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High-level context representation for emotion recognition in images

High-level context representation for emotion recognition in...

引用

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

作者： Costa, Willams de Lima Martinez, Estefania Talavera Figueiredo, Lucas Silva Teichrieb, Veronica Univ Fed Pernambuco Voxar Labs Ctr Informat Ave Jorn Anibal Fernandes Recife PE Brazil Univ Twente Drienerlolaan 5 NL-7522 NB Enschede Netherlands Univ Fed Rural Pernambuco Unidade Acad Belo Jardim PE-166100 Belo Jardim Brazil

ISBN: (纸本)9798350302493

Emotion recognition is the task of classifying perceived emotions in people. Previous works have utilized various nonverbal cues to extract features from images and correlate them to emotions. Of these cues, situational context is particularly crucial in emotion perception since it can directly influence the emotion of a person. In this paper, we propose an approach for high-level context representation extraction from images. The model relies on a single cue and a single encoding stream to correlate this representation with emotions. Our model competes with the state-of-the-art, achieving an mAP of 0.3002 on the EMOTIC dataset while also being capable of execution on consumer-grade hardware at approximate to 90 frames per second. Overall, our approach is more efficient than previous models and can be easily deployed to address real-world problems related to emotion recognition.

关键词： Emotion recognition

来源：评论

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Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision

Towards Trustable Skin Cancer Diagnosis via Rewriting Model'...

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

作者： Yan, Siyuan Yu, Zhen Zhang, Xuelin Mahapatra, Dwarikanath Chandra, Shekhar S. Janda, Monaca Soyer, Peter Ge, Zongyuan Monash Univ Clayton Vic Australia Monash Med AI Grp Melbourne Vic Australia Univ Queensland Brisbane Qld Australia Incept Inst AI Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350301298

Deep neural networks have demonstrated promising performance on image recognition tasks. However, they may heavily rely on confounding factors, using irrelevant artifacts or bias within the dataset as the cue to improve performance. When a model performs decision-making based on these spurious correlations, it can become untrustable and lead to catastrophic outcomes when deployed in the real-world scene. In this paper, we explore and try to solve this problem in the context of skin cancer diagnosis. We introduce a human-in-the-loop framework in the model training process such that users can observe and correct the model's decision logic when confounding behaviors happen. Specifically, our method can automatically discover confounding factors by analyzing the co-occurrence behavior of the samples. It is capable of learning confounding concepts using easily obtained concept exemplars. By mapping the black-box model's feature representation onto an explainable concept space, human users can interpret the concept and intervene via first order-logic instruction. We systematically evaluate our method on our newly crafted, well-controlled skin lesion dataset and several public skin lesion datasets. Experiments show that our method can effectively detect and remove confounding factors from datasets without any prior knowledge about the category distribution and does not require fully annotated concept labels. We also show that our method enables the model to focus on clinical-related concepts, improving the model's performance and trustworthiness during model inference.

关键词： cell microscopy Medical and biological vision

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Towards All-in-one Pre-training via Maximizing Multi-modal Mutual Information

Towards All-in-one Pre-training via Maximizing Multi-modal M...

引用

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

作者： Su, Weijie Zhu, Xizhou Tao, Chenxin Lu, Lewei Li, Bin Huang, Gao Qiao, Yu Wang, Xiaogang Zhou, Jie Dai, Jifeng Univ Sci & Technol China Beijing Peoples R China SenseTime Res Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

To effectively exploit the potential of large-scale models, various pre-training strategies supported by massive data from different sources are proposed, including supervised pre-training, weakly-supervised pre-training, and self-supervised pre-training. It has been proved that combining multiple pre-training strategies and data from various modalities/sources can greatly boost the training of large-scale models. However, current works adopt a multi-stage pre-training system, where the complex pipeline may increase the uncertainty and instability of the pre-training. It is thus desirable that these strategies can be integrated in a single-stage manner. In this paper, we first propose a general multi-modal mutual information formula as a unified optimization target and demonstrate that all mainstream approaches are special cases of our framework. Under this unified perspective, we propose an all-in-one single-stage pre-training approach, named Maximizing Multi-modal Mutual Information Pre-training (M3I Pretraining). Our approach achieves better performance than previous pre-training methods on various vision benchmarks, including ImageNet classification, COCO object detection, LVIS long-tailed object detection, and ADE20k semantic segmentation. Notably, we successfully pre-train a billion-level parameter image backbone and achieve state-of-the-art performance on various benchmarks under public data setting. Code shall be released at https://***/OpenGVLab/M3I-Pretraining.

