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检索条件"任意字段=Conference on Image and Video Processing II"

共 1192 条记录，以下是1-10 订阅

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TempA-VLP: Temporal-Aware Vision-Language Pretraining for Longitudinal Exploration in Chest X-Ray image

TempA-VLP: Temporal-Aware Vision-Language Pretraining for Lo...

引用

2025 IEEE/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Yang, Zhuoyi Shen, Liyue University of Michigan United States

ISBN: (纸本)9798331510831

Longitudinal medical image processing is a significant task to understand the dynamic changes of disease by taking and comparing image series over time, providing insights into how conditions evolve and enabling more accurate di-agnosis and treatment planning. While recent advance-ments in biomedical Vision-Language Pre-training (VLP) have enabled label-efficient representation learning with paired medical images and reports, existing methods pri-marily pair a single image with the corresponding textual report, limiting their ability to capture temporal relation-ships. To address this limitation, it is essential to learn temporal-aware cross-modal representations from sequen-tial medical images and text reports that highlight the tem-poral changes occurring between examinations. Specifi-cally, we introduce TempA- Vlp, a temporal-aware vision language pre-training framework with a cross-exam en-coder to integrate the information from both prior and cur-rent examinations. This approach enables the model to capture dynamic representations that reflect disease pro-gression over time, which allows us to (i) achieve state-of-the-art performance in disease progression classification, (ii) localize dynamic progression regions across consecutive examinations, as demonstrated in our new task, dynamic phrase grounding on the Chest-imagenome Gold dataset, and (iii) highlight progression localized regions, often rele-vant to lesion areas, which in turn improves disease classi-fication tasks on a single image. © 2025 IEEE.

关键词： image coding

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Classifier-Guided Captioning Across Modalities

Classifier-Guided Captioning Across Modalities

引用

2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Shaulov, Ariel Shaharabany, Tal Shaar, Eitan Chechik, Gal Wolf, Lior Tel Aviv University Israel Bar-Ilan University Israel NVIDIA Israel

ISBN: (纸本)9798350368741

Most current captioning systems use language models trained on data from specific settings, such as image-based captioning via Amazon Mechanical Turk, limiting their ability to generalize to other modality distributions and contexts. This limitation hinders performance in tasks like audio or video captioning, where different semantic cues are needed. Addressing this challenge is crucial for creating more adaptable and versatile captioning frameworks applicable across diverse real-world contexts. In this work, we introduce a method to adapt captioning networks to the semantics of alternative settings, such as capturing audibility in audio captioning, where it is crucial to describe sounds and their sources. Our framework consists of two main components: (i) a frozen captioning system incorporating a language model (LM), and (ii) a text classifier that guides the captioning system. The classifier is trained on a dataset automatically generated by GPT-4, using tailored prompts specifically designed to enhance key aspects of the generated captions. Importantly, the framework operates solely during inference, eliminating the need for further training of the underlying captioning model. We evaluated the framework on various models and modalities, with a focus on audio captioning, and report promising results. Notably, when combined with an existing zero-shot audio captioning system, our framework improves its quality and sets state-of-the-art performance in zero-shot audio captioning. © 2025 IEEE.

关键词： Audio image-captioning Language Models

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Granularity-Aware Contrastive Learning for Fine-Grained Action Recognition

Granularity-Aware Contrastive Learning for Fine-Grained Acti...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Hailun Zhang Xinrui Wang Qijun Zhao College of Computer Science Sichuan University Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The contrastive learning paradigm has been widely used for image-language pre-training and extended to video-text tuning. These approaches aim to maximize the similarity between positive sample pairs while minimizing that of negative ones through an alignment objective. Their performance is highly affected by the definition of positive and negative pairs which depends on the granularity of label classification. This effect is particularly apparent in video action recognition, where different fine-grained actions may belong to a shared coarse label. Therefore, indiscriminately treating a video sample and labels that are not identical at the fine-grained level but share the same coarse label as negative pairs leads to pushing the sample apart from the cluster of its basic coarse action. Such conflict can potentially prevent the model from pulling the sample and its target label closer. For a balanced understanding of coarse and fine-grained distinctions, we propose the Granularity-Aware Contrastive Learning (GACon) framework to improve contrastive learning for fine-grained action recognition. This is achieved through (i) a refined definition of sample-label relation and alignment objectives, and (ii) the exchange of coarse and fine-grained information between two granularity-distinct experts. Experiments on four benchmarks of fine-grained action recognition show the superiority of our proposed GACon compared to existing approaches.

