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Cyclical Pruning for Sparse Neural Networks

Cyclical Pruning for Sparse Neural Networks

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

作者： Srinivas, Suraj Kuzmin, Andrey Nagel, Markus van Baalen, Mart Skliar, Andrii Blankevoort, Tijmen Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland Qualcomm AI Res Amsterdam Netherlands

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Current methods for pruning neural network weights iteratively apply magnitude-based pruning on the model weights and re-train the resulting model to recover lost accuracy. In this work, we show that such strategies do not allow for the recovery of erroneously pruned weights. To enable weight recovery, we propose a simple strategy called cyclical pruning which requires the pruning schedule to be periodic and allows for weights pruned erroneously in one cycle to recover in subsequent ones. Experimental results on both linear models and large-scale deep neural networks show that cyclical pruning outperforms existing pruning algorithms, especially at high sparsity ratios. Our approach is easy to tune and can be readily incorporated into existing pruning pipelines to boost performance.

关键词： Deep learning Schedules computer vision conferences Computational modeling Neural networks Pipelines

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HumanNeRF: Efficiently Generated Human Radiance Field from Sparse Inputs

HumanNeRF: Efficiently Generated Human Radiance Field from S...

引用

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

作者： Zhao, Fuqiang Yang, Wei Zhang, Jiakai Lin, Pei Zhang, Yingliang Yu, Jingyi Xu, Lan ShanghaiTech Univ Shanghai Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China DGene Baton Rouge LA USA Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent neural human representations can produce high-quality multi-view rendering but require using dense multiview inputs and costly training. They are hence largely limited to static models as training each frame is infeasible. We present HumanNeRF - a neural representation with efficient generalization ability - for high-fidelity free-view synthesis of dynamic humans. Analogous to how IBRNet assists NeRF by avoiding per-scene training, HumanNeRF employs an aggregated pixel-alignment feature across multiview inputs along with a pose embedded non-rigid deformation field for tackling dynamic motions. The raw HumanNeRF can already produce reasonable rendering on sparse video inputs of unseen subjects and camera settings. To further improve the rendering quality, we augment our solution with in-hour scene-specific fine-tuning, and an appearance blending module for combining the benefits of both neural volumetric rendering and neural texture blending. Extensive experiments on various multi-view dynamic human datasets demonstrate effectiveness of our approach in synthesizing photo-realistic free-view humans under challenging motions and with very sparse camera view inputs.

关键词： Training computer vision Telepresence Dynamics Entertainment industry Rendering (computer graphics) Cameras

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Pano3D: A Holistic Benchmark and a Solid Baseline for 360° Depth Estimation

Pano3D: A Holistic Benchmark and a Solid Baseline for 360° ...

引用

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

作者： Albanis, Georgios Zioulis, Nikolaos Drakoulis, Petros Gkitsas, Vasileios Sterzentsenko, Vladimiros Alvarez, Federico Zarpalas, Dimitrios Daras, Petros Ctr Res & Technol Hellas Thessaloniki Greece Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9781665448994

Pano3D is a new benchmark for depth estimation from spherical panoramas. It aims to assess performance across all depth estimation traits, the primary direct depth estimation performance targeting precision and accuracy, and also the secondary traits, boundary preservation and smoothness. Moreover, Pano3D moves beyond typical intra-dataset evaluation to inter-dataset performance assessment. By disentangling the capacity to generalize in unseen data into different test splits, Pano3D represents a holistic benchmark for 360 degrees depth estimation. We use it as a basis for an extended analysis seeking to offer insights into classical choices for depth estimation. This results into a solid baseline for panoramic depth that followup works can built upon to steer future progress.

关键词： computer vision conferences Estimation Benchmark testing Solids pattern recognition

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MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions

MotionRNN: A Flexible Model for Video Prediction with Spacet...

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

作者： Wu, Haixu Yao, Zhiyu Wang, Jianmin Long, Mingsheng Tsinghua Univ Sch Software BNRist Beijing Peoples R China

ISBN: (纸本)9781665445092

This paper tackles video prediction from a new dimension of predicting spacetime-varying motions that are incessantly changing across both space and time. Prior methods mainly capture the temporal state transitions but overlook the complex spatiotemporal variations of the motion itself, making them difficult to adapt to ever-changing motions. We observe that physical world motions can be decomposed into transient variation and motion trend, while the latter can be regarded as the accumulation of previous motions. Thus, simultaneously capturing the transient variation and the motion trend is the key to make spacetime-varying motions more predictable. Based on these observations, we propose the MotionRNN framework, which can capture the complex variations within motions and adapt to spacetime-varying scenarios. MotionRNN has two main contributions. The first is that we design the MotionGRU unit, which can model the transient variation and motion trend in a unified way. The second is that we apply the MotionGRU to RNN-based predictive models and indicate a new flexible video prediction architecture with a Motion Highway, which can significantly improve the ability to predict changeable motions and avoid motion vanishing for stacked multiple-layer predictive models. With high flexibility, this framework can adapt to a series of models for deterministic spatiotemporal prediction. Our MotionRNN can yield significant improvements on three challenging benchmarks for video prediction with spacetime-varying motions.

