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NTIRE 2024 Challenge on Blind Enhancement of Compressed Image: Methods and Results

NTIRE 2024 Challenge on Blind Enhancement of Compressed Imag...

引用

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

作者： Ren Yang Radu Timofte Bingchen Li Xin Li Mengxi Guo Shijie Zhao Li Zhang Zhibo Chen Dongyang Zhang Yash Arora Aditya Arora Yuanbin Chen Hui Tang Tao Wang Longxuan Zhao Bin Chen Tong Tong Qiao Mo Jingwei Bao Jinhua Hao Yukang Ding Hantang Li Ming Sun Chao Zhou Shuyuan Zhu Zhi Jin Wei Wang Dandan Zhan Jiawei Wu Jiahao Wu Luwei Tu Hongyu An Xinfeng Zhang Woon-Ha Yeo Wang-Taek Oh Young-Il Kim Han-Cheol Ryu Long Sun Mingjun Zhen Jinshan Pan Jiangxin Dong Jinhui Tang Yapeng Du Ao Li Ziyang He Lei Luo Ce Zhu Xin Yao Sunder Ali Khowaja IK Hyun Lee Jaeho Lee Seongwan Kim Sharif S M A Nodirkhuja Khujaev Roman Tsoy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the Challenge on Blind Enhancement of Compressed Image at NTIRE 2024, which aims at enhancing the quality of JPEG images which are compressed with unknown quality factor. The challenge requires that the total size of codes and pre-trained model(s) cannot exceed 300 MB, since we encourage solutions for blind enhancement with generalized models, instead of separately training several models for each quality factor. In this report, we summarize the detailed settings of the challenge, the final results, and the solutions proposed by the participants. The challenge has 129 registered participants and received 13 valid submissions. Several teams (including all TOP 3 teams) have publicly released the codes (see Sec. 4). They gauge the state-of-the-art of blind quality enhancement of compressed image.

关键词： Q-factor Training computer vision Image coding Codes Reviews conferences

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Tri-VAE: Triplet Variational Autoencoder for Unsupervised Anomaly Detection in Brain Tumor MRI

Tri-VAE: Triplet Variational Autoencoder for Unsupervised An...

引用

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

作者： Hansen Wijanarko Evelyne Calista Li-Fen Chen Yong-Sheng Chen National Yang Ming Chiao Tung University Taiwan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The intricate manifestations of pathological brain lesions in imaging data pose challenges for supervised detection methods due to the scarcity of annotated samples. To overcome this difficulty, our focus shifts to unsupervised anomaly detection. In this work, we exclusively train the proposed model using healthy data to identify unseen anomalies during testing. This study entails investigating the triplet-based variational autoencoder to simultaneously learn the distribution of healthy brain data and denoising capabilities. Importantly, we rectify a misconception inherent in prior projection-based approaches which relies on the presumption that healthy regions within images would persist unaltered in the reconstructed output. This inadvertently implied a substantial likeness in latent space representations between lesion and lesion-free images. However, this assumption might not hold true, particularly due to the potential significant impact of lesion area intensities on the projection process notably for autoencoders with single information bottleneck. To overcome this limitation, we disentangled metric learning from latent sampling. This approach ensures that both lesion and lesion-free input images are projected into the same distribution, specifically the lesion-free projection. Moreover, we introduce a semantic-guided gated cross skip module to enhance spatial detail retrieval while suppressing anomalies, leveraging robust healthy brain representation semantics exist in the deeper levels of the decoder. We also discovered that incorporating structure similarity index measure as an extra training objective bolsters the capability of anomaly detection for the proposed model.

关键词： Training Measurement Pathology Semantics Noise reduction Brain modeling pattern recognition

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CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery

CDAD-Net: Bridging Domain Gaps in Generalized Category Disco...

