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DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

DN-DETR: Accelerate DETR Training by Introducing Query DeNoi...

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

作者： Li, Feng Zhang, Hao Liu, Shilong Guo, Jian Ni, Lionel M. Zhang, Lei Hong Kong Univ Sci & Technol Hong Kong Peoples R China Int Digital Econ Acad IDEA Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Peoples R China IDEA Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present in this paper a novel denoising training method to speedup DETR (DEtection TRansformer) training and offer a deepened understanding of the slow convergence issue of DETR-like methods. We show that the slow convergence results from the instability of bipartite graph matching which causes inconsistent optimization goals in early training stages. To address this issue, except for the Hungarian loss, our method additionally feeds ground-truth bounding boxes with noises into Transformer decoder and trains the model to reconstruct the original boxes, which effectively reduces the bipartite graph matching difficulty and leads to a faster convergence. Our method is universal and can be easily plugged into any DETR-like methods by adding dozens of lines of code to achieve a remarkable improvement. As a result, our DN-DETR results in a remarkable improvement (+1.9AP) under the same setting and achieves the best result (AP 43.4 and 48.6 with 12 and 50 epochs of training respectively) among DETR-like methods with ResNet-50 backbone. Compared with the baseline under the same setting, DN-DETR achieves comparable performance with 50% training epochs. Code is available at https://***/FengLi-ust/DN-DETR.

关键词： Training Codes Machine vision Noise reduction Transformers pattern recognition Decoding

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Instance-aware Image and Sentence Matching with Selective Multimodal LSTM 30

Instance-aware Image and Sentence Matching with Selective Mu...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huang, Yan Wang, Wei Wang, Liang Natl Lab Pattern Recognit Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Ctr Excellence Brain Sci & Intelligence Tech Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781538604571

Effective image and sentence matching depends on how to well measure their global visual-semantic similarity. Based on the observation that such a global similarity arises from a complex aggregation of multiple local similarities between pairwise instances of image (objects) and sentence (words), we propose a selective multimodal Long Short-Term Memory network (sm-LSTM) for instance-aware image and sentence matching. The sm-LSTM includes a multimodal context-modulated attention scheme at each timestep that can selectively attend to a pair of instances of image and sentence, by predicting pairwise instance-aware saliency maps for image and sentence. For selected pairwise instances, their representations are obtained based on the predicted saliency maps, and then compared to measure their local similarity. By similarly measuring multiple local similarities within a few timesteps, the sm-LSTM sequentially aggregates them with hidden states to obtain a final matching score as the desired global similarity. Extensive experiments show that our model can well match image and sentence with complex content, and achieve the state-of-the-art results on two public benchmark datasets.

关键词： Dogs Roads Image color analysis Aggregates Detectors pattern recognition

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Deep Embedding Learning with Discriminative Sampling Policy 32

Deep Embedding Learning with Discriminative Sampling Policy

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

作者： Duan, Yueqi Chen, Lei Lu, Jiwen Zhou, Jie Tsinghua Univ Dept Automat Beijing Peoples R China State Key Lab Intelligent Technol & Syst Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728132938

Deep embedding learning aims to learn a distance metric for effective similarity measurement, which has achieved promising performance in various tasks. As the vast majority of training samples produce gradients with magnitudes close to zero, hard example mining is usually employed to improve the effectiveness and efficiency of the training procedure. However, most existing sampling methods are designed by hand, which ignores the dependence between examples and suffer from exhaustive searching. In this paper, we propose a deep embedding with discriminative sampling policy (DE-DSP) learning framework by simultaneously training two models: a deep sampler network that learns effective sampling strategies, and a feature embedding that maps samples to the feature space. Rather than exhaustively calculating the hardness of all the examples for mining through forward-propagation, the deep sampler network exploits the strong prior of relations among samples to learn discriminative sampling policy in an more efficient manner. Experimental results demonstrate faster convergence and stronger discriminative power of our DE-DSP framework under different embedding objectives.

关键词： Categorization recognition: Detection Retrieval

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iCoseg: Interactive Co-segmentation with Intelligent Scribble Guidance

iCoseg: Interactive Co-segmentation with Intelligent Scribbl...

