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OpenMix: Exploring Outlier Samples for Misclassification Detection

OpenMix: Exploring Outlier Samples for Misclassification Det...

引用

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

作者： Zhu, Fei Cheng, Zhen Zhang, Xu-Yao Liu, Cheng-Lin Chinese Acad Sci Inst Automat MAIS Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9798350301298

Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental requirement in high-stakes applications. Unfortunately, modern deep neural networks are often overconfident for their erroneous predictions. In this work, we exploit the easily available outlier samples, i.e., unlabeled samples coming from non-target classes, for helping detect misclassification errors. Particularly, we find that the well-known Outlier Exposure, which is powerful in detecting out-of-distribution (OOD) samples from unknown classes, does not provide any gain in identifying misclassification errors. Based on these observations, we propose a novel method called OpenMix, which incorporates open-world knowledge by learning to reject uncertain pseudo-samples generated via outlier transformation. OpenMix significantly improves confidence reliability under various scenarios, establishing a strong and unified framework for detecting both misclassified samples from known classes and OOD samples from unknown classes. The code is publicly available at https://***/Impression2805/OpenMix.

关键词： detection recognition: Categorization retrieval

来源：评论

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PEFAT: Boosting Semi-supervised Medical Image Classification via Pseudo-loss Estimation and Feature Adversarial Training

PEFAT: Boosting Semi-supervised Medical Image Classification...

引用

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

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Xia, Yong Northwestern Polytech Univ Sch Comp Sci & Engn Xian Peoples R China Univ Adelaide Adelaide SA 5005 Australia

ISBN: (纸本)9798350301298

Pseudo-labeling approaches have been proven beneficial for semi-supervised learning (SSL) schemes in computer vision and medical imaging. Most works are dedicated to finding samples with high-confidence pseudo-labels from the perspective of model predicted probability. Whereas this way may lead to the inclusion of incorrectly pseudo-labeled data if the threshold is not carefully adjusted. In addition, low-confidence probability samples are frequently disregarded and not employed to their full potential. In this paper, we propose a novel Pseudo-loss Estimation and Feature Adversarial Training semi-supervised framework, termed as PEFAT, to boost the performance of multiclass and multi-label medical image classification from the point of loss distribution modeling and adversarial training. Specifically, we develop a trustworthy data selection scheme to split a high-quality pseudo-labeled set, inspired by the dividable pseudo-loss assumption that clean data tend to show lower loss while noise data is the opposite. Instead of directly discarding these samples with low-quality pseudo-labels, we present a novel regularization approach to learn discriminate information from them via injecting adversarial noises at the feature-level to smooth the decision boundary. Experimental results on three medical and two natural image benchmarks validate that our PEFAT can achieve a promising performance and surpass other state-of-the-art methods. The code is available at https://***/maxwell0027/PEFAT.

关键词： cell microscopy Medical and biological vision

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Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution

Learning Spatial-Temporal Implicit Neural Representations fo...

引用

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

作者： Lu, Yunfan Wang, Zipeng Liu, Minjie Wang, Hongjian Wang, Lin HKUST GZ AI Thrust Guangzhou Peoples R China Tsinghua Univ Shenzhen Int Grad Sch Beijing Peoples R China HKUST Dept Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9798350301298

Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video super-resolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the Spatial-Temporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https: //***/cvpr23/egvsr.

关键词： Low-level vision

来源：评论

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OvarNet: Towards Open-vocabulary Object Attribute recognition

OvarNet: Towards Open-vocabulary Object Attribute Recognitio...

