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检索条件"任意字段=2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024"

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BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping

BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird...

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

作者： Sastry, Srikumar Khanal, Subash Dhakal, Aayush Huang, Di Jacobs, Nathan Washington Univ St Louis MO 63110 USA

ISBN: (纸本)9798350318920;9798350318937

We propose a metadata-aware self-supervised learning (SSL) framework useful for fine-grained classification and ecological mapping of bird species around the world. Our framework unifies two SSL strategies: Contrastive Learning (CL) and Masked Image Modeling (MIM), while also enriching the embedding space with metadata available with ground-level imagery of birds. We separately train uni-modal and cross-modal ViT on a novel cross-view global bird species dataset containing ground-level imagery, metadata (location, time), and corresponding satellite imagery. We demonstrate that our models learn fine-grained and geographically conditioned features of birds, by evaluating on two downstream tasks: fine-grained visual classification (FGVC) and cross-modal retrieval. Pre-trained models learned using our framework achieve SotA performance on FGVC of iNAT-2021 birds and in transfer learning settings for CUB-200-2011 and NABirds datasets. Moreover, the impressive cross-modal retrieval performance of our model enables the creation of species distribution maps across any geographic region. The dataset and source code will be released at https://***/mvrl/BirdSAT.

关键词： Algorithms Animals / Insects Applications Applications Image recognition and understanding Remote Sensing

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FRoG-MOT: Fast and Robust Generic Multiple-Object Tracking by IoU and Motion-State Associations

FRoG-MOT: Fast and Robust Generic Multiple-Object Tracking b...

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

作者： Ogawa, Takuya Shibata, Takashi Hosoi, Toshinori NEC Corp Ltd Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

This paper proposes a generic multi-object tracking (MOT) algorithm that is robust to unexpected motion changes for generic objects. Deep learning has dramatically been improving MOT performances. Nevertheless, state-of-the-art tracking algorithms are still sensitive to unexpected motion changes and the generic object target beyond person tracking. This is because standard MOT benchmark datasets such as MOT17 mainly consist of persons in a crowd, often lacking unexpected shape and motion changes;thus, these issues have yet to be focused on. We propose a simple-yet-effective MOT framework that can dynamically improve tracking continuity by associating each target based on adaptively modified motion states. The keys are 1) to represent the target motions using multiple motion states that have weak correlations with each other and 2) to modify those states that have the lowest similarity to past states as outliers. Our approach can improve trajectory continuity and robustness to unexpected motion changes for generic objects. Comprehensive experiments have confirmed that our framework is comparable to existing state-of-the-art methods on a standard dataset and outperforms those algorithms on the GMOT dataset with an overall 2% improvement in IDF1, a measure of tracking continuity.

关键词： Algorithms Algorithms and algorithms formulations Machine learning architectures Video recognition and understanding

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Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth

Boosting Weakly Supervised Object Detection using Fusion and...

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

作者： Gungor, Cagri Kovashka, Adriana Intelligent Syst Program Pittsburgh PA 15260 USA Univ Pittsburgh Dept Comp Sci Pittsburgh PA USA

ISBN: (纸本)9798350318920;9798350318937

Despite recent attention to depth for various tasks, it is still an unexplored modality for weakly-supervised object detection (WSOD). We propose an amplifier method for enhancing the performance of WSOD by integrating depth information. Our approach can be applied to different WSOD methods based on multiple-instance learning, without necessitating additional annotations or inducing large computational cost. Our proposed method employs monocular depth estimation to obtain hallucinated depth information, which is then incorporated into a Siamese WSOD network using contrastive loss and fusion. By analyzing the relationship between language context and depth, we calculate depth priors to identify the bounding box proposals that may contain an object of interest. These depth priors are then utilized to update the list of pseudo ground-truth boxes, or adjust the confidence of per-box predictions. We evaluate our proposed method on three datasets (COCO, PASCAL VOC, and Conceptual Captions) by implementing it on top of two state-of-the-art WSOD methods, and we demonstrate a substantial enhancement in performance.

