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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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MSCFormer: Multi-Scale Circular Transformer for image Deblurring

MSCFormer: Multi-Scale Circular Transformer for Image Deblur...

引用

2024 conference on visual communications and image processing

作者： Wang, Shuai Wang, Han Liu, Renhe Wu, Zhipeng Wei, Bo Liu, Yu Tianjin Univ Sch Microelect Tianjin Peoples R China Univ Tokyo Sch Engn Tokyo Japan

ISBN: (纸本)9798331529543;9798331529550

Currently, with the extensive application of digital cameras in dynamic capturing, implications such as camera jitter, out-of-focus, and target motion induce various types and degrees of image blurring. Deep learning (DL) is a powerful technique that offers data-adaptive recovery without prior characterization of deblurring filter kernels. However, end-to-end networks can still be improved to restore regions with severe localized blurring. Therefore, we propose a multi-scale circular transformer (MSC-Former) employing averaged neighborhood attention (AvgNA) to solve this problem. It computes the local attention of each feature pixel by learning the correlation between the center and the surrounding windowed neighborhood, then produces integrated attention with direct averaging. We employ a multiscale circular strategy (MSCS) to compute attention at different spatial scales to expand the receptive field while maintaining a low parameter count. It uses concentric circular regions with varying radii to define neighborhoods at different scales, which expands the receptive field during attention computation while capturing spatial continuity across larger neighborhoods. Experimental results demonstrate that the proposed method surpasses the recent state-of-the-art deblurring techniques on the benchmark dataset.

关键词： image deblurring averaged neighborhood attention multi-scale circular strategy transformer

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ReLI-QA: A Multidimensional Quality Assessment Dataset for Relighted Human Heads

ReLI-QA: A Multidimensional Quality Assessment Dataset for R...

引用

2024 conference on visual communications and image processing

作者： Zhou, Yingjie Zhang, Zicheng Wen, Farong Jia, Jun Min, Xiongkuo Wang, Jia Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China PengCheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Lighting conditions significantly affect the quality of both real and AI-generated images. Facial images are particularly sensitive to lighting due to their detailed nature and the importance of facial features in conveying identity. Poor lighting can easily obscure these critical details. To address this issue, various portrait relighting methods have been developed to adjust the lighting in improperly exposed images. However, these methods often encounter challenges such as overexposure, underexposure, and detail loss in the relighted portraits. Consequently, there is a need for effective quality assessment and control of relighted human heads (RHHs). In this study, one proposed simple baseline and three typical relighting methods are applied to six selected human head (HH) images, resulting in the creation of a quality assessment dataset named ReLI-QA, which comprises 840 RHHs. A multidimensional subjective quality assessment method based on visual guidance is proposed to accurately evaluate the visual quality of each RHH in the dataset. By analyzing the results of subjective experiments, the quality of RHHs is shown to be affected by multiple factors. Finally, based on ReLI-QA, some typical image quality assessment (IQA) methods are selected for benchmark experiments. The experimental results show the limitations of the existing methods in RHH quality assessment. The dataset and code for this research has been released at https://***/zyj-2000/ReLI-QA.

关键词： image Quality Assessment Subjective Quality Assessment Relighted Human Heads AI Generated Content

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ACIQA: A Dataset and Method for Assessing the Imaging Quality of Automotive Cameras

ACIQA: A Dataset and Method for Assessing the Imaging Qualit...

引用

2024 conference on visual communications and image processing

作者： Huang, Yijie Ni, Haoyang Lu, Fangfang Zhang, Kaiwei Jia, Ziheng Min, Xiongkuo Zhai, Guangtao Shanghai Univ Elect Power Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9798331529543;9798331529550

The imaging quality of automotive cameras is crucial in complex driving environments. Therefore, it is essential to conduct subjective experiments that can realistically reflect drivers' evaluation of the imaging quality of automotive cameras in real traffic scenarios. To accurately assess the imaging quality of automotive cameras, this paper proposes a no-reference quality assessment method with quality scores that are highly consistent with human subjective perception. Initially, this study constructs a new image quality assessment dataset and then obtains the subjective scores of image quality through subjective experiments. The dataset is constructed by using a variety of realistic props to simulate scene elements that might be captured by an automotive camera and are captured using a wide range of cameras with different sensor types, lens focus, and viewing angles, resulting in a dataset of diverse images. The objective quality assessment method proposed in this paper consists of an object detection network and a multi-branch quality evaluation network. The object detection network is responsible for identifying and classifying scene elements, while the multi-branch quality evaluation network performs feature extraction and score regression on various types of elements to effectively evaluate the imaging quality of the automotive cameras. In the experiments, this no-reference quality assessment method is tested on our built dataset, and the results show that the proposed method exhibits the best performance compared with the state-of-the-art image quality assessment methods.