引用

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

作者： Chen, Keyan Jiang, Xiaolong Hu, Yao Tang, Xu Gao, Yan Chen, Jianqi Xie, Weidi Beihang Univ Beijing Peoples R China Xiaohongshu Inc Shanghai Peoples R China Shanghai Jiao Tong Univ CMIC Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

In this paper, we consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at the training stage, resembling an open-vocabulary scenario. To achieve this goal, we make the following contributions: (i) we start with a naive two-stage approach for open-vocabulary object detection and attribute classification, termed CLIP-Attr. The candidate objects are first proposed with an offline RPN and later classified for semantic category and attributes;(ii) we combine all available datasets and train with a federated strategy to fine-tune the CLIP model, aligning the visual representation with attributes, additionally, we investigate the efficacy of leveraging freely available online image-caption pairs under weakly supervised learning;(iii) in pursuit of efficiency, we train a Faster-RCNN type model end-to-end with knowledge distillation, that performs class-agnostic object proposals and classification on semantic categories and attributes with classifiers generated from a text encoder;Finally, (iv) we conduct extensive experiments on VAW, MSCOCO, LSA, and OVAD datasets, and show that recognition of semantic category and attributes is complementary for visual scene understanding, i.e., jointly training object detection and attributes prediction largely outperform existing approaches that treat the two tasks independently, demonstrating strong generalization ability to novel attributes and categories.

关键词： detection recognition: Categorization retrieval

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Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution

Better "CMOS" Produces Clearer Images: Learning Space-Varian...

引用

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

作者： Chen, Xuhai Zhang, Jiangning Xu, Chao Wang, Yabiao Wang, Chengjie Liu, Yong Zhejiang Univ APRIL Lab Hangzhou Peoples R China Tencent Youtu Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant. However, the blur involved in real applications are usually space-variant due to object motion, out-of-focus, etc., resulting in severe performance drop of the advanced SR methods. To address this problem, we firstly introduce two new datasets with out-of-focus blur, i.e., NYUv2-BSR and Cityscapes-BSR, to support further researches of blind SR with space-variant blur. Based on the datasets, we design a novel Cross-MOdal fuSion network (CMOS) that estimate both blur and semantics simultaneously, which leads to improved SR results. It involves a feature Grouping Interactive Attention (GIA) module to make the two modalities interact more effectively and avoid inconsistency. GIA can also be used for the interaction of other features because of the universality of its structure. Qualitative and quantitative experiments compared with state-of-the-art methods on above datasets and real-world images demonstrate the superiority of our method, e.g., obtaining PSNR/SSIM by +1.91./+0.0048. on NYUv2-BSR than MANet.

关键词： Low-level vision

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Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed recognition

Class-Conditional Sharpness-Aware Minimization for Deep Long...

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

作者： Zhou, Zhipeng Li, Lanqing Zhao, Peilin Heng, Pheng-Ann Gong, Wei Univ Sci & Technol China Hefei Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Shenzhen Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (纸本)9798350301298

It's widely acknowledged that deep learning models with flatter minima in its loss landscape tend to generalize better. However, such property is under-explored in deep long-tailed recognition (DLTR), a practical problem where the model is required to generalize equally well across all classes when trained on highly imbalanced label distribution. In this paper, through empirical observations, we argue that sharp minima are in fact prevalent in deep longtailed models, whereas naive integration of existing flattening operations into long-tailed learning algorithms brings little improvement. Instead, we propose an effective two-stage sharpness-aware optimization approach based on the decoupling paradigm in DLTR. In the first stage, both the feature extractor and classifier are trained under parameter perturbations at a class-conditioned scale, which is theoretically motivated by the characteristic radius of flat minima under the PAC-Bayesian framework. In the second stage, we generate adversarial features with class-balanced sampling to further robustify the classifier with the backbone frozen. Extensive experiments on multiple longtailed visual recognition benchmarks show that, our proposed Class-Conditional Sharpness-Aware Minimization (CC-SAM), achieves competitive performance compared to the state-of-the-arts. Code is available at https://***/zzpustc/CC-SAM.

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

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Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR

Lite DETR : An Interleaved Multi-Scale Encoder for Efficient...

