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Movie Genre Classification by Language Augmentation and Shot Sampling

Movie Genre Classification by Language Augmentation and Shot...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Zhang, Zhongping Gul, Yiwen Plummer, Bryan A. Miao, Xin Liu, Jiayi Wang, Huayan Boston Univ Boston MA 02215 USA Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Video-based movie genre classification has garnered considerable attention due to its various applications in recommendation systems. Prior work has typically addressed this task by adapting models from traditional video classification tasks, such as action recognition or event detection. However, these models often neglect language elements (e.g., narrations or conversations) present in videos, which can implicitly convey high-level semantics of movie genres, like storylines or background context. Additionally, existing approaches are primarily designed to encode the entire content of the input video, leading to inefficiencies in predicting movie genres. Movie genre prediction may require only a few shots(1) to accurately determine the genres, rendering a comprehensive understanding of the entire video unnecessary. To address these challenges, we propose a Movie genre Classification method based on Language augmentatIon and shot samPling (Movie-CLIP). Movie-CLIP mainly consists of two parts: a language augmentation module to recognize language elements from the input audio, and a shot sampling module to select representative shots from the entire video. We evaluate our method on MovieNet and Condensed Movies datasets, achieving approximate 6 - 9% improvement in mean Average Precision (mAP) over the baselines. We also generalize Movie-CLIP to the scene boundary detection task, achieving 1.1% improvement in Average Precision (AP) over the state-of-theart. We release our implementation at this http URL.

关键词： Algorithms Applications Arts / games / social media Video recognition and understanding

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Robust TRISO-fueled Pebble Identification by Digit recognition

Robust TRISO-fueled Pebble Identification by Digit Recogniti...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Kenia, Roshan Mendil, Jihane Jasim, Ahmed Al-Dahhan, Muthanna Yin, Zhaozheng SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA Missouri Univ Sci & Technol Dept Chem & Biochem Engn Rolla MO USA

ISBN: (纸本)9798350318920;9798350318937

Nuclear power plays a vital role in providing reliable and clean energy to fulfill increasing demands in electricity worldwide. It continues to be an essential source of national power supply as growing concerns about fossil fuel depletion, global warming, and emissions require utilizing sustainable energy sources. One area contributing to the growth of nuclear power is the development of reactors that have enhanced protection and security, thermal efficiency, and design. Reactor efficiency can be studied by the burn-up that occurs when a TRISO-fueled pebble is inserted into the nuclear core and subsequently removed. The levels of burnup are measured based on the length of time the pebble spends within the core. In our design, each pebble is numbered by multiple digits printed in six locations using Ultra-High Temperature Ceramic paint. Naturally, computer vision techniques can be used to identify and time each pebble based on its digits as it enters and exits the core. We present a deep learning approach that successfully tags each pebble by identifying its digits from a video stream of the entrance and exit of the core. In a multi-step method, we extract only the clearest and most useful views of the pebble's digits to classify as it rolls by. This algorithm is robust against issues that occur for objects in movement such as motion blur, rotations, and glare. We outperform other state-of-the-art optical character recognition (OCR) models that fail to identify digits that are in motion. Our approach creates a safer and more efficient way to measure burnup within a core while contributing to the improvement of nuclear power produced by reactors.(1)

关键词： Applications Applications Embedded sensing / real-time techniques Visualization

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RMFER: Semi-supervised Contrastive Learning for Facial Expression recognition with Reaction Mashup Video

RMFER: Semi-supervised Contrastive Learning for Facial Expre...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Cho, Yunseong Kim, Chanwoo Cho, Hoseong Ku, Yunhoe Kim, Eunseo Boboev, Muhammadjon Lee, Joonseok Baek, Seungryul UNIST Ulsan South Korea SNOW Corp Seongnam South Korea Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

Facial expression recognition (FER) has greatly benefited from deep learning but still faces challenges in dataset collection due to the nuanced nature of facial expressions. In this study, we present a novel unlabeled dataset and semi-supervised contrastive learning framework that utilizes Reaction Mashup (RM) videos, a video that includes multiple individuals reacting to the same film. We created a Reaction Mashup dataset (RMset) from these videos. Our framework integrates three distinct modules: A classification module for supervised facial expression categorization, an attention module for inter-sample attention learning, and a contrastive module for attention-based contrastive learning using RMset. We utilize both the classification and attention modules for the initial training, subsequently incorporating the contrastive module to enhance the learning process. Our experiments demonstrate that our method improves feature learning and outperforms state-of-the-art models on three benchmark FER datasets. Codes are available at https://***/yunseongcho/RMFER.