引用

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

作者： Sai Bhargav Rongali Sarthak Mehrotra Ankit Jha Mohamad Hassan N C Shirsha Bose Tanisha Gupta Mainak Singha Biplab Banerjee Indian Institute of Technology Bombay India INRIA Grenoble France Technical University of Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes. A salient challenge arises due to domain shifts between these datasets. To address this, we present a novel setting: Across Domain Generalized Category Discovery (AD-GCD) and bring forth CDAD-Net (Class Discoverer Across Domains) as a remedy. CDAD-Net is architected to synchronize potential known class samples across both the labeled (source) and unlabeled (target) datasets, while emphasizing the distinct categorization of the target data. To facilitate this, we propose an entropy-driven adversarial learning strategy that accounts for the distance distributions of target samples relative to source-domain class prototypes. Parallelly, the discriminative nature of the shared space is upheld through a fusion of three metric learning objectives. In the source domain, our focus is on refining the proximity between samples and their affiliated class prototypes, while in the target domain, we integrate a neighborhood-centric contrastive learning mechanism, enriched with an adept neighbors-mining approach. To further accentuate the nuanced feature interrelation among semantically aligned images, we champion the concept of conditional image inpainting, underscoring the premise that semantically analogous images prove more efficacious to the task than their disjointed counterparts. Experimentally, CDAD-Net eclipses existing literature with a performance increment of 8-15% on three AD-GCD benchmarks we present.

关键词： Training Measurement computer vision conferences Refining Prototypes Contrastive learning

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The Affective Growth of computer vision

The Affective Growth of Computer Vision

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

作者： Su, Norman Makoto Crandall, David J. Indiana Univ Luddy Sch Informat Comp & Engn Bloomington IN 47405 USA

ISBN: (纸本)9781665445092

The success of deep learning has led to intense growth and interest in computer vision, along with concerns about its potential impact on society. Yet we know little about how these changes have affected the people that research and practice computer vision: we as a community spend so much effort trying to replicate the abilities of humans, but so little time considering the impact of this work on ourselves. In this paper, we report on a study in which we asked computer vision researchers and practitioners to write stories about emotionally-salient events that happened to them. Our analysis of over 50 responses found tremendous affective (emotional) strain in the computer vision community. While many describe excitement and success, we found strikingly frequent feelings of isolation, cynicism, apathy, and exasperation over the state of the field. This is especially true among people who do not share the unbridled enthusiasm for normative standards for computer vision research and who do not see themselves as part of the "incrowd." Our findings suggest that these feelings are closely tied to the kinds of research and professional practices now expected in computer vision. We argue that as a community with significant stature, we need to work towards an inclusive culture that makes transparent and addresses the real emotional toil of its members.

关键词： Deep learning computer vision pattern recognition Standards Strain

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End-to-End Neural Network Compression via l1/l2 Regularized Latency Surrogates

End-to-End Neural Network Compression via l1/l2 Regularized ...

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

作者： Anshul Nasery Hardik Shah Arun Sai Suggala Prateek Jain University of Washington ETH Zurich Google Research India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural network (NN) compression via techniques such as pruning, quantization requires setting compression hyperparameters (e.g., number of channels to be pruned, bitwidths for quantization) for each layer either manually or via neural architecture search (NAS) which can be computationally expensive. We address this problem by providing an end-to-end technique that optimizes for model’s Floating Point Operations (FLOPs) via a novel $\frac{{{\ell _1}}}{{{\ell _2}}}$ latency surrogate. Our algorithm is versatile and can be used with many popular compression methods including pruning, low-rank factorization, and quantization, and can optimize for on-device latency. Crucially, it is fast and runs in almost the same amount of time as a single model training run; which is a significant training speed-up over standard NAS methods. For BERT compression on GLUE fine-tuning tasks, we achieve 50% reduction in FLOPs with only 1% drop in performance. For compressing MobileNetV3 on ImageNet-1K, we achieve 15% reduction in FLOPs without drop in accuracy, while still requiring 3× less training compute than SOTA NAS techniques. Finally, for transfer learning on smaller datasets, our technique identifies 1.2× - 1.4× cheaper architectures than standard MobileNetV3, EfficientNet suite of architectures at almost the same training cost and accuracy.

