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A Two-Stage Shake-Shake Network for Long-Tailed recognition of SAR Aerial View Objects

A Two-Stage Shake-Shake Network for Long-Tailed Recognition ...

引用

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

作者： Li, Gongzhe Pan, Linpeng Qiu, Linwei Tan, Zhiwen Xie, Fengying Zhang, Haopeng Beihang Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Synthetic Aperture Radar (SAR) has received more attention due to its complementary superiority on capturing significant information in the remote sensing area. However, for an Aerial View Object Classification (AVOC) task, SAR images still suffer from the long-tailed distribution of the aerial view objects. This disparity limit the performance of classification methods, especially for the data-sensitive deep learning models. In this paper, we propose a two-stage shake-shake network to tackle the long-tailed learning problem. Specifically, it decouples the learning procedure into the representation learning stage and the classification learning stage. Moreover, we apply the test time augmentation (TTA) and the classification with alternating normalization (CAN) to improve the accuracy. In the PBVS 1 2022 Multi-modal Aerial View Object Classification Challenge Track 1, our method achieves 21.82% and 27.97% accuracy in the development phase and testing phase respectively, which wins the top-tier among all the participants.

关键词： Training Representation learning Feature extraction Radar tracking Radar polarimetry pattern recognition Task analysis

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Cross-modal Target Retrieval for Tracking by Natural Language

Cross-modal Target Retrieval for Tracking by Natural Languag...

引用

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

作者： Li, Yihao Yu, Jun Cai, Zhongpeng Pan, Yuwen Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Tracking by natural language specification in a video is a challenging task in computer vision. Distinct from initializing the target state only by the bounding box in the first frame, language specification has a strong potential to assist visual object trackers to capture appearance variation and eliminate semantic ambiguity of the tracked object. In this paper, we carefully design a unified local-global-search framework from the perspective of cross-modal retrieval, including a local tracker, an adaptive retrieval switch module, and a target-specific retrieval module. The adaptive retrieval switch module aligns semantics from the visual signal and the lingual description of the target using three sub-modules, i.e., object-aware attention memory, part-aware cross-attention, and vision-language contrast, which achieve an automatic switch between local search and global search. When booting the global search mechanism, the target-specific retrieval module relocalizes the missing target in the image-wide range via an efficient vision-language guided proposal selector and target-text match. Numerous experimental results on three prevailing benchmarks show the effectiveness and generalization of our framework.

关键词： Visualization computer vision Target tracking Natural languages Semantics Switches Benchmark testing

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Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture

Multi-encoder Network for Parameter Reduction of a Kernel-ba...

引用

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

作者： Khalifeh, Issa Blanch, Marc Gorriz Izquierdo, Ebroul Mrak, Marta British Broadcasting Corp London W12 7TQ England Queen Mary Univ London London E1 4NS England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Video frame interpolation involves the synthesis of new frames from existing ones. Convolutional neural networks (CNNs) have been at the forefront of the recent advances in this field. One popular CNN-based approach involves the application of generated kernels to the input frames to obtain an interpolated frame. Despite all the benefits interpolation methods offer, many of these networks require a lot of parameters, with more parameters meaning a heavier computational burden. Reducing the size of the model typically impacts performance negatively. This paper presents a method for parameter reduction for a popular flow-less kernel-based network (Adaptive Collaboration of Flows). Through our technique of removing the layers that require the most parameters and replacing them with smaller encoders, we reduce the number of parameters of the network and even achieve better performance compared to the original method. This is achieved by deploying rotation to force each individual encoder to learn different features from the input images. Ablations are conducted to justify design choices and an evaluation on how our method performs on full-length videos is presented.

关键词： Training Interpolation computer vision conferences Force computer architecture pattern recognition

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Watch and Act: Dual Interacting Agents for Automatic Generation of Possession Statistics in Soccer

Watch and Act: Dual Interacting Agents for Automatic Generat...

