonstrate the superiority of our method over the state-of-the-arts.

关键词： Surface reconstruction Three-dimensional displays Pipelines Generators Surface roughness Rough surfaces pattern recognition

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Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion

Semantic Segmentation for Real Point Cloud Scenes via Bilate...

引用

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

作者： Qiu, Shi Anwar, Saeed Barnes, Nick Australian Natl Univ Canberra ACT Australia CSIRO Data61 Sydney NSW Australia

ISBN: (纸本)9781665445092

Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation. Particularly, real point cloud scenes can intuitively capture complex surroundings in the real world, but due to 3D data's raw nature, it is very challenging for machine perception. In this work, we concentrate on the essential visual task, semantic segmentation, for large-scale point cloud data collected in reality. On the one hand, to reduce the ambiguity in nearby points, we augment their local context by fully utilizing both geometric and semantic features in a bilateral structure. On the other hand, we comprehensively interpret the distinctness of the points from multiple resolutions and represent the feature map following an adaptive fusion method at point-level for accurate semantic segmentation. Further, we provide specific ablation studies and intuitive visualizations to validate our key modules. By comparing with state-of-the-art networks on three different benchmarks, we demonstrate the effectiveness of our network.

关键词： Visualization computer vision Three-dimensional displays Semantics Object detection Benchmark testing Real-time systems

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QAttn: Efficient GPU Kernels for mixed-precision vision Transformers

QAttn: Efficient GPU Kernels for mixed-precision Vision Tran...

引用

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

作者： Piotr Kluska Adrián Castelló Florian Scheidegger A. Cristiano I. Malossi Enrique S. Quintana-Ortí IBM Research Europe Universitat Politècnica de València Universitat Politècnica de València IBM Research Europe

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

vision Transformers have demonstrated outstanding performance in computer vision tasks. Nevertheless, this superior performance for large models comes at the expense of increasing memory usage for storing the parameters and intermediate activations. To accelerate model inference, in this work we develop and evaluate integer and mixed-precision kernels in Triton for the efficient execution of two fundamental building blocks of transformers –linear layer and attention– on graphics processing units (GPUs). On an NVIDIA A100 GPU, our kernel implementations of vision Transformers achieve a throughput speedup of up to 7x compared with reference kernels in PyTorch floating-point single precision (FP32). Additionally, the accuracy for the ViT Large model top-1 drops by less than one percent on the ImageNet1K classification task. We also observe up to 6x increased throughput by applying our kernels to the Segment Anything Model image encoder while keeping the mIOU close to the FP32 reference on the COCO2017 dataset for static and dynamic quantization. Furthermore, our kernels demonstrate improved speed to the TensorRT INT8 linear layer, and we improve the throughput of base FP16 (half precision) Triton attention on average by up to 19 ± 4.01%. We have open-sourced the QAtnn framework, which is tightly integrated with the PyTorch quantization workflow https://***/IBM/qattn.

关键词： computer vision Image segmentation Quantization (signal) Computational modeling Graphics processing units Prototypes Transformers

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LayoutGMN: Neural Graph Matching for Structural Layout Similarity

LayoutGMN: Neural Graph Matching for Structural Layout Simil...

引用

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

作者： Patil, Akshay Gadi Li, Manyi Fisher, Matthew Savva, Manolis Zhang, Hao Simon Fraser Univ Burnaby BC Canada Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

We present a deep neural network to predict structural similarity between 2D layouts by leveraging Graph Matching Networks (GMN). Our network, coined LayoutGMN, learns the layout metric via neural graph matching, using an attention-based GMN designed under a triplet network setting. To train our network, we utilize weak labels obtained by pixel-wise Intersection-over-Union (IoUs) to define the triplet loss. Importantly, LayoutGMN is built with a structural bias which can effectively compensate for the lack of structure awareness in IoUs. We demonstrate this on two prominent forms of layouts, viz., floorplans and UI designs, via retrieval experiments on large-scale datasets. In particular, retrieval results by our network better match human judgement of structural layout similarity compared to both IoUs and other baselines including a state-of-theart method based on graph neural networks and image convolution. In addition, LayoutGMN is the first deep model to offer both metric learning of structural layout similarity and structural matching between layout elements.

关键词： Measurement Deep learning computer vision Convolution Computational modeling Layout Graph neural networks

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Progressive Temporal Feature Alignment Network for Video Inpainting

Progressive Temporal Feature Alignment Network for Video Inp...

引用

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

作者： Zou, Xueyan Yang, Linjie Liu, Ding Lee, Yong Jae ByteDance Inc Beijing Peoples R China Univ Calif Davis Davis CA 95616 USA

ISBN: (纸本)9781665445092

Video inpainting aims to fill spatio-temporal "corrupted" regions with plausible content. To achieve this goal, it is necessary to find correspondences from neighbouring frames to faithfully hallucinate the unknown content. Current methods achieve this goal through attention, flow-based warping, or 3D temporal convolution. However, flow-based warping can create artifacts when optical flow is not accurate, while temporal convolution may suffer from spatial misalignment. We propose 'Progressive Temporal Feature Alignment Network', which progressively enriches features extracted from the current frame with the feature warped from neighbouring frames using optical flow. Our approach corrects the spatial misalignment in the temporal feature propagation stage, greatly improving visual quality and temporal consistency of the inpainted videos. Using the proposed architecture, we achieve state-of-the-art performance on the DAVIS and FVI datasets compared to existing deep learning approaches. Code is available at https://***/MaureenZOU/TSAM.

