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共 20949 条记录，以下是911-920 订阅

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MixTeacher: Mining Promising Labels with Mixed Scale Teacher for Semi-Supervised Object Detection

MixTeacher: Mining Promising Labels with Mixed Scale Teacher...

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

作者： Liu, Liang Zhang, Boshen Zhang, Jiangning Zhang, Wuhao Gan, Zhenye Tian, Guanzhong Zhu, Wenbing Wang, Yabiao Wang, Chengjie Tencent Youtu Lab Shenzhen Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Zhejiang Univ Ningbo Res Inst Hangzhou Zhejiang Peoples R China Rongcheer Co Ltd Suzhou Peoples R China

ISBN: (纸本)9798350301298

Scale variation across object instances remains a key challenge in object detection task. Despite the remarkable progress made by modern detection models, this challenge is particularly evident in the semi-supervised case. While existing semi-supervised object detection methods rely on strict conditions to filter high-quality pseudo labels from network predictions, we observe that objects with extreme scale tend to have low confidence, resulting in a lack of positive supervision for these objects. In this paper, we propose a novel framework that addresses the scale variation problem by introducing a mixed scale teacher to improve pseudo label generation and scale-invariant learning. Additionally, we propose mining pseudo labels using score promotion of predictions across scales, which benefits from better predictions from mixed scale features. Our extensive experiments on MS COCO and PASCAL VOC benchmarks under various semi-supervised settings demonstrate that our method achieves new state-of-the-art performance. The code and models are available at https: //***/lliuz/MixTeacher.

关键词： detection recognition: Categorization retrieval

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Learning Transferable Compound Expressions from Masked AutoEncoder Pretraining

Learning Transferable Compound Expressions from Masked AutoE...

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

作者： Qiu, Feng Du, Wining Zhang, Wei Liu, Chen Li, Lincheng Guo, Tianchen Yu, Xin Netease Fuxi AI Lab Guangzhou Peoples R China Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9798350365474

Video-based Compound Expression recognition (CER) aims to identify compound expressions in everyday interactions per frame. Unlike rapid progress in Facial Expression recognition (FER) for the basic emotions (e.g., surprised, sad, and fearful), CER with the compound emotions (e.g., fearfully surprised, and sadly fearful) remains underexplored, with an evident gap in the availability of substantial datasets. In this paper, we design a framework to demonstrate the feasibility of predicting compound expressions in-the-wild without relying on domain-specific supervision. To be specific, we first train a model on a large-scale facial dataset using the Masked Autoencoder (MAE) approach to learn comprehensive facial features. Then, to tailor it for facial expression analysis, we fine-tune the ViT encoder on an Action Unit (AU) detection task. To address the issue of insufficient data, we transform the task of recognizing compound emotions into a multi-label recognition task for basic emotions. We train a network by fine-tuning the pretrained ViT encoder to predict the probability of each basic emotion, and then combine these probabilities to arrive at the final prediction for the compound emotions. Experiments conducted on the C-EXPR-DB dataset demonstrate the effectiveness of our framework in the frame-by-frame prediction of compound expressions in-the-wild. Our framework is recognized as the leading solution in the Compound Expression (CE) recognition Challenge in the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW). More information for the Competition can be found in: 6th ABAW.

关键词： affective behavior analysis compound expression recognition

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Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger

Backdoor Attacks Against Deep Image Compression via Adaptive...

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

作者： Yu, Yi Wang, Yufei Yang, Wenhan Lu, Shijian Tan, Yap-Peng Kot, Alex C. Nanyang Technol Univ Singapore Singapore Peng Cheng Lab Shenzhen Peoples R China Nanyang Technol Univ IGP ROSE Singapore Singapore

ISBN: (纸本)9798350301298

Recent deep-learning-based compression methods have achieved superior performance compared with traditional approaches. However, deep learning models have proven to be vulnerable to backdoor attacks, where some specific trigger patterns added to the input can lead to malicious behavior of the models. In this paper, we present a novel backdoor attack with multiple triggers against learned image compression models. Motivated by the widely used discrete cosine transform (DCT) in existing compression systems and standards, we propose a frequency-based trigger injection model that adds triggers in the DCT domain. In particular, we design several attack objectives for various attacking scenarios, including: 1) attacking compression quality in terms of bit-rate and reconstruction quality;2) attacking task-driven measures, such as down-stream face recognition and semantic segmentation. Moreover, a novel simple dynamic loss is designed to balance the influence of different loss terms adaptively, which helps achieve more efficient training. Extensive experiments show that with our trained trigger injection models and simple modification of encoder parameters (of the compression model), the proposed attack can successfully inject several backdoors with corresponding triggers in a single image compression model.

