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QuantNAS: Quantization-aware Neural Architecture Search For Efficient Deployment On Mobile Device

QuantNAS: Quantization-aware Neural Architecture Search For ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Tianxiao Gao Li Guo Shanwei Zhao Peihan Xu Yukun Yang Xionghao Liu Shihao Wang Shiai Zhu Dajiang Zhou Ant Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep convolutional networks are increasingly applied in mobile AI scenarios. To achieve efficient deployment, researchers combine neural architecture search (NAS) and quantization to find the best quantized architecture. However, existing methods overlook the on-device implementation of quantization. The searching result is usually sub-optimal or has limited latency reduction. To this end, we propose QuantNAS, a novel quantization-aware NAS based on a two-stage one-shot method. Different from the previous method, our method considers the on-device implementation of the quantized network and searches for the architecture from a fully quantized supernet. During training, we propose a batch-statistics-based strategy to alleviate the non-convergence problem. Besides, a scale predictor is proposed and is jointly trained with the supernet. During search, the scale predictor can provide optimal scale for different subnets without retraining. At different latency levels on Kirin 9000 mobile CPU, the proposed method achieves 1.53%-1.68% Top-1 accuracy improvement on ImageNet 1K dataset and 1.7% mAP improvement.

关键词： Training computer vision Quantization (signal) conferences Computational modeling computer architecture Mobile handsets

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General Multi-label Image Classification with Transformers

General Multi-label Image Classification with Transformers

引用

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

作者： Lanchantin, Jack Wang, Tianlu Ordonez, Vicente Qi, Yanjun Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9781665445092

Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image. In this work we propose the Classification Transformer (C-Tran), a general framework for multi-label image classification that leverages Transformers to exploit the complex dependencies among visual features and labels. Our approach consists of a Transformer encoder trained to predict a set of target labels given an input set of masked labels, and visual features from a convolutional neural network. A key ingredient of our method is a label mask training objective that uses a ternary encoding scheme to represent the state of the labels as positive, negative, or unknown during training. Our model shows state-of-the-art performance on challenging datasets such as COCO and Visual Genome. Moreover, because our model explicitly represents the label state during training, it is more general by allowing us to produce improved results for images with partial or extra label annotations during inference. We demonstrate this additional capability in the COCO, Visual Genome, News-500, and CUB image datasets.

关键词： Training Deep learning Visualization Genomics Transformers Encoding pattern recognition

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NTIRE 2023 Challenge on Image Super-Resolution (×4): Methods and Results

NTIRE 2023 Challenge on Image Super-Resolution (×4): Method...

引用

2023 ieee/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Zhang, Yulun Zhang, Kai Chen, Zheng Li, Yawei Timofte, Radu Zhang, Junpei Zhang, Kexin Peng, Rui Ma, Yanbiao Jiao, Licheng Huang, Huaibo Zhou, Xiaoqiang Ai, Yuang He, Ran Qiu, Yajun Zhu, Qiang Li, Pengfei Li, Qianhui Zhu, Shuyuan Zhang, Dafeng Li, Jia Wang, Fan Li, Chunmiao Kim, TaeHyung Kil, Jungkeong Kim, Eon Yu, Yeonseung Lee, Beomyeol Lee, Subin Lim, Seokjae Chae, Somi Choi, Heungjun Huang, ZhiKai Chen, YiChung Chiang, YuanChun Yang, HaoHsiang Chen, WeiTing Chang, HuaEn Chen, I-Hsiang Hsieh, ChiaHsuan Kuo, SyYen Choi, Ui-Jin Conde, Marcos V. Khowaja, Sunder Ali Yoon, Jiseok Lee, Ik Hyun Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Wei, Jiayu Li, Yanfeng Sun, Jia Cheng, Zhanyi Li, Zhiyuan Yao, Xu Wang, Xinyi Li, Danxu Cui, Xuan Cao, Jun Li, Cheng Zheng, Jianbin Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Computer Vision Lab Eth Zurich Switzerland Shanghai Jiao Tong University China University of Würzburg Germany Xidian University China Mais&cripac Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China University of Science and Technology of China China Beijing Institute of Technology China School of Information Science and Technology ShanghaiTech University China School of Information and Communication Engineering University of Electronic Science and Technology of China China China Lotte Data Communication Company Seoul Korea Republic of Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States MegaStudyEdu Korea Republic of Computer Vision Lab Caidas University of Würzburg Germany University of Sindh Pakistan Iklab Inc. Tech University of Korea Siheung-Si Korea Republic of Micro-Nano Electronics Department Shanghai Jiao Tong University China Electrical Engineering Department Faculty of Engineering Assiut University Egypt Hefei University of Technology China Beijing Jiaotong University China South China University of Technology Guangdong Guangzhou China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on image super-resolution (×4), focusing on the proposed solutions and results. The task of image super-resolution (SR) is to generate a high-resolution (HR) output from a corresponding low-resolution (LR) input by leveraging prior information from paired LR-HR images. The aim of the challenge is to obtain a network design/solution capable to produce high-quality results with the best performance (e.g., PSNR). We want to explore how high performance we can achieve regardless of computational cost (e.g., model size and FLOPs) and data. The track of the challenge was to measure the restored HR images with the ground truth HR images on DIV2K testing dataset. The ranking of the teams is determined directly by the PSNR value. The challenge has attracted 192 registered participants, where 15 teams made valid submissions. They achieve state-of-the-art performance in single image super-resolution. © 2023 ieee.