关键词： Training Quantization (signal) Image coding Accuracy Costs Transfer learning computer architecture

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IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagery

IrrNet: Advancing Irrigation Mapping with Incremental Patch ...

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

作者： Oishee Bintey Hoque Samarth Swarup Abhijin Adiga Sayjro Kossi Nouwakpo Madhav Marathe Department of Computer Science University of Virginia Charlottesville VA USA Biocomplexity Institute University of Virginia Charlottesville VA USA US Department of Agriculture Agricultural Research Service Kimberly ID USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Irrigation mapping plays a crucial role in effective water management, essential for preserving both water quality and quantity, and is key to mitigating the global issue of water scarcity. The complexity of agricultural fields, adorned with diverse irrigation practices, especially when multiple systems coexist in close quarters, poses a unique challenge. This complexity is further compounded by the nature of Landsat’s remote sensing data, where each pixel is rich with densely packed information, complicating the task of accurate irrigation mapping. In this study, we introduce an innovative approach that employs a progressive training method, which strategically increases patch sizes throughout the training process, utilizing datasets from Landsat 5 and 7, labeled with the WRLU dataset for precise labeling. This initial focus allows the model to capture detailed features, progressively shifting to broader, more general features as the patch size enlarges. Remarkably, our method enhances the performance of existing state-of-the-art models by approximately 20%. Furthermore, our analysis delves into the significance of incorporating various spectral bands into the model, assessing their impact on performance. The findings reveal that additional bands are instrumental in enabling the model to discern finer details more effectively. This work sets a new standard for leveraging remote sensing imagery in irrigation mapping.

关键词： Training Irrigation Analytical models Accuracy Water quality Robustness Complexity theory

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Towards Weakly-Supervised Domain Adaptation for Lane Detection

Towards Weakly-Supervised Domain Adaptation for Lane Detecti...

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

作者： Jingxing Zhou Chongzhe Zhang Jürgen Beyerer Porsche Engineering Group GmbH Vision and Fusion Laboratory (IES) Karlsruhe Institute of Technology University of Stuttgart Fraunhofer IOSB

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Lane detection plays an indispensable role in automated driving functions and advanced driver assistance systems by providing fundamental spatial orientation, which is imperative for trajectory planning with traffic regulation compliance. The variability of lane structures across the world poses challenges for data-driven lane detection models. However, acquiring vast amounts of labeled data encompassing a wide variety of real-world scenarios for supervised learning is often cost-prohibitive. In this work, we propose a Weakly Supervised Domain Adaptation framework for Lane Detection (WSDAL), which requires easily-provided labels exclusively for the number of lanes in the target domain to aid the adaptation process. WSDAL consists of a teacher-student network, an additional segmentation head as an auxiliary task during training, and a novel loss function that incorporates the number of lanes prediction. As a versatile framework, WSDAL can be applied to any anchor-based lane detector. Between three frequently-used lane detection datasets (TuSimple, CULane and CurveLanes) for domain adaptation, WSDAL framework demonstrates its effectiveness and efficiency over common unsupervised domain adaptation methods and fully supervised training. In addition, we discuss the quality requisites from the labels for the weakly-supervised domain adaptation, indicating that label errors at realistic scales still provide satisfactory results on the considered tasks.

关键词： Training Image segmentation Adaptation models Lane detection Trajectory planning Supervised learning Detectors

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Adaptive Render-Video Streaming for Virtual Environments

Adaptive Render-Video Streaming for Virtual Environments

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

作者： Jia-Jie Lim Matthias S. Treder Aaron Chadha Yiannis Andreopoulos Sony Interactive Entertainment London UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In cloud-based gaming and virtual reality (G&VR), scene content is rendered in a cloud server and streamed as low-latency encoded video to the client device. In this context, distributed rendering aims to offload parts of the rendering to the client. An adaptive approach is proposed, which dynamically assigns assets to client-side vs. server-side rendering according to varying rendering time and bitrate targets. This is achieved by streaming perceptually-optimized scene control weights to the client, which are compressed with a composable autoencoder in conjunction with select video segments. This creates an adaptive render-video (REVI) streaming framework, which allows for substantial tradeoffs between client rendering time and the bitrate required to stream visually-lossless video from the server to the client. In order to estimate and control the client rendering time and the required bitrate of each subset of each scene, a random-forest based regressor is proposed, in conjunction with the use of AIMD (additive-increase/multiplicative-decrease) to ensure predetermined average bitrate or render-time targets are met. Experiments are presented based on typical sets of G&VR scenes rendered in Blender and HEVC low-latency encoding. A key result is that, when the client is providing for 50% of the rendering time needed to render the whole scene, up to 60% average bitrate saving is achieved versus streaming the entire scene to the client as video.

