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Learning Imbalanced Data with vision Transformers

Learning Imbalanced Data with Vision Transformers

引用

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

作者： Xu, Zhengzhuo Liu, Ruikang Yang, Shuo Chai, Zenghao Yuan, Chun Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China

ISBN: (纸本)9798350301298

The real-world data tends to be heavily imbalanced and severely skew the data-driven deep neural networks, which makes Long-Tailed recognition (LTR) a massive challenging task. Existing LTR methods seldom train vision Transformers (ViTs) with Long-Tailed (LT) data, while the off-the-shelf pretrain weight of ViTs always leads to unfair comparisons. In this paper, we systematically investigate the ViTs' performance in LTR and propose LiVT to train ViTs from scratch only with LT data. With the observation that ViTs suffer more severe LTR problems, we conduct Masked Generative Pretraining (MGP) to learn generalized features. With ample and solid evidence, we show that MGP is more robust than supervised manners. Although Binary Cross Entropy (BCE) loss performs well with ViTs, it struggles on the LTR tasks. We further propose the balanced BCE to ameliorate it with strong theoretical groundings. Specially, we derive the unbiased extension of Sigmoid and compensate extra logit margins for deploying it. Our Bal-BCE contributes to the quick convergence of ViTs in just a few epochs. Extensive experiments demonstrate that with MGP and Bal-BCE, LiVT successfully trains ViTs well without any additional data and outperforms comparable state-of-the-art methods significantly, e.g., our ViT-B achieves 81.0% Top-1 accuracy in iNaturalist 2018 without bells and whistles. Code is available at https://***/XuZhengzhuo/LiVT.

关键词： continual low-shot meta or long-tail learning Transfer

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MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and Results

MIPI 2023 Challenge on RGB+ToF Depth Completion: Methods and...

引用

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

作者： Zhu, Qingpeng Sun, Wenxiu Dai, Yuekun Li, Chongyi Zhou, Shangchen Feng, Ruicheng Sun, Qianhui Loy, Chen Change Gu, Jinwei Yu, Yi Huang, Yangke Zhang, Kang Chen, Meiya Wang, Yu Li, Yongchao Jiang, Hao Muduli, Amrit Kumar Kumar, Vikash Swami, Kunal Bajpai, Pankaj Kumar Ma, Yunchao Xiao, Jiaojun Ling, Zhi SenseTime Research and Tetras.AI Nanyang Technological University Singapore SenseBrain Mgtv China Xiaomi Inc. China Samsung R & D Institute India - Bangalore India

ISBN: (纸本)9798350302493

Depth completion from RGB images and sparse Time-of-Flight (ToF) measurements is an important problem in computer vision and robotics. While traditional methods for depth completion have relied on stereo vision or structured light techniques, recent advances in deep learning have enabled more accurate and efficient completion of depth maps from RGB images and sparse ToF measurements. To evaluate the performance of different depth completion methods, we organized an RGB+sparse ToF depth completion competition. The competition aimed to encourage research in this area by providing a standardized dataset and evaluation metrics to compare the accuracy of different approaches. In this report, we present the results of the competition and analyze the strengths and weaknesses of the top-performing methods. We also discuss the implications of our findings for future research in RGB+sparse ToF depth completion. We hope that this competition and report will help to advance the state-of-the-art in this important area of research. More details of this challenge and the link to the dataset can be found at https://***/MIPI2023/. © 2023 ieee.

关键词： Stereo vision

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Activating More Pixels in Image Super-Resolution Transformer

Activating More Pixels in Image Super-Resolution Transformer

引用

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

作者： Chen, Xiangyu Wang, Xintao Zhou, Jiantao Qiao, Yu Dong, Chao Univ Macau State Key Lab Internet Things Smart City Zhuhai Peoples R China Chinese Acad Sci Shenzhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Tencent PCG ARC Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better reconstruction, we propose a novel Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement. Extensive experiments show the effectiveness of the proposed modules, and we further scale up the model to demonstrate that the performance of this task can be greatly improved. Our overall method significantly outperforms the state-of-the-art methods by more than 1dB.

