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MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion

MSTRIQ: No Reference Image Quality Assessment Based on Swin ...

引用

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

作者： Wang, Jing Fan, Haotian Hou, Xiaoxia Xu, Yitian Li, Tao Lu, Xuechao Fu, Lean ByteDance Inc Bldg 10Zone ABusiness PkLane 888Tianlin Rd Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Measuring the perceptual quality of images automatically is an essential task in the area of computer vision, as degradations on image quality can exist in many processes from image acquisition, transmission to enhancing. Many Image Quality Assessment(IQA) algorithms have been designed to tackle this problem. However, it still remains unsettled due to the various types of image distortions and the lack of large-scale human-rated datasets. In this paper, we propose a novel algorithm based on the Swin Transformer [31] with fused features from multiple stages, which aggregates information from both local and global features to better predict the quality. To address the issues of small-scale datasets, relative rankings of images have been taken into account together with regression loss to simultaneously optimize the model. Furthermore, effective data augmentation strategies are also used to improve the performance. In comparisons with previous works, experiments are carried out on two standard IQA datasets and a challenge dataset. The results demonstrate the effectiveness of our work. The proposed method outperforms other methods on standard datasets and ranks 2nd in the no-reference track of NTIRE 2022 Perceptual Image Quality Assessment Challenge [53]. It verifies that our method is promising in solving diverse IQA problems and thus can be used to real-word applications.

关键词： Image quality Training computer vision Predictive models Transformers Prediction algorithms Distortion

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Multi stain graph fusion for multimodal integration in pathology

Multi stain graph fusion for multimodal integration in patho...

引用

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

作者： Dwivedi, Chaitanya Nofallah, Shima Pouryahya, Maryam Iyer, Janani Leidal, Kenneth Chung, Chuhan Watkins, Timothy Billin, Andrew Myers, Robert Abel, John Behrooz, Ali PathAI Inc Boston MA 02215 USA Gilead Sci Inc Foster City CA 94404 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our multimodal approach learns to extract complementary information from TC and H&E graphs corresponding to each stain while simultaneously learning an optimal policy to combine this information. We report up to 20% improvement in predicting fibrosis stage and NAS component grades over single-stain modeling approaches, measured by computing linearly weighted Cohen's kappa between machinederived vs. pathologist consensus scores. Broadly, this paper demonstrates the value of leveraging diverse pathology images for improved ML-powered histologic assessment.

关键词： Weight measurement computer vision Histopathology Computational modeling conferences Liver Predictive models

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Guided Deep Metric Learning

Guided Deep Metric Learning

引用

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

作者： Gonzalez-Zapata, Jorge Reyes-Amezcua, Ivan Flores-Araiza, Daniel Mendez-Ruiz, Mauricio Ochoa-Ruiz, Gilberto Mendez-Vazquez, Andres CINVESTAV Unidad Guadalajara Zapopan Mexico Tecnol Monterrey Sch Engn & Sci Monterrey Mexico

ISBN: (纸本)9781665487399

Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning. However, they sometimes lack generalization properties because they are trained often using an inappropriate sample selection strategy or due to the difficulty of the dataset caused by a distributional shift in the data. These represent a significant drawback when attempting to learn the underlying data manifold. Therefore, there is a pressing need to develop better ways of obtaining generalization and representation of the underlying manifold. In this paper, we propose a novel approach to DML that we call Guided Deep Metric Learning, a novel architecture oriented to learning more compact clusters, improving generalization under distributional shifts in DML. This novel architecture consists of two independent models: A multi-branch master model, inspired from a Few-Shot Learning (FSL) perspective, generates a reduced hypothesis space based on prior knowledge from labeled data, which guides or regularizes the decision boundary of a student model during training under an offline knowledge distillation scheme. Experiments have shown that the proposed method is capable of a better manifold generalization and representation to up to 40% improvement (Recall@1, CIFAR10), using guidelines suggested by Musgrave et al. to perform a more fair and realistic comparison, which is currently absent in the literature.

关键词： Measurement Manifolds Training Analytical models Adaptation models Visualization computer architecture

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MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment

MANIQA: Multi-dimension Attention Network for No-Reference I...

