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MetaFusion: Infrared and Visible Image Fusion via Meta-Feature Embedding from Object Detection

MetaFusion: Infrared and Visible Image Fusion via Meta-Featu...

引用

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

作者： Zhao, Wenda Xie, Shigeng Zhao, Fan He, You Lu, Huchuan Dalian Univ Technol Dalian Peoples R China Liaoning Normal Univ Dalian Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Fusing infrared and visible images can provide more texture details for subsequent object detection task. Conversely, detection task furnishes object semantic information to improve the infrared and visible image fusion. Thus, a joint fusion and detection learning to use their mutual promotion is attracting more attention. However, the feature gap between these two different-level tasks hinders the progress. Addressing this issue, this paper proposes an infrared and visible image fusion via meta-feature embedding from object detection. The core idea is that meta-feature embedding model is designed to generate object semantic features according to fusion network ability, and thus the semantic features are naturally compatible with fusion features. It is optimized by simulating a meta learning. Moreover, we further implement a mutual promotion learning between fusion and detection tasks to improve their performances. Comprehensive experiments on three public datasets demonstrate the effectiveness of our method. Code and model are available at: https://***/wdzhao123/MetaFusion.

关键词： Low-level vision

来源：评论

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REVEAL: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory

REVEAL: Retrieval-Augmented Visual-Language Pre-Training wit...

引用

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

作者： Hu, Ziniu Iscen, Ahmet Sun, Chen Wang, Zirui Chang, Kai-Wei Sun, Yizhou Schmid, Cordelia Ross, David A. Fathi, Alireza Univ Calif Los Angeles Los Angeles CA 90024 USA Google Res Mountain View CA USA

ISBN: (纸本)9798350301298

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model (REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve from it to answer knowledge-intensive queries. REVEAL consists of four key components: the memory, the encoder, the retriever and the generator. The large-scale memory encodes various sources of multimodal world knowledge (e.g. image-text pairs, question answering pairs, knowledge graph triplets, etc.) via a unified encoder. The retriever finds the most relevant knowledge entries in the memory, and the generator fuses the retrieved knowledge with the input query to produce the output. A key novelty in our approach is that the memory, encoder, retriever and generator are all pre-trained end-to-end on a massive amount of data. Furthermore, our approach can use a diverse set of multimodal knowledge sources, which is shown to result in significant gains. We show that REVEAL achieves state-of-the-art results on visual question answering and image captioning. The project page of this work is ***.

关键词： and reasoning language vision

来源：评论

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Deep Random Projector: Accelerated Deep Image Prior

Deep Random Projector: Accelerated Deep Image Prior

引用

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

作者： Li, Taihui Wang, Hengkang Zhuang, Zhong Sun, Ju Univ Minnesota Comp Sci & Engn Minneapolis MN 55455 USA Univ Minnesota Elect & Comp Engn Minneapolis MN USA

ISBN: (纸本)9798350301298

Deep image prior (DIP) has shown great promise in tackling a variety of image restoration (IR) and general visual inverse problems, needing no training data. However, the resulting optimization process is often very slow, inevitably hindering DIP's practical usage for time-sensitive scenarios. In this paper, we focus on IR, and propose two crucial modifications to DIP that help achieve substantial speedup: 1) optimizing the DIP seed while freezing randomly-initialized network weights, and 2) reducing the network depth. In addition, we reintroduce explicit priors, such as sparse gradient prior-encoded by total-variation regularization, to preserve the DIP peak performance. We evaluate the proposed method on three IR tasks, including image denoising, image super-resolution, and image inpainting, against the original DIP and variants, as well as the competing metaDIP that uses meta-learning to learn good initializers with extra data. Our method is a clear winner in obtaining competitive restoration quality in a minimal amount of time. Our code is available at https://***/sun-umn/Deep-Random-Projector.