关键词： Deep learning Visualization computer vision Three-dimensional displays Image resolution Convolution Feature extraction

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Mixed-Privacy Forgetting in Deep Networks

Mixed-Privacy Forgetting in Deep Networks

引用

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

作者： Golatkar, Aditya Achille, Alessandro Ravichandran, Avinash Polito, Marzia Soatto, Stefano Amazon Web Serv Seattle WA 98109 USA Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781665445092

We show that the influence of a subset of the training samples can be removed - or "forgotten" - from the weights of a network trained on large-scale image classification tasks, and we provide strong computable bounds on the amount of remaining information after forgetting. Inspired by real-world applications of forgetting techniques, we introduce a novel notion of forgetting in mixed-privacy setting, where we know that a "core" subset of the training samples does not need to be forgotten. While this variation of the problem is conceptually simple, we show that working in this setting significantly improves the accuracy and guarantees of forgetting methods applied to vision classification tasks. Moreover, our method allows efficient removal of all information contained in non-core data by simply setting to zero a subset of the weights with minimal loss in performance. We achieve these results by replacing a standard deep network with a suitable linear approximation. With opportune changes to the network architecture and training procedure, we show that such linear approximation achieves comparable performance to the original network and that the forgetting problem becomes quadratic and can be solved efficiently even for large models. Unlike previous forgetting methods on deep networks, ours can achieve close to the state-of-the-art accuracy on large scale vision tasks. In particular, we show that our method allows forgetting without having to trade off the model accuracy.

关键词： Training computer vision Linear approximation Network architecture Data models pattern recognition Task analysis

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Spatio-temporal Contrastive Domain Adaptation for Action recognition

Spatio-temporal Contrastive Domain Adaptation for Action Rec...

引用

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

作者： Song, Xiaolin Zhao, Sicheng Yang, Jingyu Yue, Huanjing Xu, Pengfei Hu, Runbo Chai, Hua Tianjin Univ Tianjin Peoples R China Univ Calif Berkeley Berkeley CA 94720 USA Didi Chuxing Beijing Peoples R China

ISBN: (纸本)9781665445092

Compared with image-based UDA, video-based UDA is comprehensive to bridge the domain shift on both spatial representation and temporal dynamics. Most previous works focus on short-term modeling and alignment with frame-level or clip-level features, which is not discriminative sufficiently for video-based UDA tasks. To address these problems, in this paper we propose to establish the cross-modal domain alignment via self-supervised contrastive framework, i.e., spatio-temporal contrastive domain adaptation (STCDA), to learn the joint clip-level and video-level representation alignment. Since the effective representation is modeled from unlabeled data by self-supervised learning (SSL), spatio-temporal contrastive learning (STCL) is proposed to explore the useful long-term feature representation for classification, using self-supervision setting trained from the contrastive clip/video pairs with positive or negative properties. Besides, we involve a novel domain metric scheme, i.e., video-based contrastive alignment (VCA), to optimize the category-aware video-level alignment and generalization between source and target. The proposed STCDA achieves stat-of-the-art results on several UDA benchmarks for action recognition.

关键词： Measurement Bridges Adaptation models computer vision Benchmark testing Data models pattern recognition

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Cache and Reuse: Rethinking the Efficiency of On-device Transfer Learning

Cache and Reuse: Rethinking the Efficiency of On-device Tran...

引用

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

作者： Yuedong Yang Hung-Yueh Chiang Guihong Li Diana Marculescu Radu Marculescu Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Training only the last few layers in deep neural networks has been considered an effective strategy for enhancing the efficiency of on-device training. Prior work has adopted this approach and focused on accelerating backpropagation. However, by conducting a thorough system-wide analysis, we discover that the primary bottleneck is actually the forward propagation through the frozen layers, rather than backpropagation, if only the last few layers are trained. To address this issue, we introduce the "cache and reuse" idea for on-device transfer learning and propose a two-stage training method, which consists of a cache initialization stage, where we store the output from the frozen layers, followed by a training stage. To make our approach practical, we also propose augmented feature caching and cache compression to address the challenges of non-cacheable feature maps and cache size explosion. We carry out extensive experiments on various models (e.g., convolutional neural network and vision transformers) using real edge devices to demonstrate the effectiveness of our method. As an example, on NVIDIA Jetson Orin NX with MobileNet-V2, our approach boosts the training speed by 6.6 ×, and improves the accuracy by 2.1%. For EfficientNet-b0, our method increases the training speed by 2.2 × and improves its accuracy by 1.3%. Therefore, our approach represents a significant improvement in enabling practical on-device transfer learning for edge devices with limited resources.

关键词： Training Backpropagation computer vision Accuracy conferences Transfer learning Transformers

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