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共 11282 条记录，以下是701-710 订阅

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Light Source Separation and Intrinsic Image Decomposition under AC Illumination

Light Source Separation and Intrinsic Image Decomposition un...

引用

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

作者： Yoshida, Yusaku Kawahara, Ryo Okabe, Takahiro Kyushu Inst Technol Dept Artificial Intelligence 680-4 Kawazu Iizuka Fukuoka 8208502 Japan

ISBN: (纸本)9798350301298

Artificial light sources are often powered by an electric grid, and then their intensities rapidly oscillate in response to the grid's alternating current (AC). Interestingly, the flickers of scene radiance values due to AC illumination are useful for extracting rich information on a scene of interest. In this paper, we show that the flickers due to AC illumination is useful for intrinsic image decomposition (IID). Our proposed method conducts the light source separation (LSS) followed by the IID under AC illumination. In particular, we reveal the ambiguity in the blind LSS via matrix factorization and the ambiguity in the IID assuming the diffuse reflection model, and then show why and how those ambiguities can be resolved via a physics-based approach. We experimentally confirmed that our method can recover the colors of the light sources, the diffuse reflectance values, and the diffuse and specular intensities (shadings) under each of the light sources, and that the IID under AC illumination is effective for application to auto white balancing.

关键词： Physics-based vision and shape-from-X

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PartMix: Regularization Strategy to Learn Part Discovery for Visible-Infrared Person Re-identification

PartMix: Regularization Strategy to Learn Part Discovery for...

引用

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

作者： Kim, Minsu Kim, Seungryong Park, Jungin Park, Seongheon Sohn, Kwanghoon Yonsei Univ Seoul South Korea Korea Univ Seoul South Korea Korea Inst Sci & Technol Seoul South Korea

ISBN: (纸本)9798350301298

Modern data augmentation using a mixture-based technique can regularize the models from overfitting to the training data in various computer vision applications, but a proper data augmentation technique tailored for the part-based Visible-Infrared person Re-IDentification (VI-ReID) models remains unexplored. In this paper, we present a novel data augmentation technique, dubbed PartMix, that synthesizes the augmented samples by mixing the part descriptors across the modalities to improve the performance of part-based VI-ReID models. Especially, we synthesize the positive and negative samples within the same and across different identities and regularize the backbone model through contrastive learning. In addition, we also present an entropy-based mining strategy to weaken the adverse impact of unreliable positive and negative samples. When incorporated into existing part-based VI-ReID model, PartMix consistently boosts the performance. We conduct experiments to demonstrate the effectiveness of our PartMix over the existing VI-ReID methods and provide ablation studies.

关键词： detection recognition: Categorization retrieval

来源：评论

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Two-way Multi-Label Loss

Two-way Multi-Label Loss

引用

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

作者： Kobayashi, Takumi Natl Inst Adv Ind Sci & Technol Tokyo Japan Univ Tsukuba Tsukuba Japan

ISBN: (纸本)9798350301298

A natural image frequently contains multiple classification targets, accordingly providing multiple class labels rather than a single label per image. While the single-label classification is effectively addressed by applying a softmax cross-entropy loss, the multi-label task is tackled mainly in a binary cross-entropy (BCE) framework. In contrast to the softmax loss, the BCE loss involves issues regarding imbalance as multiple classes are decomposed into a bunch of binary classifications;recent works improve the BCE loss to cope with the issue by means of weighting. In this paper, we propose a multi-label loss by bridging a gap between the softmax loss and the multi-label scenario. The proposed loss function is formulated on the basis of relative comparison among classes which also enables us to further improve discriminative power of features by enhancing classification margin. The loss function is so flexible as to be applicable to a multi-label setting in two ways for discriminating classes as well as samples. In the experiments on multi-label classification, the proposed method exhibits competitive performance to the other multi-label losses, and it also provides transferrable features on single-label ImageNet training. Codes are available at https: //***/tk1980/TwowayMultiLabelLoss.

关键词： detection recognition: Categorization retrieval

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X-Pruner: eXplainable Pruning for vision Transformers

X-Pruner: eXplainable Pruning for Vision Transformers

引用

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

作者： Yu, Lu Xiang, Wei James Cook Univ Townsville Australia La Trobe Univ Melbourne Australia

ISBN: (纸本)9798350301298

Recently vision transformer models have become prominent models for a range of tasks. These models, however, usually suffer from intensive computational costs and heavy memory requirements, making them impractical for deployment on edge platforms. Recent studies have proposed to prune transformers in an unexplainable manner, which overlook the relationship between internal units of the model and the target class, thereby leading to inferior performance. To alleviate this problem, we propose a novel explainable pruning framework dubbed X-Pruner, which is designed by considering the explainability of the pruning criterion. Specifically, to measure each prunable unit's contribution to predicting each target class, a novel explainability-aware mask is proposed and learned in an end-to-end manner. Then, to preserve the most informative units and learn the layer-wise pruning rate, we adaptively search the layer-wise threshold that differentiates between unpruned and pruned units based on their explainability-aware mask values. To verify and evaluate our method, we apply the X-Pruner on representative transformer models including the DeiT and Swin Transformer. Comprehensive simulation results demonstrate that the proposed X-Pruner outperforms the state-of-the-art black-box methods with significantly reduced computational costs and slight performance degradation. Code is available at https: //***/vickyyu90/XPruner.