关键词： Road transportation Adaptation models computer vision computer architecture Predictive models Market research Spatiotemporal phenomena

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TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing

TVConv: Efficient Translation Variant Convolution for Layout...

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

作者： Chen, Jierun He, Tianlang Zhuo, Weipeng Ma, Li Ha, Sangtae Chan, S-H Gary Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Colorado Boulder Boulder CO USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A convolution has empowered many smart applications, dynamic convolution further equips it with the ability to adapt to diverse inputs. However, the static and dynamic convolutions are either layout-agnostic or computation-heavy, making it inappropriate for layout-specific applications, e.g., face recognition and medical image segmentation. We observe that these applications naturally exhibit the characteristics of large intra-image (spatial) variance and small cross-image variance. This observation motivates our efficient translation variant convolution (TVConv) for layout-aware visual processing. Technically, TVConv is composed of affinity maps and a weight-generating block. While affinity maps depict pixel-paired relationships gracefully, the weight-generating block can be explicitly overparameterized for better training while maintaining efficient inference. Although conceptually simple, TVConv significantly improves the efficiency of the convolution and can be readily plugged into various network architectures. Extensive experiments on face recognition show that TVConv reduces the computational cost by up to 3.1x and improves the corresponding throughput by 2.3x while maintaining a high accuracy compared to the depthwise convolution. Moreover, for the same computation cost, we boost the mean accuracy by up to 4.21%. We also conduct experiments on the optic disc/cup segmentation task and obtain better generalization performance, which helps mitigate the critical data scarcity issue. Code is available at https://***/JierunChen/TVConv.

关键词： Training Visualization Convolution Face recognition Microscopy computer architecture Network architecture

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REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments

REVERIE: Remote Embodied Visual Referring Expression in Real...

引用

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

作者： Qi, Yuankai Wu, Qi Anderson, Peter Wang, Xin Wang, William Yang Shen, Chunhua van den Hengel, Anton Univ Adelaide Australia Ctr Robot Vis Adelaide SA Australia Harbin Inst Technol Weihai Peoples R China Georgia Inst Technol Atlanta GA USA Univ Calif Santa Barbara Santa Barbara CA 93106 USA Google Mountain View CA USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171685

One of the long-term challenges of robotics is to enable robots to interact with humans in the visual world via natural language, as humans are visual animals that communicate through language. Overcoming this challenge requires the ability to perform a wide variety of complex tasks in response to multifarious instructions from humans. In the hope that it might drive progress towards more flexible and powerful human interactions with robots, we propose a dataset of varied and complex robot tasks, described in natural language, in terms of objects visible in a large set of real images. Given an instruction, success requires navigating through a previously-unseen environment to identify an object. This represents a practical challenge, but one that closely reflects one of the core visual problems in robotics. Several state-of-the-art vision-and-language navigation, and referring-expression models are tested to verify the difficulty of this new task, but none of them show promising results because there are many fundamental differences between our task and previous ones. A novel Interactive Navigator-Pointer model is also proposed that provides a strong baseline on the task. The proposed model especially achieves the best performance on the unseen test split, but still leaves substantial room for improvement compared to the human performance. Repository: https: //***/YuankaiQi/REVERIE

关键词： Task analysis Navigation Robots Natural languages Visualization Object recognition Indoor environments

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Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer

Not All Tokens Are Equal: Human-centric Visual Analysis via ...

引用

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

作者： Zeng, Wang Jin, Sheng Liu, Wentao Qian, Chen Luo, Ping Ouyang, Wanli Wang, Xiaogang Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China SenseTime Res & Tetras AI Hangzhou Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

vision transformers have achieved great successes in many computer vision tasks. Most methods generate vision tokens by splitting an image into a regular and fixed grid and treating each cell as a token. However, not all regions are equally important in human-centric vision tasks, e.g., the human body needs a fine representation with many tokens, while the image background can be modeled by a few tokens. To address this problem, we propose a novel vision Transformer, called Token Clustering Transformer (TCFormer), which merges tokens by progressive clustering, where the tokens can be merged from different locations with flexible shapes and sizes. The tokens in TCFormer can not only focus on important areas but also adjust the token shapes to fit the semantic concept and adopt a fine resolution for regions containing critical details, which is beneficial to capturing detailed information. Extensive experiments show that TCFormer consistently outperforms its counterparts on different challenging human-centric tasks and datasets, including whole-body pose estimation on COCO-WholeBody and 3D human mesh reconstruction on 3DPW. Code is available at https://***/zengwang430521/***.

关键词： computer vision Visualization Three-dimensional displays Shape Pose estimation Semantics computer architecture

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Improving OCR-based Image Captioning by Incorporating Geometrical Relationship

Improving OCR-based Image Captioning by Incorporating Geomet...

