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检索条件"任意字段=Conference on Visual Communications and Image Processing 2007"

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IoU-CLIP: IoU-Aware Language-image Model Tuning for Open Vocabulary Object Detection

IoU-CLIP: IoU-Aware Language-Image Model Tuning for Open Voc...

引用

2024 conference on visual communications and image processing

作者： He, Mingzhou Wu, Qingbo Ngan, King Ngi Xiao, Yiming Meng, Fanman Qiu, Heqian Li, Hongliang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Open vocabulary object detection (OVD), which detects novel categories through detectors trained on base categories, has achieved remarkable advancement attributable to large-scale vision-language models, such as CLIP. The prior OVD works mainly focused on improving the classification accuracy of proposals, ignoring the ability of localization for novel categories. In this work, we propose IoU-aware language-image model tuning (IoU-CLIP) for open vocabulary object detection. Specifically, we construct a region image dataset with different IoU and adopt IoU values as labels to fine-tune the CLIP model to learn IoU-aware and class-agnostic semantic prompts and visual embeddings. The fine-tuned IoU-CLIP can predict IoU scores for proposals, which interact with classification scores. Meanwhile, IoU-aware and class-agnostic visual embeddings are utilized for box regression to enhance the generalization of the localization capability. We evaluate our method on the COCO and LVIS OVD benchmarks, outperforming the baseline (RegionCLIP) by 5.5% AP(50) and 5.8% AP on novel categories, respectively, achieving state-of-the-art performance.

关键词： object detection open vocabulary IoU-aware class-agnostic semantic prompts

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End-to-end Prediction of Streaming Video Quality of Experience: Dataset and Approach

End-to-end Prediction of Streaming Video Quality of Experien...

引用

2024 conference on visual communications and image processing

作者： Jia, Ziheng Min, Xiongkuo Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9798331529543;9798331529550

With the rapid development of video-on-demand (VOD) and real-time streaming video technologies, the accurate objective assessment of streaming video Quality of Experience (QoE) has become a focal point for optimizing streaming-related technologies. However, due to the inherent transmission distortions caused by poor Quality of Service (QoS) conditions in streaming videos, such as intermittent stalling, rebuffering, and drastic changes in video sharpness due to bitrate fluctuations, evaluating streaming video QoE presents numerous challenges. This paper introduces a large and diverse in-the-wild streaming video QoE evaluation dataset - the SJLIVE-1k dataset. This work addresses the limitations of corresponding datasets, which lack in-the-wild video sequences under real network conditions and whose amount of video content is insufficient. Furthermore, we propose an end-to-end objective QoE evaluation strategy that extracts video content and QoS features from the video itself without using any extra information. By implementing self-supervised contrastive learning as the "reminder" to bridge the gap between the different types of features, our approach achieves state-of-the-art results across three datasets. Our proposed dataset will be released to facilitate further research.

关键词： Quality of Experience streaming videos contrastive learning

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A MULTI-MODEL FUSION FRAMEWORK FOR NIR-TO-RGB TRANSLATION

A MULTI-MODEL FUSION FRAMEWORK FOR NIR-TO-RGB TRANSLATION

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Yan, Longbin Wang, Xiuheng Zhao, Min Liu, Shumin Chen, Jie Northwestern Polytech Univ Sch Marine Sci & Technol CIAIC Xian Peoples R China Shanghai Shengyao Intelligent Technol Co Ltd Shanghai Peoples R China Blueye Intelligence Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9781728180687

Near-infrared (NIR) images provide spectral information beyond the visible light spectrum and thus are useful in many applications. However, single-channel NIR images contain less information per pixel than RGB images and lack visibility for human perception. Transforming NIR images to RGB images is necessary for performing further analysis and computer vision tasks. In this work, we propose a novel NIR-to-RGB translation method. It contains two sub-networks and a fusion operator. Specifically, a U-net based neural network is used to learn the texture information while a CycleGAN based neural network is adopted to excavate the color information. Finally, a guided filter based fusion strategy is applied to fuse the outputs of these two neural networks. Experiment results show that our proposed method achieves superior NIR-to-RGB translation performance.