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23rd ieee conference on computer vision and pattern recognition (cvpr)

作者： Batra, Dhruv Kowdle, Adarsh Parikh, Devi Luo, Jiebo Chen, Tsuhan Carnegie Mellon Univ Pittsburgh PA 15213 USA Cornell Univ Ithaca NY 14853 USA TTIC Chicago IL USA Eastman Kodak Co Rochester NY 14650 USA

ISBN: (纸本)9781424469840

This paper presents an algorithm for Interactive Co-segmentation of a foreground object from a group of related images. While previous approaches focus on unsupervised co-segmentation, we use successful ideas from the interactive object-cutout literature. We develop an algorithm that allows users to decide what foreground is, and then guide the output of the co-segmentation algorithm towards it via scribbles. Interestingly, keeping a user in the loop leads to simpler and highly parallelizable energy functions, allowing us to work with significantly more images per group. However, unlike the interactive single image counterpart, a user cannot be expected to exhaustively examine all cutouts (from tens of images) returned by the system to make corrections. Hence, we propose iCoseg, an automatic recommendation system that intelligently recommends where the user should scribble next. We introduce and make publicly available the largest co-segmentation dataset yet, the CMU-Cornell iCoseg Dataset, with 3 8 groups, 6 4 3 images, and pixelwise hand-annotated groundtruth. Through machine experiments and real user studies with our developed interface, we show that iCoseg can intelligently recommend regions to scribble on, and users following these recommendations can achieve good quality cutouts with significantly lower time and effort than exhaustively examining all cutouts.

关键词： computer vision

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Area Under the ROC Curve Maximization for Metric Learning

Area Under the ROC Curve Maximization for Metric Learning

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

作者： Gajic, Bojana Amato, Ariel Baldrich, Ramon van de Weijer, Joost Gatta, Carlo Vintra Inc Barcelona Spain Comp Vis Ctr Barcelona Spain

ISBN: (纸本)9781665487399

Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.

关键词： Training computer vision conferences Area measurement Benchmark testing pattern recognition

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Shunted Self-Attention via Multi-Scale Token Aggregation

Shunted Self-Attention via Multi-Scale Token Aggregation

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

作者： Ren, Sucheng Zhou, Daquan He, Shengfeng Feng, Jiashi Wang, Xinchao Natl Univ Singapore Singapore Singapore South China Univ Technol Guangzhou Peoples R China Bytelsance Inc Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent vision Transformer (ViT) models have demonstrated encouraging results across various computer vision tasks, thanks to its competence in modeling long-range dependencies of image patches or tokens via self-attention. These models, however, usually designate the similar receptive fields of each token feature within each layer. Such a constraint inevitably limits the ability of each self-attention layer in capturing multi-scale features, thereby leading to performance degradation in handling images with multiple objects of different scales. To address this issue, we propose a novel and generic strategy, termed shunted self-attention (SSA), that allows ViTs to model the attentions at hybrid scales per attention layer. The key idea of SSA is to inject heterogeneous receptive field sizes into tokens: before computing the self-attention matrix, it selectively merges tokens to represent larger object features while keeping certain tokens to preserve fine-grained features. This novel merging scheme enables the self-attention to learn relationships between objects with different sizes, and simultaneously reduces the token numbers and the computational cost. Extensive experiments across various tasks demonstrate the superiority of SSA. Specifically, the SSA-based transformer achieve 84.0% Top-I accuracy and outperforms the state-of-the-art Focal Transformer on ImageNet with only half of the model size and computation cost, and surpasses Focal Transformer by 1.3 mAP on COCO and 2.9 mIOU on ADE20K under similar parameter and computation cost. Code has been released at https://***/OliverRensu/Shunted-Transformer.

关键词： Degradation Deep learning computer vision Costs Computational modeling Merging computer architecture

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IP102: A Large-Scale Benchmark Dataset for Insect Pest recognition 32

IP102: A Large-Scale Benchmark Dataset for Insect Pest Recog...

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

作者： Wu, Xiaoping Zhan, Chi Lai, Yu-Kun Cheng, Ming-Ming Yang, Jufeng Nankai Univ Coll Comp Sci Tianjin Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff Wales

ISBN: (纸本)9781728132938

Insect pests are one of the main factors affecting agricultural product yield. Accurate recognition of insect pests facilitates timely preventive measures to avoid economic losses. However, the existing datasets for the visual classification task mainly focus on common objects, e.g., flowers and dogs. This limits the application of powerful deep learning technology on specific domains like the agricultural field. In this paper;we collect a large-scale dataset named IP102 for insect pest recognition. Specifically, it contains more than 75,000 images belonging to 102 categories, which exhibit a natural long-tailed distribution. In addition, we annotate about 19, 000 images with bounding boxes for object detection. The IP102 has a hierarchical taxonomy and the insect pests which mainly affect one specific agricultural product are grouped into the same upper level category. Furthermore, we perform several baseline experiments on the IP102 dataset, including handcrafted and deep feature based classification methods. Experimental results show that this dataset has the challenges of inter and intra- class variance and data imbalance. We believe our IP102 will facilitate future research on practical insect pest control, fine-grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https : //***/xpwL295/IP102.