关键词： detection recognition: Categorization retrieval

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Network Expansion For Practical Training Acceleration

Network Expansion For Practical Training Acceleration

引用

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

作者： Ding, Ning Tang, Yehui Han, Kai Xu, Chao Wang, Yunhe Peking Univ Sch Intelligence Sci & Technol Natl Key Lab Gen AI Beijing Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Recently, the sizes of deep neural networks and training datasets both increase drastically to pursue better performance in a practical sense. With the prevalence of transformer-based models in vision tasks, even more pressure is laid on the GPU platforms to train these heavy models, which consumes a large amount of time and computing resources as well. Therefore, it's crucial to accelerate the training process of deep neural networks. In this paper, we propose a general network expansion method to reduce the practical time cost of the model training process. Specifically, we utilize both width- and depth-level sparsity of dense models to accelerate the training of deep neural networks. Firstly, we pick a sparse sub-network from the original dense model by reducing the number of parameters as the starting point of training. Then the sparse architecture will gradually expand during the training procedure and finally grow into a dense one. We design different expanding strategies to grow CNNs and ViTs respectively, due to the great heterogeneity in between the two architectures. Our method can be easily integrated into popular deep learning frameworks, which saves considerable training time and hardware resources. Extensive experiments show that our acceleration method can significantly speed up the training process of modern vision models on general GPU devices with negligible performance drop (e.g. 1.42x faster for ResNet-101 and 1.34x faster for DeiT-base on ImageNet-1k). The code is available at https://***/huawei-noah / Efficient - Computing / tree / master / TrainingAcceleration / NetworkExpansion and https : / / gitee. com / mindspore / hub / blob / master / mshub _ res / assets / noah - cvlab / gpu / 1.8/networkexpansion_v1.0_***.

关键词： Efficient and scalable vision

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Advancing Visual Grounding with Scene Knowledge: Benchmark and Method

Advancing Visual Grounding with Scene Knowledge: Benchmark a...

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

作者： Chen, Zhihong Zhang, Ruifei Song, Yibing Wan, Xiang Li, Guanbin Chinese Univ Hong Kong Shenzhen Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China Tencent AI Lab Shenzhen Peoples R China Fudan Univ AI3 Inst Shanghai Peoples R China

ISBN: (纸本)9798350301298

Visual grounding (VG) aims to establish fine-grained alignment between vision and language. Ideally, it can be a testbed for vision-and-language models to evaluate their understanding of the images and texts and their reasoning abilities over their joint space. However, most existing VG datasets are constructed using simple description texts, which do not require sufficient reasoning over the images and texts. This has been demonstrated in a recent study [27], where a simple LSTM-based text encoder without pretraining can achieve state-of-the-art performance on mainstream VG datasets. Therefore, in this paper, we propose a novel benchmark of Scene Knowledge-guided Visual Grounding (SK-VG), where the image content and referring expressions are not sufficient to ground the target objects, forcing the models to have a reasoning ability on the long-form scene knowledge. To perform this task, we propose two approaches to accept the triple-type input, where the former embeds knowledge into the image features before the image-query interaction;the latter leverages linguistic structure to assist in computing the image-text matching. We conduct extensive experiments to analyze the above methods and show that the proposed approaches achieve promising results but still leave room for improvement, including performance and interpretability. The dataset and code are available at https://***/zhjohnchan/SK-VG.

关键词： language reasoning vision

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Hint-Aug: Drawing Hints from Foundation vision Transformers towards Boosted Few-shot Parameter-Efficient Tuning

Hint-Aug: Drawing Hints from Foundation Vision Transformers ...