关键词： vision applications and systems

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Hierarchical Prompt Learning for Multi-Task Learning

Hierarchical Prompt Learning for Multi-Task Learning

引用

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

作者： Liu, Yajing Lu, Yuning Liu, Hao An, Yaozu Xu, Zhuoran Yao, Zhuokun Zhang, Baofeng Xiong, Zhiwei Gui, Chenguang JD Logist Beijing Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

vision-language models (VLMs) can effectively transfer to various vision tasks via prompt learning. Real-world scenarios often require adapting a model to multiple similar yet distinct tasks. Existing methods focus on learning a specific prompt for each task, limiting the ability to exploit potentially shared information from other tasks. Naively training a task-shared prompt using a combination of all tasks ignores fine-grained task correlations. Significant discrepancies across tasks could cause negative transferring. Considering this, we present Hierarchical Prompt (HiPro) learning, a simple and effective method for jointly adapting a pre-trained VLM to multiple downstream tasks. Our method quantifies inter-task affinity and subsequently constructs a hierarchical task tree. Task-shared prompts learned by internal nodes explore the information within the corresponding task group, while task-individual prompts learned by leaf nodes obtain fine-grained information targeted at each task. The combination of hierarchical prompts provides high-quality content of different granularity. We evaluate HiPro on four multi-task learning datasets. The results demonstrate the effectiveness of our method.

关键词： language reasoning vision

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Adaptive Spot-Guided Transformer for Consistent Local Feature Matching

Adaptive Spot-Guided Transformer for Consistent Local Featur...

引用

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

作者： Yu, Jiahuan Chang, Jiahao He, Jianfeng Zhang, Tianzhu Yu, Jiyang Wu, Feng Univ Sci & Technol China Hefei Anhui Peoples R China Deep Space Explorat Lab Nanjing Jiangsu Peoples R China China Acad Space Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency. Meanwhile, most methods struggle with large scale variations. To deal with the above issues, we propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching, which jointly models the local consistency and scale variations in a unified coarse-to-fine architecture. The proposed ASTR enjoys several merits. First, we design a spot-guided aggregation module to avoid interfering with irrelevant areas during feature aggregation. Second, we design an adaptive scaling module to adjust the size of grids according to the calculated depth information at fine stage. Extensive experimental results on five standard benchmarks demonstrate that our ASTR performs favorably against state-of-the-art methods. Our code will be released on https://***.

关键词： Low-level vision

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Zero-Shot Dual-Lens Super-Resolution

Zero-Shot Dual-Lens Super-Resolution

引用

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

作者： Xu, Ruikang Yao, Mingde Xiong, Zhiwei Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

The asymmetric dual-lens configuration is commonly available on mobile devices nowadays, which naturally stores a pair of wide-angle and telephoto images of the same scene to support realistic super-resolution (SR). Even on the same device, however, the degradation for modeling realistic SR is image-specific due to the unknown acquisition process (e.g., tiny camera motion). In this paper, we propose a zero-shot solution for dual-lens SR (ZeDuSR), where only the dual-lens pair at test time is used to learn an image-specific SR model. As such, ZeDuSR adapts itself to the current scene without using external training data, and thus gets rid of generalization difficulty. However, there are two major challenges to achieving this goal: 1) dual-lens alignment while keeping the realistic degradation, and 2) effective usage of highly limited training data. To overcome these two challenges, we propose a degradation-invariant alignment method and a degradation-aware training strategy to fully exploit the information within a single dual-lens pair. Extensive experiments validate the superiority of ZeDuSR over existing solutions on both synthesized and realworld dual-lens datasets. The implementation code is available at https://***/XrKang/ZeDuSR.

关键词： Low-level vision

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Denoising diffusion models for out-of-distribution detection

Denoising diffusion models for out-of-distribution detection

引用

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

作者： Graham, Mark S. Pinaya, Walter H. L. Tudosiu, Petru-Daniel Nachev, Parashkev Ourselin, Sebastien Cardoso, M. Jorge King's College London United Kingdom University College London United Kingdom

ISBN: (纸本)9798350302493

Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or other measurements from a generative model. Reconstruction-based methods offer an alternative approach, in which a measure of reconstruction error is used to determine if a sample is out-of-distribution. However, reconstruction-based approaches are less favoured, as they require careful tuning of the model's information bottleneck-such as the size of the latent dimension - to produce good results. In this work, we exploit the view of denoising diffusion probabilistic models (DDPM) as denoising autoencoders where the bottleneck is controlled externally, by means of the amount of noise applied. We propose to use DDPMs to reconstruct an input that has been noised to a range of noise levels, and use the resulting multi-dimensional reconstruction error to classify out-of-distribution inputs. We validate our approach both on standard computer-vision datasets and on higher dimension medical datasets. Our approach outperforms not only reconstruction-based methods, but also state-of-the-art generative-based approaches. Code is available at https://***/marksgraham/ddpm-ood. © 2023 ieee.