关键词： Datasets and Evaluation vision Applications and Systems

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Fine-grained Image-to-Image Transformation towards Visual recognition

Fine-grained Image-to-Image Transformation towards Visual Re...

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

作者： Xiong, Wei He, Yutong Zhang, Yixuan Luo, Wenhan Ma, Lin Luo, Jiebo Univ Rochester Rochester NY 14627 USA Tencent AI Lab Bellevue WA USA

ISBN: (纸本)9781728171685

Existing image-to-image transformation approaches primarily focus on synthesizing visually pleasing data. Generating images with correct identity labels is challenging yet much less explored. It is even more challenging to deal with image transformation tasks with large deformation in poses, viewpoints, or scales while preserving the identity, such as face rotation and object viewpoint morphing. In this paper, we aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image, which can thereby benefit the subsequent fine-grained image recognition and few-shot learning tasks. The generated images, transformed with large geometric deformation, do not necessarily need to be of high visual quality but are required to maintain as much identity information as possible. To this end, we adopt a model based on generative adversarial networks to disentangle the identity related and unrelated factors of an image. In order to preserve the fine-grained contextual details of the input image during the deformable transformation, a constrained nonalignment connection method is proposed to construct learnable highways between intermediate convolution blocks in the generator. Moreover;an adaptive identity modulation mechanism is proposed to transfer the identity information into the output image effectively. Extensive experiments on the CompCars and Multi-PIE datasets demonstrate that our model preserves the identity of the generated images much better than the state-of-the-art image-to-image transformation models, and as a result significantly boosts the visual recognition performance in fine-grained few-shot learning.

关键词： Generative adversarial networks

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Adaptive Class Suppression Loss for Long-Tail Object Detection

Adaptive Class Suppression Loss for Long-Tail Object Detecti...

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

作者： Wang, Tong Zhu, Yousong Zhao, Chaoyang Zeng, Wei Wang, Jinqiao Tang, Ming Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China ObjectEye Inc Beijing Peoples R China Peking Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China NEXWISE Co Ltd Guangzhou Peoples R China

ISBN: (纸本)9781665445092

To address the problem of long-tail distribution for the large vocabulary object detection task, existing methods usually divide the whole categories into several groups and treat each group with different strategies. These methods bring the following two problems. One is the training inconsistency between adjacent categories of similar sizes, and the other is that the learned model is lack of discrimination for tail categories which are semantically similar to some of the head categories. In this paper, we devise a novel Adaptive Class Suppression Loss (ACSL) to effectively tackle the above problems and improve the detection performance of tail categories. Specifically, we introduce a statistic free perspective to analyze the long-tail distribution, breaking the limitation of manual grouping. According to this perspective, our ACSL adjusts the suppression gradients for each sample of each class adaptively, ensuring the training consistency and boosting the discrimination for rare categories. Extensive experiments on long-tail datasets LVIS and Open Images show that the our ACSL achieves 5.18% and 5.2% improvements with ResNet50-FPN, and sets a new state of the art.

关键词： Training Adaptation models Vocabulary computer vision Head Object detection Manuals

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Pixels, Regions, and Objects: Multiple Enhancement for Salient Object Detection

Pixels, Regions, and Objects: Multiple Enhancement for Salie...

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

作者： Wang, Yi Wang, Ruili Fan, Xin Wang, Tianzhu He, Xiangjian Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian Peoples R China Massey Univ Sch Math & Computat Sci Palmerston North New Zealand Univ Nottingham Ningbo China Sch Comp Sci Ningbo Peoples R China

ISBN: (纸本)9798350301298

Salient object detection (SOD) aims to mimic the human visual system (HVS) and cognition mechanisms to identify and segment salient objects. However, due to the complexity of these mechanisms, current methods are not perfect. Accuracy and robustness need to be further improved, particularly in complex scenes with multiple objects and background clutter. To address this issue, we propose a novel approach called Multiple Enhancement Network (MENet) that adopts the boundary sensibility, content integrity, iterative refinement, and frequency decomposition mechanisms of HVS. A multi-level hybrid loss is firstly designed to guide the network to learn pixel-level, region-level, and object-level features. A flexible multiscale feature enhancement module (ME-Module) is then designed to gradually aggregate and refine global or detailed features by changing the size order of the input feature sequence. An iterative training strategy is used to enhance boundary features and adaptive features in the dual-branch decoder of MENet. Comprehensive evaluations on six challenging benchmark datasets show that MENet achieves state-of-the-art results. Both the codes and results are publicly available at https://***/yiwangtz/MENet.

关键词： detection recognition: Categorization retrieval

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