关键词： image quality assessment automotive camera image dataset multi-branch quality evaluation network

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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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A NOVEL image TAG SALIENCY RANKING ALGORITHM BASED ON SPARSE REPRESENTATION

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

引用

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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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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Wearable visually Assistive Device for Blind People to Appreciate Real-world Scene and Screen image

Wearable Visually Assistive Device for Blind People to Appre...

引用

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

作者： Ai, Jin Hu, Menghan Li, Guodong Zhai, Guangtao Zhang, Jian Li, Qingli Sun, Wendell Q. Univ Shanghai Sci & Technol Sch Med Instrument & Food Engn Shanghai Peoples R China East China Normal Univ Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Minist Educ Key Lab Articial Intelligence Beijing Peoples R China

ISBN: (纸本)9781728180687

Due to the loss of vision, the appreciation of the real-world scene and the images displayed on the screen becomes almost impossible for blind people. In an effort to meet the needs of the blind community, we develop a wearable visually assistive device to help them perceive images. With the help of various multimedia information processing technologies, the proposed device can first acquire image information through a depth camera, then implement an image-to-text transformation using image caption technology, and finally the obtained text sequence is fed back to the user via voice. In this way, blind people are able to perceive the outside world, thus creating an unprecedented experience for them. The main technical specifications of the system are: distance perception range is 0.1m to 10m;RGB field of view is 69.4 degrees x42.5 degrees x77 degrees;depth field of view is 91.2 degrees x65.5 degrees x 100.6 degrees;maximum weight is 3.05kg. Two demo videos of the proposed navigation system which are respectively recorded for real- world scene and screen image are available at: https://***/10.6084/***.12520499. v1

关键词： blindness assistive system vision disorder

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Bit Distribution Study and Implementation of Spatial Quality Map in the JPEG-AI Standardization

Bit Distribution Study and Implementation of Spatial Quality...

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2024 conference on visual communications and image processing

作者： Jia, Panqi Mao, Jue Koyuncu, Esin Koyuncu, A. Burakhan Solovyev, Timofey Karabutov, Alexander Zhao, Yin Alshina, Elena Kaup, Andre Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Nurnberg Germany Tech Univ Munich Chair Media Technol Munich Germany Huawei Technol Shenzhen Peoples R China

ISBN: (纸本)9798331529543;9798331529550

Currently, there is a high demand for neural network-based image compression codecs. These codecs employ non-linear transforms to create compact bit representations and facilitate faster coding speeds on devices compared to the hand-crafted transforms used in classical frameworks. The scientific and industrial communities are highly interested in these properties, leading to the standardization effort of JPEG-AI. The JPEG-AI verification model has been released and is currently under development for standardization. Utilizing neural networks, it can outperform the classic codec VVC intra by over 10% BD-rate operating at base operation point. Researchers attribute this success to the flexible bit distribution in the spatial domain, in contrast to VVC intra's anchor that is generated with a constant quality point. However, our study reveals that VVC intra displays a more adaptable bit distribution structure through the implementation of various block sizes. As a result of our observations, we have proposed a spatial bit allocation method to optimize the JPEG-AI verification model's bit distribution and enhance the visual quality. Furthermore, by applying the VVC bit distribution strategy, the objective performance of JPEG-AI verification mode can be further improved, resulting in a maximum gain of 0.45 dB in PSNR-Y.

关键词： Learned image Compression JPEG-AI bit rate matching algorithm optimization

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Comparison of the State-of-the-Art image and Video Quality Assessment Metrics 29

Comparison of the State-of-the-Art Image and Video Quality A...

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29th IEEE conference on Signal processing and communications Applications (SIU)

作者： Akkaya, Emre Ozbek, Nukhet Ege Univ Muhendislik Fak Elekt Elekt Muhendisligi Bolumu Izmir Turkey

ISBN: (纸本)9781665436496

Today, thanks to the developing technology, changes are experienced in many areas and these changes sometimes bring some problems, issues that need to be solved or improved. In the field of digital image and video processing, the viewer experience has been greatly improved with the development of image and video coding methods and technologies in recent years. However, due to these developments, the importance of video and image quality evaluation techniques has increased. In the field of imaging technology, although the human visual system and human judgment experiments will give us the most accurate results, this rapidly growing field has made human judgment experiments unsuitable with the increasing content. At this point, effective and automatic quality assessment metrics are needed to evaluate and optimize advanced compression technologies in terms of visual perception quality. In this paper, our aim is to compare the performances of some of the new generation image and video quality assessment metrics.

关键词： human visual system traditional image&video quality metrics the state-of-the-art image and video quality metrics

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