关键词： Algorithms Algorithms Image recognition and understanding vision + language and/or other modalities

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Long-Tailed Visual recognition via Self-Heterogeneous Integration with Knowledge Excavation

Long-Tailed Visual Recognition via Self-Heterogeneous Integr...

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

作者： Jin, Yan Li, Mengke Lu, Yang Cheung, Yiu-ming Wang, Hanzi Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart City Xiamen Peoples R China Xiamen Univ Minist Educ China Key Lab Multimedia Trusted Percept & Efficient Co Xiamen Peoples R China Guangdong Lab Artificial Intelligence & Digital E Shenzhen Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Deep neural networks have made huge progress in the last few decades. However, as the real-world data often exhibits a long-tailed distribution, vanilla deep models tend to be heavily biased toward the majority classes. To address this problem, state-of-the-art methods usually adopt a mixture of experts (MoE) to focus on different parts of the long-tailed distribution. Experts in these methods are with the same model depth, which neglects the fact that different classes may have different preferences to be fit by models with different depths. To this end, we propose a novel MoE-based method called Self-Heterogeneous Integration with Knowledge Excavation (SHIKE). We first propose Depth-wise Knowledge Fusion (DKF) to fuse features between different shallow parts and the deep part in one network for each expert, which makes experts more diverse in terms of representation. Based on DKF, we further propose Dynamic Knowledge Transfer (DKT) to reduce the influence of the hardest negative class that has a non-negligible impact on the tail classes in our MoE framework. As a result, the classification accuracy of long-tailed data can be significantly improved, especially for the tail classes. SHIKE achieves the state-of-the-art performance of 56.3%, 60.3%, 75.4% and 41.9% on CIFAR100-LT (IF100), ImageNet-LT, iNaturalist 2018, and Places-LT, respectively. The source code is available at https://***/jinyan-06/SHIKE.

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

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Exploring the Impact of Rendering Method and Motion Quality on Model Performance when Using Multi-view Synthetic Data for Action recognition

Exploring the Impact of Rendering Method and Motion Quality ...

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

作者： Panev, Stanislav Kim, Emily Namburu, Sai Abhishek Si Nikolova, Desislava de Melo, Celso De la Torre, Fernando Hodgins, Jessica Carnegie Mellon Univ Pittsburgh PA 15213 USA Tech Univ Sofia Sofia Bulgaria Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350318920;9798350318937

This paper explores the use of synthetic data in a human action recognition (HAR) task to avoid the challenges of obtaining and labeling real-world datasets. We introduce a new dataset suite comprising five datasets, eleven common human activities, three synchronized camera views (aerial and ground) in three outdoor environments, and three visual domains (real and two synthetic). For the synthetic data, two rendering methods (standard computer graphics and neural rendering) and two sources of human motions (motion capture and video-based motion reconstruction) were employed. We evaluated each dataset type by training popular activity recognition models and comparing the performance on the real test data. Our results show that synthetic data achieve slightly lower accuracy (4-8%) than real data. On the other hand, a model pre-trained on synthetic data and fine-tuned on limited real data surpasses the performance of either domain alone. Standard computer graphics (CG)-rendered data delivers better performance than the data generated from the neural-based rendering method. The results suggest that the quality of the human motions in the training data also affects the test results: motion capture delivers higher test accuracy. Additionally, a model trained on CG aerial view synthetic data exhibits greater robustness against camera viewpoint changes than one trained on real data. See the project page: http://***/REMAG/

关键词： Antennas

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Fog-Resilient Bangla Car Plate recognition using Dark Channel Prior and YOLO

Fog-Resilient Bangla Car Plate Recognition using Dark Channe...

