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A robust non-blind deblurring method using deep denoiser prior

A robust non-blind deblurring method using deep denoiser pri...

引用

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

作者： Fang, Yingying Zhang, Hao Wong, Hok Shing Zeng, Tieyong Imperial Coll London London England Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The existing non-blind deblurring methods are mostly susceptible to noise in the given blurring kernel, which is usually estimated from the observed image. This will produce undesirable ringing artifacts around the recovered edges when the given kernel is not accurate enough. Besides, the noise and outliers in the observed images may also severely degrade the performance of the deblurring methods. Considering these factors, we designed a robust non-blind deblurring method taking all these noises into account. In this paper, we propose a kernel error term to rectify the given kernel in the midst of the deconvolution process. A residual error term is also introduced to deal with the outliers caused by noise or saturation. A deep learning denoiser prior is adopted to reserve the fine textures in the recovered image. The experiments show clearly that the proposed method achieves remarkable progress in both the visual quality and the numerical results of the recovered images compared to the state-of-the-art deblurring methods.

关键词： Deep learning Measurement Visualization Deconvolution Image edge detection conferences pattern recognition

来源：评论

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Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding

Visual Programming for Zero-shot Open-Vocabulary 3D Visual G...

引用

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

作者： Yuan, Zhihao Ren, Jinke Feng, Chun-Mei Zhao, Hengshuang Cui, Shuguang Li, Zhen CUHKSZ FNii Shenzhen Peoples R China CUHKSZ SSE Shenzhen Peoples R China ASTAR IHPC Singapore Singapore HKU Hong Kong Peoples R China

ISBN: (纸本)9798350353006

3D Visual Grounding (3DVG) aims at localizing 3D object based on textual descriptions. Conventional supervised methods for 3DVG often necessitate extensive annotations and a predefined vocabulary, which can be restrictive. To address this issue, we propose a novel visual programming approach for zero-shot open-vocabulary 3DVG, leveraging the capabilities of large language models (LLMs). Our approach begins with a unique dialog-based method, engaging with LLMs to establish a foundational understanding of zero-shot 3DVG. Building on this, we design a visual program that consists of three types of modules, i.e., view-independent, view-dependent, and functional modules. These modules, specifically tailored for 3D scenarios, work collaboratively to perform complex reasoning and inference. Furthermore, we develop an innovative language-object correlation module to extend the scope of existing 3D object detectors into open-vocabulary scenarios. Extensive experiments demonstrate that our zero-shot approach can outperform some supervised baselines, marking a significant stride towards effective 3DVG. Code is available at https://***/ZSVG3D.

关键词： Point Cloud vision and Language Visual Grounding

来源：评论

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Connecting vision and Language with Video Localized Narratives

Connecting Vision and Language with Video Localized Narrativ...

引用

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

作者： Voigtlaender, Paul Changpinyo, Soravit Pont-Tuset, Jordi Soricut, Radu Ferrari, Vittorio Google Res Mountain View CA 94043 USA

ISBN: (纸本)9798350301298

We propose Video Localized Narratives, a new form of multimodal video annotations connecting vision and language. In the original Localized Narratives [36], annotators speak and move their mouse simultaneously on an image, thus grounding each word with a mouse trace segment. However, this is challenging on a video. Our new protocol empowers annotators to tell the story of a video with Localized Narratives, capturing even complex events involving multiple actors interacting with each other and with several passive objects. We annotated 20k videos of the OVIS, UVO, and Oops datasets, totalling 1.7M words. Based on this data, we also construct new benchmarks for the video narrative grounding and video question answering tasks, and provide reference results from strong baseline models. Our annotations are available at https://***/video-localized-narratives/.