关键词： Low-level vision

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Uni-Perceiver v2: A Generalist Model for Large-Scale vision and vision-Language Tasks

Uni-Perceiver v2: A Generalist Model for Large-Scale Vision ...

引用

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

作者： Li, Hao Zhu, Jinguo Jiang, Xiaohu Zhu, Xizhou Li, Hongsheng Yuan, Chun Wang, Xiaohua Qiao, Yu Wang, Xiaogang Wang, Wenhai Dai, Jifeng Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Xi An Jiao Tong Univ Xian Peoples R China Tsinghua Univ SIGS Beijing Peoples R China SenseTime Res Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Despite the remarkable success of foundation models, their task-specific fine-tuning paradigm makes them inconsistent with the goal of general perception modeling. The key to eliminating this inconsistency is to use generalist models for general task modeling. However, existing attempts at generalist models are inadequate in both versatility and performance. In this paper, we propose UniPerceiver v2, which is the first generalist model capable of handling major large-scale vision and vision-language tasks with competitive performance. Specifically, images are encoded as general region proposals, while texts are encoded via a Transformer-based language model. The encoded representations are transformed by a task-agnostic decoder. Different tasks are formulated as a unified maximum likelihood estimation problem. We further propose an effective optimization technique named Task-Balanced Gradient Normalization to ensure stable multi-task learning with an unmixed sampling strategy, which is helpful for tasks requiring large batch-size training. After being jointly trained on various tasks, Uni-Perceiver v2 is capable of directly handling downstream tasks without any task-specific adaptation. Results show that Uni-Perceiver v2 outperforms all existing generalist models in both versatility and performance. Meanwhile, compared with the commonly-recognized strong baselines that require tasks-specific finetuning, Uni-Perceiver v2 achieves competitive performance on a broad range of vision and vision-language tasks.

关键词： detection recognition: Categorization retrieval

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Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pods

Transfer Learning from Synthetic In-vitro Soybean Pods Datas...

引用

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

作者： Yang, Si Zheng, Lihua Chen, Xieyuanli Zabawa, Laura Zhang, Man Wang, Minjuan China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China China Agr Univ Minist Educ Key Lab Smart Agr Syst Beijing Peoples R China Univ Bonn Inst Geodesy & Geoinformat Bonn Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The mature soybean plants are of complex architecture with pods frequently touching each other, posing a challenge for in-situ segmentation of on-branch soybean pods. Deep learning-based methods can achieve accurate training and strong generalization capabilities, but it demands massive labeled data, which is often a limitation, especially for agricultural applications. As lacking the labeled data to train an in-situ segmentation model for on-branch soybean pods, we propose a transfer learning from synthetic in-vitro soybean pods. First, we present a novel automated image generation method to rapidly generate a synthetic in-vitro soybean pods dataset with plenty of annotated samples. The in-vitro soybean pods samples are overlapped to simulate the frequently physically touching of on-branch soybean pods. Then, we design a two-step transfer learning. In the first step, we finetune an instance segmentation network pretrained by a source domain (MS COCO dataset) with a synthetic target domain (in-vitro soybean pods dataset). In the second step, transferring from simulation to reality is performed by finetuning on a few real-world mature soybean plant samples. The experimental results show the effectiveness of the proposed two-step transfer learning method, such that AP50 was 0.80 for the real-world mature soybean plant test dataset, which is higher than that of direct adaptation and its AP50 was 0.77. Furthermore, the visualizations of in-situ segmentation results of on-branch soybean pods show that our method performs better than other methods, especially when soybean pods overlap densely.