关键词： computer vision

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DoNet: Deep De-overlapping Network for Cytology Instance Segmentation

DoNet: Deep De-overlapping Network for Cytology Instance Seg...

引用

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

作者： Jiang, Hao Zhang, Rushan Zhou, Yanning Wang, Yumeng Chen, Hao Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tencent AI Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350301298

Cell instance segmentation in cytology images has significant importance for biology analysis and cancer screening, while remains challenging due to 1) the extensive overlapping translucent cell clusters that cause the ambiguous boundaries, and 2) the confusion of mimics and debris as nuclei. In this work, we proposed a De-overlapping Network (DoNet) in a decompose-and-recombined strategy. A Dual-path Region Segmentation Module (DRM) explicitly decomposes the cell clusters into intersection and complement regions, followed by a Semantic Consistency-guided Recombination Module (CRM) for integration. To further introduce the containment relationship of the nucleus in the cytoplasm, we design a Mask-guided Region Proposal Strategy (MRP) that integrates the cell attention maps for inner-cell instance prediction. We validate the proposed approach on ISBI2014 and CPS datasets. Experiments show that our proposed DoNet significantly outperforms other state-of-the-art (SOTA) cell instance segmentation methods. The code is available at https://***/DeepDoNet/DoNet.

关键词： cell microscopy Medical and biological vision

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Unpaired Face Restoration via Learnable Cross-Quality Shift

Unpaired Face Restoration via Learnable Cross-Quality Shift

引用

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

作者： Dong, Yangyi Zhang, Xiaoyun Wang, Zhixin Zhang, Ya Chen, Siheng Wang, Yanfeng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Face restoration aims to recover high-quality (HQ) face images from low-quality (LQ) ones with various unknown degradations. Unpaired face restoration approaches focus on the adaptation to unseen degradations, which is a more challenging setting. Recently, generative facial priors of StyleGAN are used to improve the restoration capability of paired face restoration methods. For unpaired methods, however, using face priors is a challenge due to the lack of paired supervision. To address this issue, we take advantage of the editing capabilities of StyleGAN's latent code and propose a novel learnable cross-quality shift. The proposed learnable cross-quality shift not only introduces the generative facial priors into the unpaired framework, but also enables the straight-forward addition/subtraction in the latent feature space to achieve quality conversion. Furthermore, we design a two-branch framework with the proposed cross-quality shift to deal with unpaired data and improve the fidelity of restoration. With the unpaired framework, our method can be fine-tuned on images with unseen degradation. Experimental results show that (i) compared to state-of-the-art methods, our method improves performances under moderate and severe degradation situations;and (ii) both the proposed learnable cross-quality shift and the two-branch framework benefit the restoration performance.

关键词： Degradation computer vision Codes Face recognition conferences Image restoration Faces

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Generative Bias for Robust Visual Question Answering

Generative Bias for Robust Visual Question Answering

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

作者： Cho, Jae Won Kim, Dong-Jin Ryu, Hyeonggon Kweon, In So Korea Adv Inst Sci & Technol Daejeon South Korea Hanyang Univ Seoul South Korea

ISBN: (纸本)9798350301298

The task of Visual Question Answering (VQA) is known to be plagued by the issue of VQA models exploiting biases within the dataset to make its final prediction. Various previous ensemble based debiasing methods have been proposed where an additional model is purposefully trained to be biased in order to train a robust target model. However, these methods compute the bias for a model simply from the label statistics of the training data or from single modal branches. In this work, in order to better learn the bias a target VQA model suffers from, we propose a generative method to train the bias model directly from the target model, called GenB. In particular, GenB employs a generative network to learn the bias in the target model through a combination of the adversarial objective and knowledge distillation. We then debias our target model with GenB as a bias model, and show through extensive experiments the effects of our method on various VQA bias datasets including VQA-CP2, VQA-CP1, GQA-OOD, and VQA-CE, and show state-of-the-art results with the LXMERT architecture on VQA-CP2.

关键词： language reasoning vision

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