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

作者： Sarkar, Saikat Mukherjee, Dipti Prasad Chakrabarti, Amlan Univ Calcutta Kolkata India Indian Stat Inst Kolkata India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Pass localization and team identification are two primary tasks for pass-count based possession statistics generation of a soccer match. While the existing works perform these two tasks separately, we propose dual interacting reinforcement learning agents to jointly perform these tasks. The proposed model has a localization agent, that decides which direction to move a temporal window to localize a pass. On the other hand, there is an identification agent that decides if the temporal window contains a pass for team-A (or team-B), or the localization agent needs to readjust the temporal window further. In this multi-agent setup, an agent may communicate by sharing some message to guide the other agent to achieve its task. To achieve this inter-agent communication, we extend the Dueling DQN architecture and share the value of a state as a message to the other agent. Two agents watch, act independently and cooperate with each other in order to detect a valid pass in a soccer video. A novel reward function is proposed that helps the agents to learn the optimal policy. Experiments performed on online videos show that our method is 3% better at localization of pass than the competitive methods.

关键词： Location awareness computer vision conferences Reinforcement learning Games computer architecture Real-time systems

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Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression recognition

Coarse-to-Fine Cascaded Networks with Smooth Predicting for ...

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

作者： Xue, Fanglei Tan, Zichang Zhu, Yu Ma, Zhongsong Guo, Guodong Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Technol & Engn Ctr Space Utilizat Key Lab Space Utilizat Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China Natl Engn Lab Deep Learning Technol & Applicat Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Facial expression recognition plays an important role in human-computer interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with Smooth Predicting (CFC-SP) to improve the performance of facial expression recognition. CFC-SP contains two core components, namely Coarse-to-Fine Cascaded networks (CFC) and Smooth Predicting (SP). For CFC, it first groups several similar emotions to form a rough category, and then employs a network to conduct a coarse but accurate classification. Later, an additional network for these grouped emotions is further used to obtain fine-grained predictions. For SP, it improves the recognition capability of the model by capturing both universal and unique expression features. To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment. Experiments on Aff-Wild2 show the effectiveness of the proposed CFSP. We achieved 3rd place in the Expression Classification Challenge of the 3rd Competition on Affective Behavior Analysis in-the-wild. The code will be released at https://***/BR-IDL/PaddleViT.

关键词： Human computer interaction computer vision Codes Face recognition conferences Behavioral sciences

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Cell Selection-based Data Reduction Pipeline for Whole Slide Image Analysis of Acute Myeloid Leukemia

Cell Selection-based Data Reduction Pipeline for Whole Slide...

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

作者： Kockwelp, Jacqueline Thiele, Sebastian Kockwelp, Pascal Bartsch, Jannis Schliemann, Christoph Angenendt, Linus Risse, Benjamin Univ Munster Munster Germany Univ Med Ctr Munster Munster Germany

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

computer-aided analyses of cells in Whole Slide Images (WSIs) have become an important topic in digital pathology. Despite the recent success of deep learning in biomedical research, these methods are still difficult to apply to multi-gigabyte WSIs. To overcome this difficulty, a variety of patch-based solutions have been introduced, which however all suffer from certain limitations compared to manual examinations and often fail to meet the specificities of cytological inspections. Here we introduce an alternative scheme which incorporates clinical expertise in the selection process to automatically identify the clinically relevant areas. By using a bone marrow smear dataset containing 22-gigapixel images of 153 patients, we introduce a novel pipeline combining unsupervised and supervised methodologies to gradually select the most appropriate single-cell regions, which are subsequently used in multiple medically crucial Acute Myeloid Leukemia (AML) predictions. Our approach is capable of dealing with a variety of common WSI challenges, massively limits the manual annotation effort, reduces the data by a factor of up to 99.9% and achieves super-human performance on the final cytological prediction tasks.

关键词： Deep learning Pathology computer vision Image analysis conferences Pipelines Manuals

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Material Swapping for 3D Scenes using a Learnt Material Similarity Measure

Material Swapping for 3D Scenes using a Learnt Material Simi...

引用

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

作者： Perroni-Scharf, Maxine Sunkavalli, Kalyan Eisenmann, Jonathan Hold-Geoffroy, Yannick Princeton Univ Princeton NJ 08544 USA Adobe Res San Jose CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We present a method for augmenting photo-realistic 3D scene assets by automatically recognizing, matching, and swapping their materials. Our method proposes a material matching pipeline for the efficient replacement of unknown materials with perceptually similar PBR materials from a database, enabling the quick creation of many variations of a given 3D synthetic scene. At the heart of this method is a novel material similarity feature that is learnt, in conjunction with optimal lighting conditions, by fine-tuning a deep neural network on a material classification task using our proposed dataset. Our evaluation demonstrates that lighting optimization improves CNN-based texture feature extraction methods and better estimates material properties. We conduct a series of experiments showing our method's ability to augment photo-realistic indoor scenes using both standard and procedurally generated PBR materials.