关键词： NIR-to-RGB translation U-net CycleGAN guided filter image fusion

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Annotating Only at Definite Pixels: A Novel Weakly Supervised Semantic Segmentation Method for Sea Fog Recognition

Annotating Only at Definite Pixels: A Novel Weakly Supervise...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Zhu, Xun Xu, Mengqiu Wu, Ming Zhang, Chuang Zhang, Bin Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781665475921

Sea fog recognition is a challenging and significant semantic segmentation task in remote sensing images. The fully supervised learning method relies on the pixel-level label, which is labor-intensive and time-consuming. Moreover, it is impossible to accurately annotate all pixels of the sea fog region due to the limited ability of the human eye to distinguish between low clouds and sea fog. In this paper, we propose a novel approach of point-based annotation for weakly supervised semantic segmentation with the auxiliary information of International Comprehensive Ocean-Atmosphere Data Set (ICOADS) visibility data. It only needs several definite points for both foreground and background, which significantly reduces the annotation cost of manpower. We conduct extensive experiments on Himawari-8 satellite remote sensing images to demonstrate the effectiveness of our annotation method. The mean intersection over union (mIoU) and overall recognition accuracy of our annotation method reach 82.72% and 95.18%, respectively. Compared with the fully supervised learning method, the accuracy and the recognition rate of sea fog area are improved with a maximum increase of 7.69% and 9.69%, respectively.

关键词： sea fog recognition weakly supervised learning point annotation semantic segmentation remote sensing image

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A NOVEL image TAG SALIENCY RANKING ALGORITHM BASED ON SPARSE REPRESENTATION

A NOVEL IMAGE TAG SALIENCY RANKING ALGORITHM BASED ON SPARSE...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Wang, Caixia Song, Zehai Feng, Songhe Lang, Congyan Yan, Shuicheng Beijing Jiaotong Univ Beijing Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781479902880

As the explosive growth of the web image data, image tag ranking used for image retrieval accurately from mass images is becoming an active research topic. However, the existing ranking approaches are not very ideal, which remains to be improved. This paper proposed a new image tag saliency ranking algorithm based on sparse representation. we firstly propagate labels from image-level to region-level via Multi-instance Learning driven by sparse representation, which means reconstructing the target instance from positive bag via the sparse linear combination of all the instances from training set, instances with nonzero reconstruction coefficients are considered to be similar to the target instance;then visual attention model is used for tag saliency analysis. Comparing with the existing approaches, the proposed method achieves a better effect and shows a better performance.

关键词： tag saliency ranking sparse representation multi-instance learning Diverse Density visual attention model

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Locally adaptive reconstruction of lost low frequency coefficients in wavelet coded images

Locally adaptive reconstruction of lost low frequency coeffi...

引用

conference on visual communications and image processing 2007

作者： Rombaut, Joost Pizurica, Aleksandra Philips, Wilfried Univ Ghent Dept TELIN St Pietersniuwstraat 41 B-9000 Ghent Belgium

ISBN: (纸本)9780819466211

In packet switched networks such as the Internet, packets may get lost during transmission due to, e.g., network congestion. This leads to a quality degradation of the original signal. As video communication is a bandwidth consuming application, the original data are first compressed. This compression step increases the impact of information loss even more. In wavelet based image and video coding, the low frequency data is the most important. Loss of low frequency coefficients results in annoying black holes in the received images and video. This effect can be countered by post processing error concealment: a lost coefficient is estimated from its neighboring coefficients. In this paper we present a locally adaptive interpolation method for the lost low frequency coefficients. For each lost low frequency coefficient, we estimate the optimal interpolation direction (horizontal or vertical) using novel error measures. In this way, we preserve the edges in the reconstructed image much better. Compared to older techniques of similar complexity, our scheme reconstructs images with the same or better quality. This is reflected in the visual as well as in the numerical results: there is an increase of up to 4.4 dB compared to bilinear concealment. The proposed scheme is fast and simple, which makes it suitable for real-time applications.