关键词： Low-level vision

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Modality-invariant Visual Odometry for Embodied vision

Modality-invariant Visual Odometry for Embodied Vision

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

作者： Memmel, Marius Bachmann, Roman Zamir, Amir Univ Washington Seattle WA 98195 USA Swiss Fed Inst Technol EPFL Lausanne Switzerland

ISBN: (纸本)9798350301298

Effectively localizing an agent in a realistic, noisy setting is crucial for many embodied vision tasks. Visual Odometry (VO) is a practical substitute for unreliable GPS and compass sensors, especially in indoor environments. While SLAM-based methods show a solid performance without large data requirements, they are less flexible and robust w.r.t. to noise and changes in the sensor suite compared to learning-based approaches. Recent deep VO models, however, limit themselves to a fixed set of input modalities, e.g., RGB and depth, while training on millions of samples. When sensors fail, sensor suites change, or modalities are intentionally looped out due to available resources, e.g., power consumption, the models fail catastrophically. Furthermore, training these models from scratch is even more expensive without simulator access or suitable existing models that can be fine-tuned. While such scenarios get mostly ignored in simulation, they commonly hinder a model's reusability in real-world applications. We propose a Transformer-based modality-invariant VO approach that can deal with diverse or changing sensor suites of navigation agents. Our model outperforms previous methods while training on only a fraction of the data. We hope this method opens the door to a broader range of real-world applications that can benefit from flexible and learned VO models.

关键词： Embodied vision: Active agents simulation

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PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification

PIP-Net: Patch-Based Intuitive Prototypes for Interpretable ...

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

作者： Nauta, Meike Schloetterer, Joerg van Keulen, Maurice Seifert, Christin Univ Twente Enschede Netherlands Univ Duisburg Essen Essen Germany

ISBN: (纸本)9798350301298

Interpretable methods based on prototypical patches recognize various components in an image in order to explain their reasoning to humans. However, existing prototype-based methods can learn prototypes that are not in line with human visual perception, i.e., the same prototype can refer to different concepts in the real world, making interpretation not intuitive. Driven by the principle of explainability-bydesign, we introduce PIP-Net (Patch-based Intuitive Prototypes Network): an interpretable image classification model that learns prototypical parts in a self-supervised fashion which correlate better with human vision. PIP-Net can be interpreted as a sparse scoring sheet where the presence of a prototypical part in an image adds evidence for a class. The model can also abstain from a decision for out-of-distribution data by saying "I haven't seen this before". We only use image-level labels and do not rely on any part annotations. PIP-Net is globally interpretable since the set of learned prototypes shows the entire reasoning of the model. A smaller local explanation locates the relevant prototypes in one image. We show that our prototypes correlate with ground-truth object parts, indicating that PIP-Net closes the "semantic gap" between latent space and pixel space. Hence, our PIP-Net with interpretable prototypes enables users to interpret the decision making process in an intuitive, faithful and semantically meaningful way. Code is available at https://***/M- Nauta/PIPNet.

关键词： Explainable computer vision

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Multi-Label Compound Expression recognition: C-EXPR Database & Network

Multi-Label Compound Expression Recognition: C-EXPR Database...

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

作者： Kollias, Dimitrios Queen Mary Univ London London England

ISBN: (纸本)9798350301298

Research in automatic analysis of facial expressions mainly focuses on recognising the seven basic ones. However, compound expressions are more diverse and represent the complexity and subtlety of our daily affective displays more accurately. Limited research has been conducted for compound expression recognition (CER), because only a few databases exist, which are small, lab controlled, im-balanced and static. In this paper we present an in-thewild A/V database, C-EXPR-DB, consisting of 400 videos of 200K frames, annotated in terms of 13 compound expressions, valence-arousal emotion descriptors, action units, speech, facial landmarks and attributes. We also propose C-EXPR-NET, a multi-task learning (MTL) method for CER and AU detection (AU-D);the latter task is introduced to enhance CER performance. For AU-D we incorporate AU semantic description along with visual information. For CER we use a multi-label formulation and the KL-divergence loss. We also propose a distribution matching loss for coupling CER and AU-D tasks to boost their performance and alleviate negative transfer (i.e., when MT model's performance is worse than that of at least one single-task model). An extensive experimental study has been conducted illustrating the excellent performance of C-EXPR-NET, validating the theoretical claims. Finally, C-EXPR-NET is shown to effectively generalize its knowledge in new emotion recognition contexts, in a zero-shot manner.

关键词： body gesture Humans: Face movement pose

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VILA: Learning Image Aesthetics from User Comments with vision-Language Pretraining

VILA: Learning Image Aesthetics from User Comments with Visi...