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

作者： Yu, Zhongzhi Wu, Shang Fu, Yonggan Zhang, Shunyao Lin, Yingyan (Celine) Georgia Inst Technol Atlanta GA 30332 USA Rice Univ Houston TX USA

ISBN: (纸本)9798350301298

Despite the growing demand for tuning foundation vision transformers (FViTs) on downstream tasks, fully unleashing FViTs' potential under data-limited scenarios (e.g., few-shot tuning) remains a challenge due to FViTs' data-hungry nature. Common data augmentation techniques fall short in this context due to the limited features contained in the few-shot tuning data. To tackle this challenge, we first identify an opportunity for FViTs in few-shot tuning: pretrained FViTs themselves have already learned highly representative features from large-scale pretraining data, which are fully preserved during widely used parameter-efficient tuning. We thus hypothesize that leveraging those learned features to augment the tuning data can boost the effectiveness of few-shot FViT tuning. To this end, we propose a framework called Hint-based Data Augmentation (Hint-Aug), which aims to boost FViT in few-shot tuning by augmenting the over-fitted parts of tuning samples with the learned features of pretrained FViTs. Specifically, Hint-Aug integrates two key enablers: (1) an Attentive Over-fitting Detector (AOD) to detect over-confident patches of foundation ViTs for potentially alleviating their over-fitting on the few-shot tuning data and (2) a Confusion-based Feature Infusion (CFI) module to infuse easy-to-confuse features from the pretrained FViTs with the over-confident patches detected by the above AOD in order to enhance the feature diversity during tuning. Extensive experiments and ablation studies on five datasets and three parameter-efficient tuning techniques consistently validate Hint-Aug's effectiveness: 0.04% similar to 32.91% higher accuracy over the state-of-the-art (SOTA) data augmentation method under various low-shot settings. For example, on the Pet dataset, Hint-Aug achieves a 2.22% higher accuracy with 50% less training data over SOTA data augmentation methods.

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

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NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud

NerVE: Neural Volumetric Edges for Parametric Curve Extracti...

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

作者： Zhu, Xiangyu Du, Dong Chen, Weikai Zhao, Zhiyou Nie, Yinyu Han, Xiaoguang CUHKSZ SSE Shenzhen Peoples R China CUHKSZ FNii Shenzhen Peoples R China Tencent Amer Palo Alto CA USA Tech Univ Munich Munich Germany

ISBN: (纸本)9798350301298

Extracting parametric edge curves from point clouds is a fundamental problem in 3D vision and geometry processing. Existing approaches mainly rely on keypoint detection, a challenging procedure that tends to generate noisy output, making the subsequent edge extraction error-prone. To address this issue, we propose to directly detect structured edges to circumvent the limitations of the previous point-wise methods. We achieve this goal by presenting NerVE, a novel neural volumetric edge representation that can be easily learned through a volumetric learning framework. NerVE can be seamlessly converted to a versatile piece-wise linear (PWL) curve representation, enabling a unified strategy for learning all types of free-form curves. Furthermore, as NerVE encodes rich structural information, we show that edge extraction based on NerVE can be reduced to a simple graph search problem. After converting NerVE to the PWL representation, parametric curves can be obtained via off-the-shelf spline fitting algorithms. We evaluate our method on the challenging ABC dataset [19]. We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin.

关键词： vision + graphics

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Gaussian Label Distribution Learning for Spherical Image Object Detection

Gaussian Label Distribution Learning for Spherical Image Obj...

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

作者： Xu, Hang Liu, Xinyuan Zhao, Qiang Ma, Yike Yan, Chenggang Dai, Feng Hangzhou Dianzi Univ Hangzhou Peoples R China Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Spherical image object detection emerges in many applications from virtual reality to robotics and automatic driving, while many existing detectors use ln-norms loss for regression of spherical bounding boxes. There are two intrinsic flaws for ln-norms loss, i.e., independent optimization of parameters and inconsistency between metric (dominated by IoU) and loss. These problems are common in planar image detection but more significant in spherical image detection. Solution for these problems has been extensively discussed in planar image detection by using IoU loss and related variants. However, these solutions cannot be migrated to spherical image object detection due to the undifferentiable of the Spherical IoU (SphIoU). In this paper, we design a simple but effective regression loss based on Gaussian Label Distribution Learning (GLDL) for spherical image object detection. Besides, we observe that the scale of the object in a spherical image varies greatly. The huge differences among objects from different categories make the sample selection strategy based on SphIoU challenging. Therefore, we propose GLDL-ATSS as a better training sample selection strategy for objects of the spherical image, which can alleviate the drawback of IoU threshold-based strategy of scale-sample imbalance. Extensive results on various two datasets with different baseline detectors show the effectiveness of our approach.