关键词： Deep learning Transfer learning computer vision Instance segmentation Plant phenotyping

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MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

引用

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

作者： Nair, Saeejith Abbasi, Saad Wong, Alexander Shafiee, Mohammad Javad Univ Waterloo Waterloo ON Canada DarwinAI Waterloo ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) has enabled automatic discovery of more efficient neural network architectures, especially for mobile and embedded vision applications. Although recent research has proposed ways of quickly estimating latency on unseen hardware devices with just a few samples, little focus has been given to the challenges of estimating latency on runtimes using optimized graphs, such as TensorRT and specifically for edge devices. As devices like NVIDIA's Jetsons get more popular in embedded computing and robotics, we observe a pressing need to more accurately estimate inference latency of neural network architectures on diverse runtimes, including highly optimized ones. In this work, we propose MAPLE-Edge, an edge device-oriented extension of MAPLE, the state-of-the-art latency predictor for general purpose hardware, where we train a regression network on architecture-latency pairs in conjunction with a hardware-runtime descriptor to effectively estimate latency on a diverse pool of edge devices. Compared to MAPLE, MAPLE-Edge can describe the runtime and target device platform using a much smaller set of CPU performance counters that are widely available on all Linux kernels, while still achieving up to +49.6% accuracy gains against previous state-of-the-art baseline methods on optimized edge device runtimes, using just 10 measurements from an unseen target device. We also demonstrate that unlike MAPLE which performs best when trained on a pool of devices sharing a common runtime, MAPLE-Edge can effectively generalize across runtimes by applying a trick of normalizing performance counters by the operator latency, in the measured hardware-runtime descriptor. Lastly, we show that for runtimes exhibiting lower than desired accuracy, performance can be boosted by collecting additional samples from the target device, with an extra 90 samples translating to gains of nearly +40%.

关键词： Performance evaluation Training Runtime Linux Neural networks computer architecture Pressing

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Slide-Transformer: Hierarchical vision Transformer with Local Self-Attention

Slide-Transformer: Hierarchical Vision Transformer with Loca...

引用

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

作者： Pan, Xuran Ye, Tianzhu Xia, Zhuofan Song, Shiji Huang, Gao Tsinghua Univ BNRist Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350301298

Self-attention mechanism has been a key factor in the recent progress of vision Transformer (ViT), which enables adaptive feature extraction from global contexts. However, existing self-attention methods either adopt sparse global attention or window attention to reduce the computation complexity, which may compromise the local feature learning or subject to some handcrafted designs. In contrast, local attention, which restricts the receptive field of each query to its own neighboring pixels, enjoys the benefits of both convolution and self-attention, namely local inductive bias and dynamic feature selection. Nevertheless, current local attention modules either use inefficient Im2Col function or rely on specific CUDA kernels that are hard to generalize to devices without CUDA support. In this paper, we propose a novel local attention module, Slide Attention, which leverages common convolution operations to achieve high efficiency, flexibility and generalizability. Specifically, we first re-interpret the column-based Im2Col function from a new row-based perspective and use Depthwise Convolution as an efficient substitution. On this basis, we propose a deformed shifting module based on the re-parameterization technique, which further relaxes the fixed key/value positions to deformed features in the local region. In this way, our module realizes the local attention paradigm in both efficient and flexible manner. Extensive experiments show that our slide attention module is applicable to a variety of advanced vision Transformer models and compatible with various hardware devices, and achieves consistently improved performances on comprehensive benchmarks.

关键词： Deep learning architectures and techniques

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Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting

Towards High-Quality and Efficient Video Super-Resolution vi...

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

作者： Li, Gen Ji, Jie Qin, Minghai Niu, Wei Ren, Bin Afghah, Fatemeh Guo, Linke Ma, Xiaolong Clemson Univ Clemson SC 29631 USA William & Mary Williamsburg VA USA

ISBN: (纸本)9798350301298

As deep convolutional neural networks (DNNs) are widely used in various fields of computer vision, leveraging the overfitting ability of the DNN to achieve video resolution upscaling has become a new trend in the modern video delivery system. By dividing videos into chunks and overfitting each chunk with a super-resolution model, the server encodes videos before transmitting them to the clients, thus achieving better video quality and transmission efficiency. However, a large number of chunks are expected to ensure good overfitting quality, which substantially increases the storage and consumes more bandwidth resources for data transmission. On the other hand, decreasing the number of chunks through training optimization techniques usually requires high model capacity, which significantly slows down execution speed. To reconcile such, we propose a novel method for high-quality and efficient video resolution upscaling tasks, which leverages the spatial-temporal information to accurately divide video into chunks, thus keeping the number of chunks as well as the model size to minimum. Additionally, we advance our method into a single overfitting model by a data-aware joint training technique, which further reduces the storage requirement with negligible quality drop. We deploy our models on an off-the-shelf mobile phone, and experimental results show that our method achieves real-time video super-resolution with high video quality. Compared with the state-of-the-art, our method achieves 28 fps streaming speed with 41.6 PSNR, which is 14x faster and 2.29 dB better in the live video resolution upscaling tasks. Code available in https://***/coulsonlee/***.