关键词： Statistical tests

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DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Da...

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

作者： Wolf, Valentin Lugmayr, Andreas Danelljan, Martin Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781665445092

The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications. Current strategies aim to synthesize realistic training data by modeling noise and degradations that appear in real-world settings. We propose DeFlow, a method for learning stochastic image degradations from unpaired data. Our approach is based on a novel unpaired learning formulation for conditional normalizing flows. We model the degradation process in the latent space of a shared flow encoder-decoder network. This allows us to learn the conditional distribution of a noisy image given the clean input by solely minimizing the negative log-likelihood of the marginal distributions. We validate our DeFlow formulation on the task of joint image restoration and super-resolution. The models trained with the synthetic data generated by DeFlow outperform previous learnable approaches on three recent datasets.

关键词： Degradation Superresolution Training data Stochastic processes Data models Image restoration pattern recognition

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Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning

Self-Promoted Prototype Refinement for Few-Shot Class-Increm...

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

作者： Zhu, Kai Cao, Yang Zhai, Wei Cheng, Jie Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. It is a challenging task since representation optimization and prototype reorganization can only be achieved under little supervision. To address this problem, we propose a novel incremental prototype learning scheme. Our scheme consists of a random episode selection strategy that adapts the feature representation to various generated incremental episodes to enhance the corresponding extensibility, and a self-promoted prototype refinement mechanism which strengthens the expression ability of the new classes by explicitly considering the dependencies among different classes. Particularly, a dynamic relation projection module is proposed to calculate the relation matrix in a shared embedding space and leverage it as the factor for bootstrapping the update of prototypes. Extensive experiments on three benchmark datasets demonstrate the above-par incremental performance, outperforming state-of-the-art methods by a margin of 13%, 17% and 11%, respectively.

关键词： computer vision Adaptation models Computational modeling Prototypes Benchmark testing Power capacitors pattern recognition

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Weakly Supervised Video Salient Object Detection

Weakly Supervised Video Salient Object Detection

引用

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

作者： Zhao, Wangbo Zhang, Jing Li, Long Barnes, Nick Liu, Nian Han, Junwei Northwestern Polytech Univ Brain & Artificial Intelligence Lab Xian Peoples R China Australian Natl Univ Canberra ACT Australia CSIRO Canberra ACT Australia Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

Significant performance improvement has been achieved for fully-supervised video salient object detection with the pixel-wise labeled training datasets, which are time-consuming and expensive to obtain. To relieve the burden of data annotation, we present the first weakly supervised video salient object detection model based on relabeled "fixation guided scribble annotations". Specifically, an "Appearance-motion fusion module" and bidirectional ConvLSTM based framework are proposed to achieve effective multi-modal learning and long-term temporal context modeling based on our new weak annotations. Further, we design a novel foreground-background similarity loss to further explore the labeling similarity across frames. A weak annotation boosting strategy is also introduced to boost our model performance with a new pseudo-label generation technique. Extensive experimental results on six benchmark video saliency detection datasets illustrate the effectiveness of our solution(1).