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

作者： Nasim, Hamim Ibne Printia, Fateha Jannat Himel, Mahamudul Hasan Rashid, Rubaba Chowdhury, Iffat Jahan Mondal, Joyanta Jyoti Islam, Md. Farhadul Noor, Jannatun BRAC Univ Comp Sustainabil & Social Good C2SG Res Grp Dhaka Bangladesh Univ Alabama Birmingham Birmingham AL USA

ISBN: (纸本)9798350370287;9798350370713

Despite advancements in Automatic License Plate Detection (ALPD) methods, the majority of them fail to address the diverse image challenges faced in real-world driving scenarios. These challenges encompass issues like low image quality, contrast issues, etc. Factors such as license plate background, horizontal tilt, and adverse weather conditions like rain or fog further impede LP detection and recognition. This research focuses on the localization and recognition of Bangla vehicle plates in foggy conditions through the application of the Dark Channel Prior (DCP) fog-dehazing technique. The selection of Bangla as the target language is motivated by its status as a low-resource language with high digital text complexity, resulting in limited available resources. The proposed method comprises three main phases. The DCP dehazing algorithm reduces fog in input images initially. Then, the YOLOv8 object detection model is used to identify Bangla license plates from dehazed images, followed by OCR for text recognition. This study leverages DCP, YOLOv8, and OCR technologies to enhance the identification of Bangla vehicle plates under hazardous conditions, thereby contributing to the improvement of transportation safety, law enforcement, traffic management, and taxation processes.

关键词： Optical character recognition

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Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation

Masked Collaborative Contrast for Weakly Supervised Semantic...

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

作者： Wu, Fangwen He, Jingxuan Yin, Yufei Hao, Yanbin Huang, Gang Cheng, Lechao Zhejiang Lab Hangzhou Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350318920;9798350318937

This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation. MCC adroitly draws inspiration from masked image modeling and contrastive learning to devise a novel framework that induces keys to contract toward semantic regions. Unlike prevalent techniques that directly eradicate patch regions in the input image when generating masks, we scrutinize the neighborhood relations of patch tokens by exploring masks considering keys on the affinity matrix. Moreover, we generate positive and negative samples in contrastive learning by utilizing the masked local output and contrasting it with the global output. Elaborate experiments on commonly employed datasets evidences that the proposed MCC mechanism effectively aligns global and local perspectives within the image, attaining impressive performance. The source code is available at https://***/fwu11/MCC.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Fine-grained Audible Video Description

Fine-grained Audible Video Description

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

作者： Shen, Xuyang Li, Dong Zhou, Jinxing Qin, Zhen He, Bowen Han, Xiaodong Li, Aixuan Dai, Yuchao Kong, Lingpeng Wang, Meng Qiao, Yu Zhong, Yiran Shanghai Artificial Intelligence Lab Shanghai Peoples R China OpenNLPLab Beijing Peoples R China Hefei Univ Technol Hefei Peoples R China Northwestern Polytech Univ Xian Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350301298

We explore a new task for audio-visual-language modeling called fine-grained audible video description (FAVD). It aims to provide detailed textual descriptions for the given audible videos, including the appearance and spatial locations of each object, the actions of moving objects, and the sounds in videos. Existing visual-language modeling tasks often concentrate on visual cues in videos while undervaluing the language and audio modalities. On the other hand, FAVD requires not only audio-visual-language modeling skills but also paragraph-level language generation abilities. We construct the first fine-grained audible video description benchmark (FAVDBench) to facilitate this research. For each video clip, we first provide a one-sentence summary of the video, i.e., the caption, followed by 4-6 sentences describing the visual details and 12 audio-related descriptions at the end. The descriptions are provided in both English and Chinese. We create two new metrics for this task: an EntityScore to gauge the completeness of entities in the visual descriptions, and an AudioScore to assess the audio descriptions. As a preliminary approach to this task, we propose an audio-visual-language transformer that extends existing video captioning model with an additional audio branch. We combine the masked language modeling and auto-regressive language modeling losses to optimize our model so that it can produce paragraph-level descriptions. We illustrate the efficiency of our model in audio-visual-language modeling by evaluating it against the proposed benchmark using both conventional captioning metrics and our proposed metrics. We further put our benchmark to the test in video generation models, demonstrating that employing fine-grained video descriptions can create more intricate videos than using captions. Code and dataset are available at https://***/OpenNLPLab/FAVDBench. Our online benchmark is available at ***.