关键词： computer vision theory

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CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching

CORA: Adapting CLIP for Open-Vocabulary Detection with Regio...

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

作者： Wu, Xiaoshi Zhu, Feng Zhao, Rui Li, Hongsheng Chinese Univ Hong Kong Multimedia Lab Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China Ctr Perceptual & Interact Intelligence CPII Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects. We identify the two core obstacles that need to be tackled when incorporating these models into detector training: (1) the distribution mismatch that happens when applying a VL-model trained on whole images to region recognition tasks;(2) the difficulty of localizing objects of unseen classes. To overcome these obstacles, we propose CORA, a DETR-style framework that adapts CLIP for Open-vocabulary detection by Region prompting and Anchor pre-matching. Region prompting mitigates the whole-to-region distribution gap by prompting the region features of the CLIP-based region classifier. Anchor pre-matching helps learning generalizable object localization by a class-aware matching mechanism. We evaluate CORA on the COCO OVD benchmark, where we achieve 41.7 AP50 on novel classes, which outperforms the previous SOTA by 2.4 AP50 even without resorting to extra training data. When extra training data is available, we train CORA(+) on both ground-truth base-category annotations and additional pseudo bounding box labels computed by CORA. CORA(+) achieves 43.1 AP50 on the COCO OVD benchmark and 28.1 box APr on the LVIS OVD benchmark. The code is available at https://***/tgxs002/CORA.

关键词： detection recognition: Categorization retrieval

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MAESTER: Masked Autoencoder Guided Segmentation at Pixel Resolution for Accurate, Self-Supervised Subcellular Structure recognition

MAESTER: Masked Autoencoder Guided Segmentation at Pixel Res...

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

作者： Xie, Ronald Pang, Kuan Bader, Gary D. Wang, Bo Univ Toronto Toronto ON M5S 1A1 Canada Vector Inst Toronto ON Canada Univ Hlth Network Toronto ON Canada Donnelly Ctr Toronto ON Canada

ISBN: (纸本)9798350301298

Accurate segmentation of cellular images remains an elusive task due to the intrinsic variability in morphology of biological structures. Complete manual segmentation is unfeasible for large datasets, and while supervised methods have been proposed to automate segmentation, they often rely on manually generated ground truths which are especially challenging and time consuming to generate in biology due to the requirement of domain expertise. Furthermore, these methods have limited generalization capacity, requiring additional manual labels to be generated for each dataset and use case. We introduce MAESTER (Masked AutoEncoder guided SegmenTation at pixEl Resolution), a self-supervised method for accurate, subcellular structure segmentation at pixel resolution. MAESTER treats segmentation as a representation learning and clustering problem. Specifically, MAESTER learns semantically meaningful token representations of multi-pixel image patches while simultaneously maintaining a sufficiently large field of view for contextual learning. We also develop a cover-and-stride inference strategy to achieve pixel-level subcellular structure segmentation. We evaluated MAESTER on a publicly available volumetric electron microscopy (VEM) dataset of primary mouse pancreatic islets beta cells and achieved upwards of 29.1% improvement over state-of-the-art under the same evaluation criteria. Furthermore, our results are competitive against supervised methods trained on the same tasks, closing the gap between self-supervised and supervised approaches. MAESTER shows promise for alleviating the critical bottleneck of ground truth generation for imaging related data analysis and thereby greatly increasing the rate of biological discovery. Code available at https : / / github. com / bowang-lab/MAESTER

关键词： cell microscopy Medical and biological vision

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