引用

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

作者： Wang, Jing Tang, Jinhui Yang, Mingkun Bai, Xiang Luo, Jiebo Nanjing Univ Sci & Technol Nanjing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9781665445092

OCR-based image captioning aims to automatically describe images based on all the visual entities (both visual objects and scene text) in images. Compared with conventional image captioning, the reasoning of scene text is required for OCR-based image captioning since the generated descriptions often contain multiple OCR tokens. Existing methods attempt to achieve this goal via encoding the OCR tokens with rich visual and semantic representations. However, strong correlations between OCR tokens may not be established with such limited representations. In this paper, we propose to enhance the connections between OCR tokens from the viewpoint of exploiting the geometrical relationship. We comprehensively consider the height, width, distance, IoU and orientation relations between the OCR tokens for constructing the geometrical relationship. To integrate the learned relation as well as the visual and semantic representations into a unified framework, a Long Short-Term Memory plus Relation-aware pointer network (LSTM-R) architecture is presented in this paper. Under the guidance of the geometrical relationship between OCR tokens, our LSTM-R capitalizes on a newly-devised relation-aware pointer network to select OCR tokens from the scene text for OCR-based image captioning. Extensive experiments demonstrate the effectiveness of our LSTM-R. More remarkably, LSTM-R achieves state-of-the-art performance on TextCaps, with the CIDEr-D score being increased from 98.0% to 109.3%.

关键词： Training Visualization computer vision Correlation Computational modeling Semantics computer architecture

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Prototype Completion with Primitive Knowledge for Few-Shot Learning

Prototype Completion with Primitive Knowledge for Few-Shot L...

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

作者： Zhang, Baoquan Li, Xutao Ye, Yunming Huang, Zhichao Zhang, Lisai Harbin Inst Technol Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Few-shot learning is a challenging task, which aims to learn a classifier for novel classes with few examples. Pretraining based meta-learning methods effectively tackle the problem by pre-training a feature extractor and then fine-tuning it through the nearest centroid based meta-learning. However, results show that the fine-tuning step makes very marginal improvements. In this paper, 1) we figure out the key reason, i.e., in the pre-trained feature space, the base classes already form compact clusters while novel classes spread as groups with large variances, which implies that fine-tuning the feature extractor is less meaningful;2) instead of fine-tuning the feature extractor, we focus on estimating more representative prototypes during meta-learning. Consequently, we propose a novel prototype completion based meta-learning framework. This framework first introduces primitive knowledge (i.e., class-level part or attribute annotations) and extracts representative attribute features as priors. Then, we design a prototype completion network to learn to complete prototypes with these priors. To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples. Extensive experiments show that our method: (i) can obtain more accurate prototypes;(ii) outperforms state-of-the-art techniques by 2% similar to 9% in terms of classification accuracy. Our code is available online(1).

关键词： Knowledge engineering computer vision Codes Annotations Computational modeling Prototypes Feature extraction

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MDAN: Multi-level Dependent Attention Network for Visual Emotion Analysis

MDAN: Multi-level Dependent Attention Network for Visual Emo...

引用

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

作者： Xu, Liwen Wang, Zhengtao Wu, Bin Lui, Simon Tencent Mus Entertainment Innovat Technol Ctr Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Visual Emotion Analysis (VEA) is attracting increasing attention. One of the biggest challenges of VEA is to bridge the affective gap between visual clues in a picture and the emotion expressed by the picture. As the granularity of emotions increases, the affective gap increases as well. Existing deep approaches try to bridge the gap by directly learning discrimination among emotions globally in one shot. They ignore the hierarchical relationship among emotions at different affective levels, and the variation in the affective level of emotions to be classified. In this paper, we present the multi-level dependent attention network (MDAN) with two branches to leverage the emotion hierarchy and the correlation between different affective levels and semantic levels. The bottom-up branch directly learns emotions at the highest affective level and largely prevents hierarchy violation by explicitly following the emotion hierarchy while predicting emotions at lower affective levels. In contrast, the top-down branch aims to disentangle the affective gap by one-to-one mapping between semantic levels and affective levels, namely, Affective Semantic Mapping. A local classifier is appended at each semantic level to learn discrimination among emotions at the corresponding affective level. Then, we integrate global learning and local learning into a unified deep framework and optimize it simultaneously. Moreover, to properly model channel dependencies and spatial attention while disentangling the affective gap, we carefully designed two attention modules: the Multi-head Cross Channel Attention module and the Level-dependent Class Activation Map module. Finally, the proposed deep frame-work obtains new state-of-the-art performance on six VEA benchmarks, where it outperforms existing state-of-the-art methods by a large margin, e.g., +3.85% on the WEBEmo dataset at 25 classes classification accuracy.

关键词： Bridges Visualization computer vision Correlation Computational modeling Semantics Benchmark testing

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