关键词： image reconstruction error concealment packet loss wavelet coding

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Super-resolution based on region-matching motion estimation

Super-resolution based on region-matching motion estimation

引用

conference on visual communications and image processing 2007

作者： Omer, Osama A. Tanaka, Toshihisa Tokyo Univ Agr & Technol Dept Elect & Elect Engn 2-24-16 Nakacho Koganei Tokyo 1848588 Japan

ISBN: (纸本)9780819466211

We consider the problem of recovering a high-resolution (HR) frame from a sequence of low-resolution (LR) frames. It is challenging to design a super-resolution (SR) algorithm for arbitrary video sequences. Video frames in general cannot be related through global parametric transformation due to the arbitrary individual pixel movement between frame pairs. Hence a local motion model needs to be used for frame alignment. An accurate alignment is the key to success of reconstruction-based super-resolution algorithms. Motivated by this challenge we propose to employ region-matching technique for image registration in this paper. The proposed algorithm consists of the alignment step to produce a blurred version of the HR frame and the restoration step to estimate the HR frame. The experimental results of the proposed algorithm are compared with the results of using affine, block matching, and optical flow motion models. It is shown that the use of region matching for SR is very promising in producing higher quality images.

关键词： super-resolution region matching image registration resolution enhancement

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A Unified Single image De-raining Model via Region Adaptive Coupled Network

A Unified Single Image De-raining Model via Region Adaptive ...

引用

IEEE International conference on visual communications and image processing (VCIP)

作者： Wu, Qingbo Chen, Li Ngan, King Ngi Li, Hongliang Meng, Fanman Xu, Linfeng Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

ISBN: (纸本)9781728180687

Single image de-raining is quite challenging due to the diversity of rain types and inhomogeneous distributions of rainwater. By means of dedicated models and constraints, existing methods perform well for specific rain type. However, their generalization capability is highly limited as well. In this paper, we propose a unified de-raining model by selectively fusing the clean background of the input rain image and the well restored regions occluded by various rains. This is achieved by our region adaptive coupled network (RACN), whose two branches integrate the features of each other in different layers to jointly generate the spatial-variant weight and restored image respectively. On the one hand, the weight branch could lead the restoration branch to focus on the regions with higher contributions for de-raining. On the other hand, the restoration branch could guide the weight branch to keep off the regions with over-/under-filtering risks. Extensive experiments show that our method outperforms many state-of-the-art de-raining algorithms on diverse rain types including the rain streak, raindrop and rain-mist.

关键词： Single image de-raining region adaptive coupled network weighted fusion model

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A new angle-based spatial modeling for query by visual thesaurus composition

A new angle-based spatial modeling for query by visual thesa...

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IEEE International conference on image processing (ICIP 2007)

作者： Houissa, Hichem Boujemaa, Nozha Inst Natl Rech Informat & Automat F-78153 Le Chesnay France

ISBN: (纸本)9781424414369

Querying by visual Thesaurus (VT) is a novel paradigm for content-based image retrieval approaches for it gives the user the possibility, in case of inappropriate starting example, to compose his query by arranging the visual patches of the starting "page zero" according to his mental image. A refinement of the willed results can be achieved by inducing a spatial description within the retrieval procedure. This paper presents a novel approach to model the spatial relations between the visual patches. We define the Weighted Angle Spatial Histogram (WASH) that combines the angular computation between pairs of regions of interest and their respective topological regularity/irregularity. WASH has shown great robustness to region shape and scale in the image because segmented regions are considered as a composition of elementary relevant and minor subregions. We tested our approach on generic database, and we compared it with other state-of-the-art techniques.

关键词： image region analysis spatial reasoning

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Does where you gaze on an image affect your perception of quality? Applying visual attention to image quality metric

Does where you gaze on an image affect your perception of qu...

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IEEE International conference on image processing (ICIP 2007)

作者： Ninassi, A. Le Meur, O. Le Callet, P. Barba, D. THOMSON R&D France 1 Ave Belle Fontaine F-35511 Cesson Sevigne France Univ Nantes IRCCyN Ecole Polytech CNRSUMR 6597 F-44306 Nantes France

ISBN: (纸本)9781424414369

The aim of an objective image quality assessment is to find an automatic algorithm that evaluates the quality of pictures or video as a human observer would do. To reach this goal, researchers try to simulate the Human visual System (HVS). visual attention is a main feature of the HVS, but few studies have been done on using it in image quality assessment. In this work, we investigate the use of the visual attention information in their final pooling step. The rationale of this choice is that an artefact is likely more annoying in a salient region than in other areas. To shed light on this point, a quality assessment campaign has been conducted during which eye movements have been recorded. The results show that applying the visual attention to image quality assessment is not trivial, even with the ground truth.

关键词： visual attention image quality assessment eye tracking error pooling

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