关键词： Efficient and scalable vision

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Improving vision-and-Language Navigation by Generating Future-View Image Semantics

Improving Vision-and-Language Navigation by Generating Futur...

引用

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

作者： Li, Jialu Bansal, Mohit UNC Chapel Hill Chapel Hill NC 27599 USA

ISBN: (纸本)9798350301298

vision-and-Language Navigation (VLN) is the task that requires an agent to navigate through the environment based on natural language instructions. At each step, the agent takes the next action by selecting from a set of navigable locations. In this paper, we aim to take one step further and explore whether the agent can benefit from generating the potential future view during navigation. Intuitively, humans will have an expectation of how the future environment will look like, based on the natural language instructions and surrounding views, which will aid correct navigation. Hence, to equip the agent with this ability to generate the semantics of future navigation views, we first propose three proxy tasks during the agent's in-domain pre-training: Masked Panorama Modeling (MPM), Masked Trajectory Modeling (MTM), and Action Prediction with Image Generation (APIG). These three objectives teach the model to predict missing views in a panorama (MPM), predict missing steps in the full trajectory (MTM), and generate the next view based on the full instruction and navigation history (APIG), respectively. We then fine-tune the agent on the VLN task with an auxiliary loss that minimizes the difference between the view semantics generated by the agent and the ground truth view semantics of the next step. Empirically, our VLN-SIG achieves the new state-of-the-art on both Room-to-Room dataset and CVDN dataset. We further show that our agent learns to fill in missing patches in future views qualitatively, which brings more interpretability over agents' predicted actions. Lastly, we demonstrate that learning to predict future view semantics also enables the agent to have better performance on longer paths.

关键词： language reasoning vision

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Dynamic Generative Targeted Attacks with pattern Injection

Dynamic Generative Targeted Attacks with Pattern Injection

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

作者： Feng, Weiwei Xu, Nanqing Zhang, Tianzhu Zhang, Yongdong Univ Sci & Technol China Hefei Peoples R China Deep Space Explorat Lab Hefei Peoples R China

ISBN: (纸本)9798350301298

Adversarial attacks can evaluate model robustness and have been of great concern in recent years. Among various attacks, targeted attacks aim at misleading victim models to output adversary-desired predictions, which are more challenging and threatening than untargeted ones. Existing targeted attacks can be roughly divided into instance-specific and instance-agnostic attacks. Instance-specific attacks craft adversarial examples via iterative gradient updating on the specific instance. In contrast, instance-agnostic attacks learn a universal perturbation or a generative model on the global dataset to perform attacks. However, they rely too much on the classification boundary of substitute models, ignoring the realistic distribution of the target class, which may result in limited targeted attack performance. And there is no attempt to simultaneously combine the information of the specific instance and the global dataset. To deal with these limitations, we first conduct an analysis via a causal graph and propose to craft transferable targeted adversarial examples by injecting target patterns. Based on this analysis, we introduce a generative attack model composed of a cross-attention guided convolution module and a pattern injection module. Concretely, the former adopts a dynamic convolution kernel and a static convolution kernel for the specific instance and the global dataset, respectively, which can inherit the advantages of both instance-specific and instance-agnostic attacks. And the pattern injection module utilizes a pattern prototype to encode target patterns, which can guide the generation of targeted adversarial examples. Besides, we also provide rigorous theoretical analysis to guarantee the effectiveness of our method. Extensive experiments demonstrate that our method shows superior performance than 10 existing adversarial attacks against 13 models.

关键词： Adversarial attack and defense

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