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

作者： Li, Feng Zeng, Ailing Liu, Shilong Zhang, Hao Li, Hongyang Zhang, Lei Ni, Lionel M. Hong Kong Univ Sci & Technol Hong Kong Peoples R China IDEA Shenzhen Peoples R China Tsinghua Univ Inst AI Dept CST BNRist Ctr Beijing Peoples R China South China Univ Sci & Technol Guangzhou Guangdong Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798350301298

Recent DEtection TRansformer-based (DETR) models have obtained remarkable performance. Its success cannot be achieved without the re-introduction of multi-scale feature fusion in the encoder. However, the excessively increased tokens in multi-scale features, especially for about 75% of low-level features, are quite computationally inefficient, which hinders real applications of DETR models. In this paper, we present Lite DETR, a simple yet efficient end-to-end object detection framework that can effectively reduce the GFLOPs of the detection head by 60% while keeping 99% of the original performance. Specifically, we design an efficient encoder block to update high-level features (corresponding to small-resolution feature maps) and low-level features (corresponding to large-resolution feature maps) in an interleaved way. In addition, to better fuse cross-scale features, we develop a key-aware deformable attention to predict more reliable attention weights. Comprehensive experiments validate the effectiveness and efficiency of the proposed Lite DETR, and the efficient encoder strategy can generalize well across existing DETR-based models. The code will be available in https://***/IDEA-Research/LiteDETR.

关键词： vision applications and systems

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

作者： Hoque, Oishee Bintey Swarup, Samarth Adiga, Abhijin Nouwakpo, Sayjro Kossi Marathe, Madhav Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA Univ Virginia Biocomplex Inst Charlottesville VA 22903 USA ARS USDA Kimberly ID USA

ISBN: (纸本)9798350365474

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： computer vision Irrigation Mapping Patch Scaling Remote Sensing Segmentation Transfer learning

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MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation

MAIR: Multi-view Attention Inverse Rendering with 3D Spatial...

引用

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

作者： Choi, JunYong Lee, SeokYeong Park, Haesol Jung, Seung-Won Kim, Ig-Jae Cho, Junghyun Korea Inst Sci & Technol KIST Seoul South Korea Korea Univ Seoul South Korea Univ Sci & Technol KIST Sch AI Robot Koto City Japan Yonsei Univ Yonsei KIST Convergence Res Inst Seoul South Korea

ISBN: (纸本)9798350301298

We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-view HDR synthetic dataset, scene-level inverse rendering has mainly been studied using single-view image. We were able to successfully perform scene-level inverse rendering using multi-view images by expanding OpenRooms dataset and designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Our experiments show that the proposed method not only achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene. Also, our sophisticated 3D spatially-varying lighting volume allows for photorealistic object insertion in any 3D location.

关键词： vision + graphics

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Good is Bad: Causality Inspired Cloth-debiasing for Cloth-changing Person Re-identification

Good is Bad: Causality Inspired Cloth-debiasing for Cloth-ch...

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

作者： Yang, Zhengwei Lin, Meng Zhong, Xian Wu, Yu Wang, Zheng Wuhan Univ Inst Artificial Intelligence Natl Engn Res Ctr Multimedia Software Wuhan Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ Technol Hubei Key Lab Multimedia & Network Commun Engn Wuhan Peoples R China Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan Peoples R China

ISBN: (纸本)9798350301298

Entangled representation of clothing and identity (ID)intrinsic clues are potentially concomitant in conventional person Re-IDentification (ReID). Nevertheless, eliminating the negative impact of clothing on ID remains challenging due to the lack of theory and the difficulty of isolating the exact implications. In this paper, a causality-based Auto-Intervention Model, referred to as AIM1, is first proposed to mitigate clothing bias for robust cloth-changing person ReID (CC-ReID). Specifically, we analyze the effect of clothing on the model inference and adopt a dual-branch model to simulate causal intervention. Progressively, clothing bias is eliminated automatically with model training. AIM is encouraged to learn more discriminative ID clues that are free from clothing bias. Extensive experiments on two standard CC-ReID datasets demonstrate the superiority of the proposed AIM over other state-of-the-art methods.

关键词： detection recognition: Categorization retrieval

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