引用

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

作者： Yang, Sidi Wu, Tianhe Shi, Shuwei Lao, Shanshan Gong, Yuan Cao, Mingdeng Wang, Jiahao Yang, Yujiu Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception. Unfortunately, existing NR-IQA methods are far from meeting the needs of predicting accurate quality scores on GAN-based distortion images. To this end, we propose Multi-dimension Attention Network for no-reference Image Quality Assessment (MANIQA) to improve the performance on GAN-based distortion. We firstly extract features via ViT, then to strengthen global and local interactions, we propose the Transposed Attention Block (TAB) and the Scale Swin Transformer Block (SSTB). These two modules apply attention mechanisms across the channel and spatial dimension, respectively. In this multi-dimensional manner, the modules cooperatively increase the interaction among different regions of images globally and locally. Finally, a dual branch structure for patch-weighted quality prediction is applied to predict the final score depending on the weight of each patch's score. Experimental results demonstrate that MANIQA outperforms state-of-the-art methods on four standard datasets (LIVE, TID2013, CSIQ, and KADID-10K) by a large margin. Besides, our method ranked first place in the final testing phase of the NTIRE 2022 Perceptual Image Quality Assessment Challenge Track 2: No-Reference. Codes and models are available at https://***/IIGROUP/MANIQA.

关键词： Image quality Databases Distortion Feature extraction Transformers Quality assessment pattern recognition

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RV-GAN: Recurrent GAN for Unconditional Video Generation

RV-GAN: Recurrent GAN for Unconditional Video Generation

引用

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

作者： Gupta, Sonam Keshari, Arti Das, Sukhendu Indian Inst Technol Dept Comp Sci Engn Visualizat & Percept Lab Madras Tamil Nadu India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Generative models aiming to generate content from noise have achieved high-fidelity synthesis for image data. However, obtaining comparable performance in the field of unconditional video generation still remains challenging. In this work, we propose a recurrent GAN architecture to model the high-dimensional video data distribution. Recurrent networks by design are able to generate complex, long sequences in an autoregressive fashion. However, the standard LSTM unit for videos (ConvLSTM) is not ideally suited for the task of unconditional video generation. Therefore, we propose a simple yet effective LSTM variant called as TransConv LSTM (TC-LSTM) by modulating the conventional ConvLSTM to have a transpose convolutional structure in input-to-state transitions. This enables the network to model both spatial and temporal relationships across layers simultaneously inside the TC-LSTM unit. TC-LSTM unit acts as a building block of our generator. Extensive quantitative and qualitative analysis shows that RV-GAN outperforms state-of-the-art methods by a significant margin on Moving MNIST, MUG, Weizmann and UCF101 datasets. Additionally, owing to the recurrent structure, our method is able to generate high-quality videos, up to 2 times longer (32 frames) than training videos at inference time. Further analysis confirms that the proposed architecture is generic and can be easily adapted to other tasks like class-conditional video synthesis and text-to-video synthesis.

关键词： Training Architecture computer architecture Generative adversarial networks Data models Hybrid power systems Generators

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Contrastive Regularization for Semi-Supervised Learning

Contrastive Regularization for Semi-Supervised Learning

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

作者： Lee, Doyup Kim, Sungwoong Kim, Ildoo Cheon, Yeongjae Cho, Minsu Han, Wook-Shin POSTECH Pohang South Korea Kakao Brain Seongnam South Korea Kakao Enterprise Seongnam South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high performance. In this study, we analyze that the consistency regularization restricts the propagation of labeling information due to the exclusion of samples with unconfident pseudo-labels in the model updates. Then, we propose contrastive regularization to improve both efficiency and accuracy of the consistency regularization by well-clustered features of unlabeled data. In specific, after strongly augmented samples are assigned to clusters by their pseudolabels, our contrastive regularization updates the model so that the features with confident pseudo-labels aggregate the features in the same cluster, while pushing away features in different clusters. As a result, the information of confident pseudo-labels can be effectively propagated into more unlabeled samples during training by the well-clustered features. On benchmarks of semi-supervised learning tasks, our contrastive regularization improves the previous consistency-based methods and achieves state-of-the-art results, especially with fewer training iterations. Our method also shows robust performance on open-set semi-supervised learning where unlabeled data includes out-of-distribution samples.

关键词： Training computer vision Analytical models conferences Memory management Semisupervised learning Benchmark testing

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Diagnosing Rarity in Human-object Interaction Detection

Diagnosing Rarity in Human-object Interaction Detection

引用

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

作者： Kilickaya, Mert Smeulders, Arnold QUvA Deep Vis Lab Amsterdam Netherlands

ISBN: (纸本)9781728193601

Human-object interaction (HOI) detection is a core task in computer vision. The goal is to localize all human-object pairs and recognize their interactions. An interaction defined by a tuple leads to a long-tailed visual recognition challenge since many combinations are rarely represented. The performance of the proposed models is limited especially for the tail categories, but little has been done to understand the reason. To that end, in this paper, we propose to diagnose rarity in HOI detection. We propose a three-step strategy, namely Detection, Identification and recognition where we carefully analyse the limiting factors by studying state-of-the-art models. Our findings indicate that detection and identification steps are altered by the interaction signals like occlusion and relative location, as a result limiting the recognition accuracy.