关键词： Datasets and evaluation

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Scaling Language-Image Pre-training via Masking

Scaling Language-Image Pre-training via Masking

引用

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

作者： Li, Yanghao Fan, Haoqi Hu, Ronghang Feichtenhofert, Christoph He, Kaiming Meta AI FAIR New York NY 10023 USA

ISBN: (纸本)9798350301298

We present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP [52]. Our method randomly masks out and removes a large portion of image patches during training. Masking allows us to learn from more image-text pairs given the same wall-clock time and contrast more samples per iteration with similar memory footprint. It leads to a favorable trade-off between accuracy and training time. In our experiments on 400 million image-text pairs, FLIP improves both accuracy and speed over the no-masking baseline. On a large diversity of downstream tasks, FLIP dominantly outperforms the CLIP counterparts trained on the same data. Facilitated by the speedup, we explore the scaling behavior of increasing the model size, data size, or training length, and report encouraging results and comparisons. We hope that our work will foster future research on scaling vision-language learning.

关键词： and reasoning language vision

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Update Compression for Deep Neural Networks on the Edge

Update Compression for Deep Neural Networks on the Edge

引用

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

作者： Chen, Bo Bakhshi, Ali Batista, Gustavo Ng, Brian Chin, Tat-Jun Univ Adelaide Adelaide SA Australia Univ New South Wales Sydney NSW Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

An increasing number of artificial intelligence (AI) applications involve the execution of deep neural networks (DNNs) on edge devices. Many practical reasons motivate the need to update the DNN model on the edge device post-deployment, such as refining the model, concept drift, or outright change in the learning task. In this paper, we consider the scenario where retraining can be done on the server side based on a copy of the DNN model, with only the necessary data transmitted to the edge to update the deployed model. However, due to bandwidth constraints, we want to minimise the transmission required to achieve the update. We develop a simple approach based on matrix factorisation to compress the model update-this differs from compressing the model itself. The key idea is to preserve existing knowledge in the current model and optimise only small additional parameters for the update which can be used to reconstitute the model on the edge. We compared our method to similar techniques used in federated learning;our method usually requires less than half of the update size of existing methods to achieve the same accuracy.

关键词： Deep learning Neural networks Refining Redundancy Bandwidth Data models pattern recognition

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LASO: Language-guided Affordance Segmentation on 3D Object

LASO: Language-guided Affordance Segmentation on 3D Object

引用

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

作者： Li, Yicong Zhao, Na Xiao, Junbin Feng, Chun Wang, Xiang Chua, Tat-seng Natl Univ Singapore Singapore Singapore Singapore Univ Technol & Design Singapore Singapore Univ Sci & Technol China Hefei Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Inst Dataspace Hefei Anhui Peoples R China

ISBN: (纸本)9798350353006

Segmenting affordance in 3D data is key for bridging perception and action in robots. Existing efforts mostly focus on the visual side and overlook the affordance knowledge from a semantic aspect. This oversight not only limits their generalization to unseen objects, but more importantly, hinders their synergy with large language models (LLMs) which are excellent task planners that can decompose an overarching command into agent-actionable instructions. With this regard, we propose a novel task, Language-guided Affordance Segmentation on 3D Object (LASO), which challenges a model to segment a 3D object's part relevant to a given affordance question. To facilitate the task, we contribute a dataset comprising 19,751 point-question pairs, covering 8434 object shapes and 870 expert-crafted questions. As a pioneer solution, we further propose PointRefer, which highlights an adaptive fusion module to identify target affordance regions at different scales. To ensure a text-aware segmentation, we adopt a set of affordance queries conditioned on linguistic cues to generate dynamic kernels. These kernels are further used to convolute with point features and generate a segmentation mask. Comprehensive experiments and analyses validate PointRefer's effectiveness. With these efforts, We hope that LASO can steer the direction of 3D affordance, guiding it towards enhanced integration with the evolving capabilities of LLMs. Code and data are available at https://***/yl3800/LASO.

关键词： 3D-Language Multimodal vision-Language

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TDT: Teaching Detectors to Track without Fully Annotated Videos

TDT: Teaching Detectors to Track without Fully Annotated Vid...