关键词： Explainable computer vision

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Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network

Weakly Supervised Video Emotion Detection and Prediction via...

引用

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

作者： Zhang, Zhicheng Wang, Lijuan Yang, Jufeng Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China

ISBN: (纸本)9798350301298

Automatically predicting the emotions of user-generated videos (UGVs) receives increasing interest recently. However, existing methods mainly focus on a few key visual frames, which may limit their capacity to encode the context that depicts the intended emotions. To tackle that, in this paper, we propose a cross-modal temporal erasing network that locates not only keyframes but also context and audio-related information in a weakly-supervised manner. In specific, we first leverage the intra- and inter-modal relationship among different segments to accurately select keyframes. Then, we iteratively erase keyframes to encourage the model to concentrate on the contexts that include complementary information. Extensive experiments on three challenging video emotion benchmarks demonstrate that our method performs favorably against state-of-the-art approaches. The code is released on https://***/nku-zhichengzhang/WECL.

关键词： vision applications and systems

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Knowledge Distillation for Efficient Instance Semantic Segmentation with Transformers

Knowledge Distillation for Efficient Instance Semantic Segme...

引用

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

作者： Li, Maohui Halstead, Michael McCool, Chris Univ Bonn Bonn Germany Lamarr Inst Machine Learning & Artificial Intelli Dortmund Germany

ISBN: (纸本)9798350365474

Instance-based semantic segmentation provides detailed per-pixel scene understanding information crucial for both computer vision and robotics applications. However, state-of-the-art approaches such as Mask2Former are computationally expensive and reducing this computational burden while maintaining high accuracy remains challenging. Knowledge distillation has been regarded as a potential way to compress neural networks, but to date limited work has explored how to apply this to distill information from the output queries of a model such as Mask2Former. In this paper, we match the output queries of the student and teacher models to enable a query-based knowledge distillation scheme. We independently match the teacher and the student to the groundtruth and use this to define the teacher to student relationship for knowledge distillation. Using this approach we show that it is possible to perform knowledge distillation where the student models can have a lower number of queries and the backbone can be changed from a Transformer architecture to a convolutional neural network architecture. Experiments on two challenging agricultural datasets, sweet pepper (BUP20) and sugar beet (SB20), and Cityscapes demonstrate the efficacy of our approach. Across the three datasets the student models obtain an average absolute performance improvement in AP of 1.8 and 1.9 points for ResNet-50 and Swin-Tiny backbone respectively. To the best of our knowledge, this is the first work to propose knowledge distillation schemes for instance semantic segmentation with transformer-based models.

关键词： computer vision for Agriculture Automation Knowledge Distillation Efficient Instance Segmentation Transformercomputer vision for Agriculture Automation Knowledge Distillation Efficient Instance Segmentation Transformer

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GIVL: Improving Geographical Inclusivity of vision-Language Models with Pre-Training Methods

GIVL: Improving Geographical Inclusivity of Vision-Language ...

引用

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

作者： Yin, Da Gao, Feng Thattai, Govind Johnston, Michael Chang, Kai -Wei Univ Calif Los Angeles Los Angeles CA 90095 USA Amazon Alexa AI Lexington MA USA

ISBN: (纸本)9798350301298

A key goal for the advancement of AI is to develop technologies that serve the needs not just of one group but of all communities regardless of their geographical region. In fact, a significant proportion of knowledge is locally shared by people from certain regions but may not apply equally in other regions because of cultural differences. If a model is unaware of regional characteristics, it may lead to performance disparity across regions and result in bias against underrepresented groups. We propose GIVL, a Geographically Inclusive vision-and-Language Pre-trained model. There are two attributes of geo-diverse visual concepts which can help to learn geodiverse knowledge: 1) concepts under similar categories have unique knowledge and visual characteristics, 2) concepts with similar visual features may fall in completely different categories. Motivated by the attributes, we design new pre-training objectives Image-Knowledge Matching (IKM) and Image Edit Checking (IEC) to pre-train GIVL. Compared with similar-size models pre-trained with similar scale of data, GIVL achieves state-of-the-art (SOTA) and more balanced performance on geo-diverse V&L tasks.

关键词： language reasoning vision

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Prompt-Guided Zero-Shot Anomaly Action recognition using Pretrained Deep Skeleton Features

Prompt-Guided Zero-Shot Anomaly Action Recognition using Pre...