关键词： Training Three-dimensional displays Pipelines Neural networks Lighting Feature extraction Rendering (computer graphics)

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Three Stream Graph Attention Network using Dynamic Patch Selection for the classification of micro-expressions

Three Stream Graph Attention Network using Dynamic Patch Sel...

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

作者： Jain, Ankith Kumar, Rakesh Bhanu, Bir Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

To understand the genuine emotions expressed by humans during social interactions, it is necessary to recognize the subtle changes on the face (micro-expressions) demonstrated by an individual. Facial micro-expressions are brief, rapid, spontaneous gestures and non-voluntary facial muscle movements beneath the skin. Therefore, it is a challenging task to classify facial micro-expressions. This paper presents an end-to-end novel three-stream graph attention network model to capture the subtle changes on the face and recognize micro-expressions (MEs) by exploiting the relationship between optical flow magnitude, optical flow direction, and the node locations features. A facial graph representational structure is used to extract the spatial and temporal information using the three frames. The varying dynamic patch size of optical flow features is used to extract the local texture information across each landmark point. The network only utilizes the landmark points location features and optical flow information across these points and generates good results for the classification of MEs. A comprehensive evaluation of SAMM and the CASME II datasets demonstrates the high efficacy, efficiency, and generalizability of the proposed approach and achieves better results than the state-of-the-art methods.

关键词： Emotion recognition computer vision Face recognition conferences Feature extraction Skin Facial muscles

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Classification of Facial Expression In-the-Wild based on Ensemble of Multi-head Cross Attention Networks

Classification of Facial Expression In-the-Wild based on Ens...

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

作者： Jeong, Jae Yeop Hong, Yeong-Gi Kim, Daun Jeong, Jin-Woo Jung, Yuchul Kim, Sang-Ho Seoul Natl Univ Sci & Technol Dept Data Sci Gongreung Ro 232 Seoul South Korea Kumoh Natl Inst Technol Daehak Ro 61 Gumi South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

How to build a system for robust classification and recognition of facial expressions has been one of the most important research issues for successful interactive computing applications. However, previous datasets and studies mainly focused on facial expression recognition in a controlled/lab setting, therefore, could hardly be generalized in a more practical and real-life environment. The Affective Behavior Analysis in-the-wild (ABAW) 2022 competition released a dataset consisting of various video clips of facial expressions in-the-wild. In this paper, we propose a method based on the ensemble of multi-head cross attention networks to address the facial expression classification task introduced in the ABAW 2022 competition. We built a uni-task approach for this task, achieving the average F1-score of 34.60 on the validation set and 33.77 on the test set, ranking second place on the final leaderboard.

关键词： Gold computer vision Face recognition conferences Estimation Multitasking Behavioral sciences

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CLIP-Guided vision-Language Pre-training for Question Answering in 3D Scenes

CLIP-Guided Vision-Language Pre-training for Question Answer...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Parelli, Maria Delitzas, Alexandros Hars, Nikolas Vlassis, Georgios Anagnostidis, Sotirios Bachmann, Gregor Hofmann, Thomas Eth Zurich Switzerland

ISBN: (纸本)9798350302493

Training models to apply linguistic knowledge and visual concepts from 2D images to 3D world understanding is a promising direction that researchers have only recently started to explore. In this work, we design a novel 3D pre-training vision-Language method that helps a model learn semantically meaningful and transferable 3D scene point cloud representations. We inject the representational power of the popular CLIP model into our 3D encoder by aligning the encoded 3D scene features with the corresponding 2D image and text embeddings produced by CLIP. To assess our model's 3D world reasoning capability, we evaluate it on the downstream task of 3D Visual Question Answering. Experimental quantitative and qualitative results show that our pre-training method outperforms state-of-the-art works in this task and leads to an interpretable representation of 3D scene features. © 2023 ieee.

关键词： computer vision

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