引用

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

作者： Yu, Shuzhi Wu, Guanhang Gu, Chunhui Fathy, Mohammed E. Duke Univ Durham NC 27706 USA Google LLC Mountain View CA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recently, one-stage trackers that use a joint model to predict both detections and appearance embeddings in one forward pass received much attention and achieved state-of-the-art results on the Multi-Object Tracking (MOT) benchmarks. However, their success depends on the availability of videos that are fully annotated with tracking data, which is expensive and hard to obtain. This can limit the model generalization. In comparison, the two-stage approach, which performs detection and embedding separately, is slower but easier to train as their data are easier to annotate. We propose to combine the best of the two worlds through a data distillation approach. Specifically, we use a teacher embedder, trained on Re-ID datasets, to generate pseudo appearance embedding labels for the detection datasets. Then, we use the augmented dataset to train a detector that is also capable of regressing these pseudo-embeddings in a fully-convolutional fashion. Our proposed one-stage solution matches the two-stage counterpart in quality but is 3 times faster. Even though the teacher embedder has not seen any tracking data during training, our proposed tracker achieves competitive performance with some popular trackers (e.g. JDE) trained with fully labeled tracking data.

关键词： Training computer vision Annotations conferences Detectors Predictive models Benchmark testing

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Nonuniformly Dehaze Network for Visible Remote Sensing Images

Nonuniformly Dehaze Network for Visible Remote Sensing Image...

引用

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

作者： Chen, Zhaojie Li, Qi Feng, Huajun Xu, Zhihai Chen, Yueting Zhejiang Univ Coll Opt Sci & Engn Hangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Nonuniform haze on remote sensing images degrades image quality and hinders many high-level tasks. In this paper, we propose a Nonuniformly Dehaze Network towards nonuniform haze on visible remote sensing images. To extract robust haze-aware features, we propose Nonuniformly Excite (NE) module. Inspired by the well-known gather-excite attention module, NE module works in a map-excite manner. In the map operation, we utilize a proposed Dual Attention Dehaze block to extract local enhanced features. In the gather operation, we utilize a strided deformable convolution to nonuniformly process features and extract nonlocal haze-aware features. In the excite operation, we employ a pixel-wise attention between local enhanced features and nonlocal haze-aware features, to gain finer haze-aware features. Moreover, we recursively embed NE modules in a multi-scale framework. It helps not only significantly reduce network's parameters, but also recursively deliver and fuse haze-aware features from higher levels, which makes learning more efficient. Experiments demonstrate that the proposed network performs favorably against the state-of-the-art methods on both synthetic and real-world images.

关键词： Image quality computer vision Fuses Convolution conferences Feature extraction pattern recognition

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Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity

Revisiting Vicinal Risk Minimization for Partially Supervise...

引用

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

作者： Dong, Nanqing Wang, Jiayi Voiculescu, Irina Univ Oxford Dept Comp Sci Oxford England Univ Oxford Math Inst Oxford England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical dataset that is fully labeled for all classes of interest. Instead, it would be convenient to collect multiple small partially labeled datasets from different matching sources, where the medical images may have only been annotated for a subset of classes of interest. This paper offers an empirical understanding of an under-explored problem, namely partially supervised multi-label classification (PSMLC), where a multi-label classifier is trained with only partially labeled medical images. In contrast to the fully supervised counterpart, the partial supervision caused by medical data scarcity has non-trivial negative impacts on the model performance. A potential remedy could be augmenting the partial labels. Though vicinal risk minimization (VRM) has been a promising solution to improve the generalization ability of the model, its application to PSMLC remains an open question. To bridge the methodological gap, we provide the first VRM-based solution to PSMLC. The empirical results also provide insights into future research directions on partially supervised learning under data scarcity.

关键词： Bridges computer vision Costs conferences Supervised learning Entropy Data models

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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semanti...

引用

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

作者： He, Weizhao Zhang, Yang Zhuo, Wei Shen, Linlin Yang, Jiaqi Deng, Songhe Sun, Liang Shenzhen Univ Comp Vision Inst Sch Comp Sci Software Engn Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Univ Nottingham Sch Comp Sci Nottingham England

ISBN: (纸本)9798350353006

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the Segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support port prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively.

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