关键词： Adaptation models Contrastive learning Speech recognition Bidirectional control Signal processing Benchmark testing Acoustics Speech processing Tuning Information exchange

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Adaptive Aspect Ratios with Patch-Mixup-ViT-based Vehicle ReID

Adaptive Aspect Ratios with Patch-Mixup-ViT-based Vehicle Re...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Mei Qiu Lauren Ann Christopher Stanley Chien Lingxi Li Purdue University USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Vision Transformers (ViTs) have shown exceptional performance in vehicle re-identification (ReID) tasks. However, non-square aspect ratios of image or video inputs can negatively impact re-identification accuracy. To address this challenge, we propose a novel, human perception driven, and general ViT-based ReID framework that fuses models trained on various aspect ratios. Our key contributions are threefold: (i) We analyze the impact of aspect ratios on performance using the VeRi-776 and VehicleID datasets, providing guidance for input settings based on the distribution of original image aspect ratios. (ii) We introduce patch-wise mixup strategy during ViT patchification (guided by spatial attention scores) and implement uneven stride for better alignment with object aspect ratios. (iii) We propose a dynamic feature fusion ReID network to enhance model robustness. Our method outperforms state-of-the-art transformer-based approaches on both datasets, with only a minimal increase in inference time per *** code is released here: Adaptive_AR_PM_TransReID.

关键词： Computer vision Adaptation models Fuses Signal processing Transformers Data augmentation Robustness Data models Vehicle dynamics Speech processing

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Classifier-Guided Captioning Across Modalities

Classifier-Guided Captioning Across Modalities

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ariel Shaulov Tal Shaharabany Eitan Shaar Gal Chechik Lior Wolf Tel Aviv University Bar-Ilan University Bar-Ilan University NVIDIA Israel

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Training Adaptation models Limiting Semantics Text categorization Focusing Data models Speech processing Optimization Context modeling

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CRISP-ii: Coarse-grained Reconfigurable image Stream Processor for image-processing and Intelligent Operations in QFHD video Cameras

CRISP-II: Coarse-grained Reconfigurable Image Stream Process...

引用

IEEE Asian Solid State Circuits conference (A-SSCC)

作者： Cheng, Teng-Yuan Chen, Liang-Gee Chien, Shao-Yi Natl Taiwan Univ Grad Inst Elect Engn Media IC & Syst Lab Taipei Taiwan Natl Taiwan Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9781467327718

A 372.3mW coarse-grained reconfigurable image stream processor, CRISP-ii, for image-processing and intelligent operations is implemented in TSMC 90nm low-power technology with a core size of 15.21mm(2). With the proposed multi-stream mode unified protocol, hierarchical ring architecture, and adaptive computing engine, CRISP-ii is able to solve the emerging scalability and flexibility problems. It could execute several advanced operations efficiently, like high-dynamic-imaging and face detection. Compared with other state-of-the-art processors, CRISP-ii achieves 8.42 times power efficiency than the highly parallel SIMD processor, and meets the real-time requirement of video cameras with QFHD (3840x2160) resolution.

关键词： video cameras PROCESSOR core size ring structures configurability Surgery Facial Recognition streams computing engine imagery (Psychotherapy) image images

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TULIP ii: A FLEXIBLE MULTIPROCESSOR SYSTEM FOR image processing BY CONTROLLING THE video DATA FLOWS.