引用

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

作者： Sato, Fumiaki Hachiuma, Ryo Sekii, Taiki Konica Minolta Inc Tokyo Japan

ISBN: (纸本)9798350301298

This study investigates unsupervised anomaly action recognition, which identifies video-level abnormal-human-behavior events in an unsupervised manner without abnormal samples, and simultaneously addresses three limitations in the conventional skeleton-based approaches: target domain-dependent DNN training, robustness against skeleton errors, and a lack of normal samples. We present a unified, user prompt-guided zero-shot learning framework using a target domain-independent skeleton feature extractor, which is pretrained on a large-scale action recognition dataset. Particularly, during the training phase using normal samples, the method models the distribution of skeleton features of the normal actions while freezing the weights of the DNNs and estimates the anomaly score using this distribution in the inference phase. Additionally, to increase robustness against skeleton errors, we introduce a DNN architecture inspired by a point cloud deep learning paradigm, which sparsely propagates the features between joints. Furthermore, to prevent the unobserved normal actions from being misidentified as abnormal actions, we incorporate a similarity score between the user prompt embeddings and skeleton features aligned in the common space into the anomaly score, which indirectly supplements normal actions. On two publicly available datasets, we conduct experiments to test the effectiveness of the proposed method with respect to abovementioned limitations.

关键词： Video: Action and event understanding

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CAGE: Circumplex Affect Guided Expression Inference

CAGE: Circumplex Affect Guided Expression Inference

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

作者： Wagner, Niklas Maetzler, Felix Vossberg, Samed R. Schneider, Helen Pavlitska, Svetlana Zoellner, J. Marius Karlsruhe Inst Technol KIT Karlsruhe Germany FZI Res Ctr Informat Technol Karlsruhe Germany

ISBN: (纸本)9798350365474

Understanding emotions and expressions is a task of interest across multiple disciplines, especially for improving user experiences. Contrary to the common perception, it has been shown that emotions are not discrete entities but instead exist along a continuum. People understand discrete emotions differently due to a variety of factors, including cultural background, individual experiences, and cognitive biases. Therefore, most approaches to expression understanding, particularly those relying on discrete categories, are inherently biased. In this paper, we present a comparative in-depth analysis of two common datasets (AffectNet and EMOTIC) equipped with the components of the circumplex model of affect. Further, we propose a model for the prediction of facial expressions tailored for lightweight applications. Using a small-scaled MaxViT-based model architecture, we evaluate the impact of discrete expression category labels in training with the continuous valence and arousal labels. We show that considering valence and arousal in addition to discrete category labels helps to significantly improve expression inference. The proposed model outperforms the current state-of-the-art models on AffectNet, establishing it as the best-performing model for inferring valence and arousal achieving a 7% lower RMSE. Training scripts and trained weights to reproduce our results can be found here: https:// ***/wagner-niklas/CAGE_expression_inference.

关键词： computer vision Expression Inference Transformer

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SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection

SAFDNet: A Simple and Effective Network for Fully Sparse 3D ...

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

作者： Zhang, Gang Chen, Junnan Gao, Guohuan Li, Jianmin Liu, Si Hu, Xiaolin Tsinghua Univ Inst AI Dept Comp Sci & Technol BNRist Beijing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Beijing Inst Technol Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Tsinghua Lab Brain & Intelligence THBI Beijing Peoples R China Chinese Inst Brain Res CIBR Beijing 100010 Peoples R China

ISBN: (纸本)9798350353006

LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing high-performing 3D object detectors usually build dense feature maps in the backbone network and prediction head. However, the computational costs introduced by the dense feature maps grow quadratically as the perception range increases, making these models hard to scale up to long-range detection. Some recent works have attempted to construct fully sparse detectors to solve this issue;nevertheless, the resulting models either rely on a complex multi-stage pipeline or exhibit inferior performance. In this work, we propose a fully sparse adaptive feature diffusion network (SAFDNet) for LiDAR-based 3D object detection. In SAFDNet, an adaptive feature diffusion strategy is designed to address the center feature missing problem. We conducted extensive experiments on Waymo Open, nuScenes, and Argoverse2 datasets. SAFDNet performed slightly better than the previous SOTA on the first two datasets but much better on the last dataset, which features long-range detection, verifying the efficacy of SAFDNet in scenarios where long-range detection is required. Notably, on Argoverse2, SAFDNet surpassed the previous best hybrid detector HEDNet by 2.6% mAP while being 2.1x faster, and yielded 2.1% mAP gains over the previous best sparse detector FSDv2 while being 1.3x faster. The code will be available at https://***/zhanggang001/HEDNet.

关键词： 3D Object Detection computer vision Feature Diffusion Fully Sparse Detector

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