关键词： Cloud computing Bit rate Lighting Virtual environments Streaming media Ray tracing Rendering (computer graphics)

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Towards Efficient Machine Unlearning with Data Augmentation: Guided Loss-Increasing (GLI) to Prevent the Catastrophic Model Utility Drop

Towards Efficient Machine Unlearning with Data Augmentation:...

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

作者： Dasol Choi Soora Choi Eunsun Lee Jinwoo Seo Dongbin Na Yonsei University MODULABS Chung Ang University Kyung Hee University Millennialsworks Inc. POSTECH

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Machine unlearning algorithms aim to make a model forget specific data that might be used in the training phase. To solve this problem, various studies have adopted loss-increasing methods. For example, some unlearning methods have presented data augmentation methods to generate synthesized images that maximize loss values for images to be forgotten. In contrast, some unlearning methods directly update the model in the direction of increasing loss for the images to be forgotten. In this paper, we first revisit these loss-increasing methods and analyze their limitations. We have found that these simple loss-increasing strategies can be effective in the aspect of the forgetting score, however, can hurt the original model utility unexpectedly, we call this phenomenon catastrophic model utility drop. We propose a novel data augmentation method, Guided Loss-Increasing (GLI), that restricts the direction of the data update to resolve the utility drop issue. This is achieved by aligning updates with the model’s existing knowledge, thereby ensuring that the unlearning process does not adversely affect the model’s original performance. Our extensive experiments demonstrate our method shows superior (1) model utility and (2) forgetting performance compared to the previous state-of-the-art (SOTA) methods. Furthermore, we demonstrate Jensen–Shannon divergence can be utilized to robustly evaluate the forgetting score. The source codes are publicly available at https://***/Dasol-Choi/Guided_Loss_Increasing.

关键词： Measurement Training computer vision Source coding conferences Computational modeling Data augmentation

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Dedicated Inference Engine and Binary-Weight Neural Networks for Lightweight Instance Segmentation

Dedicated Inference Engine and Binary-Weight Neural Networks...

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

作者： Tse-Wei Chen Wei Tao Dongyue Zhao Kazuhiro Mima Tadayuki Ito Kinya Osa Masami Kato Device Technology Development Headquarters Canon Inc. Tokyo Japan Canon Innovative Solution (Beijing) Co. Ltd. Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Binary-weight Neural Networks (BNNs), in which weights are binarized and activations are quantized, are employed to reduce computational costs of various kinds of applications. In this paper, a design methodology of hardware architecture for inference engines is proposed to handle modern BNNs with two operation modes. Multiply-Accumulate (MAC) operations can be simplified by replacing multiply operations with bitwise operations. The proposed method can effectively reduce the gate count of inference engines by removing a part of computational costs from the hardware system. The architecture of MAC operations can calculate the inference results of BNNs efficiently with only 52% of hardware costs compared with the related works. To show that the inference engine can handle practical applications, two lightweight networks which combine the backbones of SegNeXt and the decoder of SparseInst for instance segmentation are also proposed. The output results of the lightweight networks are computed using only bitwise operations and add operations. The proposed inference engine has lower hardware costs than related works. The experimental results show that the proposed inference engine can handle the proposed instance-segmentation networks and achieves higher accuracy than YOLACT on the "Person" category although the model size is 77.7× smaller compared with YOLACT.

关键词： Instance segmentation Costs Accuracy Design methodology Neural networks computer architecture Logic gates

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