关键词： Algorithms Algorithms Biometrics body pose Datasets and evaluations face gesture

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CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation

CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Vo...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Hung, Alex Ling Yu Zheng, Haoxin Zhao, Kai Du, Xiaoxi Pang, Kaifeng Miao, Qi Raman, Steven S. Terzopoulos, Demetri Sung, Kyunghyun Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9798350318920;9798350318937

A large portion of volumetric medical data, especially magnetic resonance imaging (MRI) data, is anisotropic, as the through-plane resolution is typically much lower than the in-plane resolution. Both 3D and purely 2D deep learning-based segmentation methods are deficient in dealing with such volumetric data since the performance of 3D methods suffers when confronting anisotropic data, and 2D methods disregard crucial volumetric information. Insufficient work has been done on 2.5D methods, in which 2D convolution is mainly used in concert with volumetric information. These models focus on learning the relationship across slices, but typically have many parameters to train. We offer a Cross-Slice Attention Module (CSAM) with minimal trainable parameters, which captures information across all the slices in the volume by applying semantic, positional, and slice attention on deep feature maps at different scales. Our extensive experiments using different network architectures and tasks demonstrate the usefulness and generalizability of CSAM. Associated code is available at https://***/aL3x-O-o-Hung/CSAM.

关键词： Algorithms Applications Biomedical / healthcare / medicine Image recognition and understanding

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Interactive Segmentation for Diverse Gesture TypesWithout Context

Interactive Segmentation for Diverse Gesture TypesWithout Co...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Myers-Dean, Josh Fan, Yifei Price, Brian Chan, Wilson Gurari, Danna Univ Colorado Boulder Boulder CO 80309 USA Adobe Res San Jose CA USA Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350318920;9798350318937

Interactive segmentation entails a human marking an image to guide how a model either creates or edits a segmentation. Our work addresses limitations of existing methods: they either only support one gesture type for marking an image (e.g., either clicks or scribbles) or require knowledge of the gesture type being employed, and require specifying whether marked regions should be included versus excluded in the final segmentation. We instead propose a simplified interactive segmentation task where a user only must mark an image, where the input can be of any gesture type without specifying the gesture type. We support this new task by introducing the first interactive segmentation dataset with multiple gesture types as well as a new evaluation metric capable of holistically evaluating interactive segmentation algorithms. We then analyze numerous interactive segmentation algorithms, including ones adapted for our novel task. While we observe promising performance overall, we also highlight areas for future improvement. To facilitate further extensions of this work, we publicly share our new dataset at https://***/joshmyersdean/dig.

关键词： Algorithms Algorithms Applications Arts / games / social media Datasets and evaluations Image recognition and understanding

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FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation

FreMIM: Fourier Transform Meets Masked Image Modeling for Me...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Wane, Wenxuan Wane, Jing Chen, Chen Jiao, Jianbo Cai, Yuanxiu Song, Shanshan Li, Jiangyun Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA Univ Birmingham Sch Comp Sci Birmingham W Midlands England

ISBN: (纸本)9798350318920;9798350318937

The research community has witnessed the powerful potential of self-supervised Masked Image Modeling (MIM), which enables the models capable of learning visual representation from unlabeled data. In this paper, to incorporate both the crucial global structural information and local details for dense prediction tasks, we alter the perspective to the frequency domain and present a new MIM-based framework named FreMIM for self-supervised pre-training to better accomplish medical image segmentation tasks. Based on the observations that the detailed structural information mainly lies in the high-frequency components and the highlevel semantics are abundant in the low-frequency counterparts, we further incorporate multi-stage supervision to guide the representation learning during the pre-training phase. Extensive experiments on three benchmark datasets show the superior advantage of our FreMIM over previous state-of-the-art MIM methods. Compared with various baselines trained from scratch, our FreMIM could consistently bring considerable improvements to model performance. The code will be publicly available at https: //***/jingw193/FreMIM.

关键词： Algorithms Applications Biomedical / healthcare / medicine Image recognition and understanding

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Deep Depth from Focus with Differential Focus Volume

Deep Depth from Focus with Differential Focus Volume

引用

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

作者： Yang, Fengting Huang, Xiaolei Zhou, Zihan Penn State Univ University Pk PA 16802 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Depth-from-focus (DFF) is a technique that infers depth using the focus change of a camera. In this work, we propose a convolutional neural network (CNN) to find the best-focused pixels in a focal stack and infer depth from the focus estimation. The key innovation of the network is the novel deep differential focus volume (DFV). By computing the first-order derivative with the stacked features over different focal distances, DFV is able to capture both the focus and context information for focus analysis. Besides, we also introduce a probability regression mechanism for focus estimation to handle sparsely sampled focal stacks and provide uncertainty estimation to the final prediction. Comprehensive experiments demonstrate that the proposed model achieves state-of-the-art performance on multiple datasets with good generalizability and fast speed.