关键词： Training computer vision Annotations Object detection Boosting Data models pattern recognition

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Image Change Captioning by Learning from an Auxiliary Task

Image Change Captioning by Learning from an Auxiliary Task

引用

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

作者： Hosseinzadeh, Mehrdad Wang, Yang Univ Manitoba Winnipeg MB Canada Huawei Technol Canada Markham ON Canada

ISBN: (纸本)9781665445092

We tackle the challenging task of image change captioning. The goal is to describe the subtle difference between two very similar images by generating a sentence caption. While the recent methods mainly focus on proposing new model architectures for this problem, we instead focus on an alternative training scheme. Inspired by the success of multi-task learning, we formulate a training scheme that uses an auxiliary task to improve the training of the change captioning network. We argue that the task of composed query image retrieval is a natural choice as the auxiliary task. Given two almost similar images as the input, the primary network generates a caption describing the fine change between those two images. Next, the auxiliary network is provided with the generated caption and one of those two images. It then tries to pick the second image among a set of candidates. This forces the primary network to generate detailed and precise captions via having an extra supervision loss by the auxiliary network. Furthermore, we propose a new scheme for selecting a negative set of candidates for the retrieval task that can effectively improve the performance. We show that the proposed training strategy performs well on the task of change captioning on benchmark datasets.

关键词： Training computer vision Image color analysis Image retrieval Semantics Benchmark testing pattern recognition

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Leveraging Large Language Models for Multimodal Search

Leveraging Large Language Models for Multimodal Search

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ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Oriol Barbany Michael Huang Xinliang Zhu Arnab Dhua CSIC-UPC Institut de Robòtica i Informàtica Industrial Visual Search & AR Amazon

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal search has become increasingly important in providing users with a natural and effective way to express their search intentions. Images offer fine-grained details of the desired products, while text allows for easily incorporating search modifications. However, some existing multimodal search systems are unreliable and fail to address simple queries. The problem becomes harder with the large variability of natural language text queries, which may contain ambiguous, implicit, and irrelevant information. Addressing these issues may require systems with enhanced matching capabilities, reasoning abilities, and context-aware query parsing and rewriting. This paper introduces a novel multimodal search model that achieves a new performance milestone on the Fashion200K dataset [19]. Additionally, we propose a novel search interface integrating Large Language Models (LLMs) to facilitate natural language interaction. This interface routes queries to search systems while conversationally engaging with users and considering previous searches. When coupled with our multimodal search model, it heralds a new era of shopping assistants capable of offering human-like interaction and enhancing the overall search experience.

关键词： computer vision Large language models conferences Natural languages Pipelines Image retrieval computer architecture

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Data-Efficient and Robust Task Selection for Meta-Learning

Data-Efficient and Robust Task Selection for Meta-Learning

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Donglin Zhan James Anderson Columbia University New York NY

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Meta-learning methods typically learn tasks under the assumption that all tasks are equally important. However, this assumption is often not valid. In real-world applications, tasks can vary both in their importance during different training stages and in whether they contain noisy labeled data or not, making a uniform approach suboptimal. To address these issues, we propose the Data-Efficient and Robust Task Selection (DERTS) algorithm, which can be incorporated into both gradient and metric-based meta-learning algorithms. DERTS selects weighted subsets of tasks from task pools by minimizing the approximation error of the full gradient of task pools in the meta-training stage. The selected tasks are efficient for rapid training and robust towards noisy label scenarios. Unlike existing algorithms, DERTS does not require any architecture modification for training and can handle noisy label data in both the support and query sets. Analysis of DERTS shows that the algorithm follows similar training dynamics as learning on the full task pools. Experiments show that DERTS outperforms existing sampling strategies for meta-learning on both gradient-based and metric-based meta-learning algorithms in limited data budget and noisy task settings.

关键词： Metalearning Training computer vision Heuristic algorithms conferences computer architecture Approximation algorithms

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NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions

NExT-QA: Next Phase of Question-Answering to Explaining Temp...

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

作者： Xiao, Junbin Shang, Xindi Yao, Angela Chua, Tat-Seng Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (纸本)9781665445092

We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Based on the dataset, we set up multi-choice and open-ended QA tasks targeting causal action reasoning, temporal action reasoning, and common scene comprehension. Through extensive analysis of baselines and established VideoQA techniques, we find that top-performing methods excel at shallow scene descriptions but are weak in causal and temporal action reasoning. Furthermore, the models that are effective on multi-choice QA, when adapted to open-ended QA, still struggle in generalizing the answers. This raises doubt on the ability of these models to reason and highlights possibilities for improvement. With detailed results for different question types and heuristic observations for future works, we hope NExT-QA will guide the next generation of VQA research to go beyond superficial description towards a deeper understanding of videos. (The dataset and related resources are available at https://***/doc-doc/***)

关键词： Adaptation models computer vision Benchmark testing Knowledge discovery Cognition pattern recognition Task analysis

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