TULIP II: A FLEXIBLE MULTIPROCESSOR SYSTEM FOR IMAGE PROCESS...

引用

IEEE 1985 Proceedings of the International conference on Cybernetics and Society.

作者： Tatsumi, Shoji Ohno, Hiroshi Ohnishi, Hiroyuki Horiba, Takahiro Toyohashi Univ of Technology Toyohashi Jpn Toyohashi Univ of Technology Toyohashi Jpn

The authors describe an image processing system, TULIP-ii, which can be reconfigured into many processing modes, such as independent, image division, and pipeline, by controlling the flow of image data on the video bus. The goal of this system is to execute consecutively the work from image preprocessing to image understanding with a high speed. TULIP-ii is a master-slave multiprocessor which consists of one master processor module (MPM) and 16 slave processor modules (SPM). A characteristic of TULIP-ii lies in the structure of each SPM and a bus system between each SPM. Each SPM has its own video bus with eight bits of parallel image data and one status bit. In this way, a bus system is built which physically connects each individual SPM to all others. Also, each individual SPM has a mapping data memory and two image data memories. The mapping data memory plays an important role for TULIP-ii to take flexible structures. According to data stored in the mapping data memory, video data flows are controlled.

关键词： COMPUTER SYSTEMS, DIGITAL

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Proceedings of 2003 International conference on image processing - Volume ii

Proceedings of 2003 International Conference on Image Proces...

引用

Proceedings: 2003 International conference on image processing, ICIP-2003

The proceedings contains 278 papers from the conference on Proceedings of 2003 international conference on image processing- Volume ii. Topics discussed include: object reconstruction and pose indexing by volume feedback;combinational pyramids;scale-sets image analysis;fault-tolerant tracking for gait analysis;color segmentation by ordered mergings;image fusion with the hermite transform;lossless chain coder for gray edge images;and fast global registration for image processing.

关键词： video signal processing

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Comparison of On-Board and Off-Board processing Power Consumption for Drone Camera images

Comparison of On-Board and Off-Board Processing Power Cons...

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16th International conference on ICT Innovations, ICT Innovations 2024

作者： Mitrev, Atanasko Boris Stojkoska, Biljana Risteska Faculty of Computer Science and Engineering Ss. Cyril and Methodius University in Skopje Skopje Macedonia

ISBN: (纸本)9783031861611

Unmanned aerial vehicles (UAVs) equipped with cameras, capture images and videos from high altitude, usually for surveillance purpose. This paper investigates the power consumption of a drone processing unit under two different operational scenarios: (i) On-Board (local image processing using an onboard Raspberry Pi) and (ii) Off-Board (streaming video over Wi-Fi for remote processing). Therefore, we deployed the well-known YOLO (You Only Look Once) image detection algorithm on the Raspberry Pi and investigated the power consumption for the both scenarios. Contrary to the expectations, our findings prove that energy consumption is very similar in both scenarios. The results of this study are valuable for designers of energy-efficient UAV systems, particularly for applications where prolonged flight duration and real-time data processing are critical. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drones

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SPATIOTEMPORAL SEGMENTATION OF video DATA 2

SPATIOTEMPORAL SEGMENTATION OF VIDEO DATA

引用

conference on image and video processing ii

作者： WANG, JYA ADELSON, EH MIT MEDIA LABCAMBRIDGEMA 02139

ISBN: (纸本)0819414778

image segmentation provides a powerful semantic description of video imagery essential in image understanding and efficient manipulation of image data. In particular, segmentation based on image motion defines regions undergoing similar motion allowing an image coding system to more efficiently represent video sequences. This paper describes a general iterative framework for segmentation of video data. The objective of our spatiotemporal segmentation is to produce a layered image representation of the video for image coding applications whereby video data is simply described as a set of moving layers.

关键词： image segmentation Motion models Motion estimation video Data modeling Affine motion model image compression image processing video coding video processing

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