关键词： Technological innovation computer vision Uncertainty Computational modeling Estimation computer architecture pattern recognition

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G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation

G-CASCADE: Efficient Cascaded Graph Convolutional Decoding f...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Rahman, Md Mostafijur Marculescu, Radu Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350318920;9798350318937

In this paper, we are the first to propose a new graph convolution-based decoder namely, Cascaded Graph Convolutional Attention Decoder (G-CASCADE), for 2D medical image segmentation. G-CASCADE progressively refines multi-stage feature maps generated by hierarchical transformer encoders with an efficient graph convolution block. The encoder utilizes the self-attention mechanism to capture long-range dependencies, while the decoder refines the feature maps preserving long-range information due to the global receptive fields of the graph convolution block. Rigorous evaluations of our decoder with multiple transformer encoders on five medical image segmentation tasks (i.e., Abdomen organs, Cardiac organs, Polyp lesions, Skin lesions, and Retinal vessels) show that our model outperforms other state-of-the-art (SOTA) methods. We also demonstrate that our decoder achieves better DICE scores than the SOTA CASCADE decoder with 80.8% fewer parameters and 82.3% fewer FLOPs. Our decoder can easily be used with other hierarchical encoders for general-purpose semantic and medical image segmentation tasks. The implementation can be found at: https://***/SLDGroup/G-CASCADE.

关键词： Algorithms Algorithms and algorithms Applications Biomedical / healthcare / medicine formulations Image recognition and understanding Machine learning architectures

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Solving the Plane-Sphere Ambiguity in Top-Down Structure-from-Motion

Solving the Plane-Sphere Ambiguity in Top-Down Structure-fro...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Haalck, Lars Risse, Benjamin Univ Munster Inst Geoinformat Munster Germany Univ Munster Inst Comp Sci Munster Germany

ISBN: (纸本)9798350318920;9798350318937

Drone-based land surveys and tracking applications with a moving camera require three-dimensional reconstructions from videos recorded using a downward facing camera and are usually generated by Structure-from-Motion (SfM) algorithms. Unfortunately, monocular SfM pipelines can fail in the presence of lens distortion due to a critical configuration resulting in a plane-sphere ambiguity which is characterized by severe curvatures of the reconstructions and erroneous relative camera pose estimations. We propose a 4-point minimal solver for the relative pose estimation for two views sharing the same radial distortion parameters (i.e. from the same camera) with a viewing direction perpendicular to the ground plane. To extract 3D reconstructions from continuous videos, the relative pose of pairwise frames is estimated by using the solver with RANSAC and the Sampson error where globally consistent distortion parameters are determined by taking the medial of all values. Moreover, we propose an additional regularizer for the final bundle adjustment to remove any remaining curvature of the reconstruction if necessary. We tested our methods on synthetic and real-world data and our results demonstrate a significant reduction of curvature and more accurate relative pose estimations. Our algorithm can be easily integrated into existing pipelines and is therefore a practical solution to resolve the plane-sphere ambiguity in a variety of top-down SfM applications.

关键词： 3D computer vision Algorithms Algorithms Image recognition and understanding

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Mitigate Domain Shift by Primary-Auxiliary Objectives Association for Generalizing Person ReID

Mitigate Domain Shift by Primary-Auxiliary Objectives Associ...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Li, Qilei Gong, Shaogang Queen Mary Univ London London England

ISBN: (纸本)9798350318920;9798350318937

While deep learning has significantly improved ReID model accuracy under the independent and identical distribution (IID) assumption, it has also become clear that such models degrade notably when applied to an unseen novel domain due to unpredictable/unknown domain shift. Contemporary domain generalization (DG) ReID models struggle in learning domain-invariant representation solely through training on an instance classification objective. We consider that a deep learning model is heavily influenced and therefore biased towards domain-specific characteristics, e.g., background clutter, scale and viewpoint variations, limiting the generalizability of the learned model, and hypothesize that the pedestrians are domain invariant owning they share the same structural characteristics. To enable the ReID model to be less domain-specific from these pure pedestrians, we introduce a method that guides model learning of the primary ReID instance classification objective by a concurrent auxiliary learning objective on weakly labeled pedestrian saliency detection. To solve the problem of conflicting optimization criteria in the model parameter space between the two learning objectives, we introduce a Primary-Auxiliary Objectives Association (PAOA) mechanism to calibrate the loss gradients of the auxiliary task towards the primary learning task gradients. Benefiting from the harmonious multitask learning design, our model can be extended with the recent test-time diagram to form the PAOA+, which performs on-the-fly optimization against the auxiliary objective in order to maximize the model's generative capacity in the test target domain. Experiments demonstrate the superiority of the proposed PAOA model.

关键词： Algorithms Algorithms Image recognition and understanding Video recognition and understanding

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