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

作者： Ke, Junjie Ye, Keren Yu, Jiahui Wu, Yonghui Milanfar, Peyman Yang, Feng Google Res New York NY 10019 USA

ISBN: (纸本)9798350301298

Assessing the aesthetics of an image is challenging, as it is influenced by multiple factors including composition, color, style, and high-level semantics. Existing image aesthetic assessment (IAA) methods primarily rely on human-labeled rating scores, which oversimplify the visual aesthetic information that humans perceive. Conversely, user comments offer more comprehensive information and are a more natural way to express human opinions and preferences regarding image aesthetics. In light of this, we propose learning image aesthetics from user comments, and exploring vision-language pretraining methods to learn multimodal aesthetic representations. Specifically, we pretrain an image-text encoder-decoder model with image-comment pairs, using contrastive and generative objectives to learn rich and generic aesthetic semantics without human labels. To efficiently adapt the pretrained model for downstream IAA tasks, we further propose a lightweight rank-based adapter that employs text as an anchor to learn the aesthetic ranking concept. Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines. With only minimal fine-tuning parameters using the proposed adapter module, our model achieves state-of-the-art IAA performance over the AVA dataset.(1)

关键词： Computational imaging

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DETRs with Hybrid Matching

DETRs with Hybrid Matching

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

作者： Jia, Ding Yuan, Yuhui He, Haodi Wu, Xiaopei Yu, Haojun Lin, Weihong Sun, Lei Zhang, Chao Hu, Han Peking Univ Beijing Peoples R China Stanford Univ Stanford CA USA Zhejiang Univ Hangzhou Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9798350301298

One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections. This end-to-end signature is important for the versatility of DETR, and it has been generalized to broader vision tasks. However, we note that there are few queries assigned as positive samples and the one-to-one set matching significantly reduces the training efficacy of positive samples. We propose a simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with an auxiliary one-to-many matching branch during training. Our hybrid strategy has been shown to significantly improve accuracy. In inference, only the original one-to-one match branch is used, thus maintaining the end-to-end merit and the same inference efficiency of DETR. The method is named H-DETR, and it shows that a wide range of representative DETR methods can be consistently improved across a wide range of visual tasks, including Deformable-DETR, PETRv2, PETR, and TransTrack, among others. Code is available at: https://***/HDETR.

关键词： detection recognition: Categorization retrieval

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Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation

Guiding Pseudo-labels with Uncertainty Estimation for Source...

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

作者： Litrico, Mattia Del Bue, Alessio Morerio, Pietro Ist Italiano Tecnol Pattern Anal & Comp Vision PAVIS Genoa Italy

ISBN: (纸本)9798350301298

Standard Unsupervised Domain Adaptation (UDA) methods assume the availability of both source and target data during the adaptation. In this work, we investigate Source-free Unsupervised Domain Adaptation (SF-UDA), a specific case of UDA where a model is adapted to a target domain without access to source data. We propose a novel approach for the SF-UDA setting based on a loss reweighting strategy that brings robustness against the noise that inevitably affects the pseudo-labels. The classification loss is reweighted based on the reliability of the pseudo-labels that is measured by estimating their uncertainty. Guided by such reweighting strategy, the pseudo-labels are progressively refined by aggregating knowledge from neighbouring samples. Furthermore, a self-supervised contrastive framework is leveraged as a target space regulariser to enhance such knowledge aggregation. A novel negative pairs exclusion strategy is proposed to identify and exclude negative pairs made of samples sharing the same class, even in presence of some noise in the pseudo-labels. Our method outperforms previous methods on three major benchmarks by a large margin. We set the new SF-UDA state-of-the-art on VisDA-C and DomainNet with a performance gain of +1.8% on both benchmarks and on PACS with +12.3% in the single-source setting and +6.6% in multi-target adaptation. Additional analyses demonstrate that the proposed approach is robust to the noise, which results in significantly more accurate pseudo-labels compared to state-of-the-art approaches.

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

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Extending global-local view alignment for self-supervised learning with remote sensing imagery

Extending global-local view alignment for self-supervised le...

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

作者： Wanyan, Xinye Seneviratne, Sachith Shen, Shuchang Kirley, Michael

ISBN: (纸本)9798350365474

Since large number of high-quality remote sensing images are readily accessible, exploiting the corpus of images with less manual annotation draws increasing attention. Self-supervised models acquire general feature representations by formulating a pretext task that generates pseudolabels for massive unlabeled data to provide supervision for training. While prior studies have explored multiple self-supervised learning techniques in remote sensing domain, pretext tasks based on local-global view alignment remain underexplored, despite achieving state-of-the-art results on natural imagery. Inspired by DINO [6], which employs an effective representation learning structure with knowledge distillation based on global-local view alignment, we formulate two pretext tasks for self-supervised learning on remote sensing imagery (SSLRS). Using these tasks, we explore the effectiveness of positive temporal contrast as well as multi-sized views on SSLRS. We extend DINO and propose DINO-MC which uses local views of various sized crops instead of a single fixed size in order to alleviate the limited variation in object size observed in remote sensing imagery. Our experiments demonstrate that even when pre-trained on only 10% of the dataset, DINO-MC performs on par or better than existing state-of-the-art SSLRS methods on multiple remote sensing tasks, while using less computational resources. All codes, models, and results are released at https://***/WennyXY/DINO-MC.

关键词： computer vision remote sensing imagery self-supervised learning

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