关键词： computer vision

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Uniform Priors for Data-Efficient Learning

Uniform Priors for Data-Efficient Learning

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

作者： Sinha, Samarth Roth, Karsten Goyal, Anirudh Ghassemi, Marzyeh Akata, Zeynep Larochelle, Hugo Garg, Animesh Univ Toronto Toronto ON Canada Univ Tubingen Tubingen Germany MILA Montreal PQ Canada MIT Cambridge MA 02139 USA MPI IS Stuttgart Germany Google Mountain View CA 94043 USA Vector Inst Bengaluru India Nvidia Santa Clara CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Few or zero-shot adaptation to novel tasks is important for the scalability and deployment of machine learning models. It is therefore crucial to find properties that encourage more transferable features in deep networks for generalization. In this paper, we show that models that learn uniformly distributed features from the training data, are able to perform better transfer learning at test-time. Motivated by this, we evaluate our method: uniformity regularization (UR) on its ability to facilitate adaptation to unseen tasks and data on six distinct domains: Few-Learning with Images, Few-shot Learning with Language, Deep Metric Learning, 0-Shot Domain Adaptation, Out-of-Distribution classification, and Neural Radiance Fields. Across all experiments, we show that using UR, we are able to learn robust vision systems which consistently offer benefits over baselines trained without uniformity regularization and are able to achieve state-of-the-art performance in Deep Metric Learning, Few-shot learning with images and language.

关键词： Measurement Training Deep learning Adaptation models Scalability Machine vision Transfer learning

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Contrastive Learning-based Robust Object Detection under Smoky Conditions

Contrastive Learning-based Robust Object Detection under Smo...

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

作者： Wu, Wei Chang, Hao Zheng, Yonghua Li, Zhu Chen, Zhiwen Zhang, Ziheng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Object detection is to effectively find out interested targets in images and then accurately determine their categories and positions. Recently many excellent methods have been developed to provide powerful detection capability. However, their performance may degrade significantly under severe weather such as smoky conditions. In this paper, we propose a contrastive learning-based robust object detection algorithm for smoke images. The proposed object detector consists of two modules: contrastive learning module and object bounding box prediction module. The first module learns representation vectors by maximizing agreement between different augmented views of the same smoke image. These representations are then sent to the second module to yield the bounding box for each object. In addition, we also propose a novel affine data augmentation method. Extensive experiments have been conducted on A2I2-Haze dataset which is the first real haze dataset with in-situ smoke measurement aligned to aerial and ground imagery. This dataset is also the only dataset used in the 5th UG(2)+ challenges of CVPR 2022 for both training and testing. Compared with state-of-the-art methods, evaluation results show the superiority of our proposed object detector.

关键词： Training computer vision conferences Object detection Detectors Transforms Prediction algorithms

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Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution

Fast and Memory-Efficient Network Towards Efficient Image Su...

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

作者： Du, Zongcai Liu, Ding Liu, Jie Tang, Jie Wu, Gangshan Fu, Lean Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Runtime and memory consumption are two important aspects for efficient image super-resolution (EISR) models to be deployed on resource-constrained devices. Recent advances in EISR [16, 32] exploit distillation and aggregation strategies with plenty of channel split and concatenation operations to fully use limited hierarchical features. In contrast, sequential network operations avoid frequently accessing preceding states and extra nodes, and thus are beneficial to reducing the memory consumption and runtime overhead. Following this idea, we design our lightweight network backbone by mainly stacking multiple highly optimized convolution and activation layers and decreasing the usage of feature fusion. We propose a novel sequential attention branch, where every pixel is assigned an important factor according to local and global contexts, to enhance high-frequency details. In addition, we tailor the residual block for EISR and propose an enhanced residual block (ERB) to further accelerate the network inference. Finally, combining all the above techniques, we construct a fast and memory-efficient network (FMEN) and its small version FMEN-S, which runs 33% faster and reduces 74% memory consumption compared with the state-of-the-art EISR model: E-RFDN, the champion in [49]. Besides, FMEN-S achieves the lowest memory consumption and the second shortest runtime in NTIRE 2022 challenge on efficient super-resolution [28]. Code is available at https://***/NJU-Jet/FMEN.

关键词： computer vision Runtime Codes Convolution conferences Memory management Superresolution

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