关键词： and reasoning language vision

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CIMI4D: A Large Multimodal Climbing Motion Dataset under Human-scene Interactions

CIMI4D: A Large Multimodal Climbing Motion Dataset under Hum...

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

作者： Yan, Ming Wang, Xin Dai, Yudi Shen, Siqi Wen, Chenglu Xu, Lan Ma, Yuexin Wang, Cheng Xiamen Univ Fujian Key Lab Sensing & Comp Smart Cities Xiamen Peoples R China Xiamen Univ Natl Inst Data Sci Hlth & Med Xiamen Peoples R China Xiamen Univ Sch Informat Key Lab Multimedia Trusted Percept & Efficient Co Minist Educ China Xiamen Peoples R China ShanghaiTech Univ Shanghai Engn Res Ctr Intelligent Vision & Imagin Shanghai Peoples R China

ISBN: (纸本)9798350301298

Motion capture is a long-standing research problem. Although it has been studied for decades, the majority of research focus on ground-based movements such as walking, sitting, dancing, etc. Off-grounded actions such as climbing are largely overlooked. As an important type of action in sports and firefighting field, the climbing movements is challenging to capture because of its complex back poses, intricate human-scene interactions, and difficult global localization. The research community does not have an in-depth understanding of the climbing action due to the lack of specific datasets. To address this limitation, we collect CIMI4D, a large rock ClImbing MotIon dataset from 12 persons climbing 13 different climbing walls. The dataset consists of around 180,000 frames of pose inertial measurements, LiDAR point clouds, RGB videos, high-precision static point cloud scenes, and reconstructed scene meshes. Moreover, we frame-wise annotate touch rock holds to facilitate a detailed exploration of human-scene interaction. The core of this dataset is a blending optimization process, which corrects for the pose as it drifts and is affected by the magnetic conditions. To evaluate the merit of CIMI4D, we perform four tasks which include human pose estimations (with/without scene constraints), pose prediction, and pose generation. The experimental results demonstrate that CIMI4D presents great challenges to existing methods and enables extensive research opportunities. We share the dataset with the research community in http://***/cimi4d/.

关键词： body gesture Humans: Face movement pose

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ViLEM: Visual-Language Error Modeling for Image-Text Retrieval

ViLEM: Visual-Language Error Modeling for Image-Text Retriev...

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

作者： Chen, Yuxin Zhang, Zongyang Zhang, Ziqi Qi, Zhongang Yuan, Chunfeng Shan, Ying Li, Bing Hu, Weiming Qie, Xiaohu Wu, JianPing Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Inst Automat Beijing Peoples R China ARC Lab Singapore Singapore Tencent PCG Shenzhen Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Dominant pre-training works for image-text retrieval adopt "dual-encoder" architecture to enable high efficiency, where two encoders are used to extract image and text representations and contrastive learning is employed for global alignment. However, coarse-grained global alignment ignores detailed semantic associations between image and text. In this work, we propose a novel proxy task, named Visual-Language ErrorModeling (ViLEM), to inject detailed image-text association into "dual-encoder" model by "proofreading" each word in the text against the corresponding image. Specifically, we first edit the image-paired text to automatically generate diverse plausible negative texts with pre-trained language models. ViLEM then enforces the model to discriminate the correctness of each word in the plausible negative texts and further correct the wrong words via resorting to image information. Furthermore, we propose a multi-granularity interaction framework to perform ViLEM via interacting text features with both global and local image features, which associates local text semantics with both high-level visual context and multi-level local visual information. Our method surpasses state-of-the-art "dual-encoder" methods by a large margin on the image-text retrieval task and significantly improves discriminativeness to local textual semantics. Our model can also generalize well to video-text retrieval.

关键词： and reasoning language vision

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