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Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs

Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Davide Caffagni Federico Cocchi Nicholas Moratelli Sara Sarto Marcella Cornia Lorenzo Baraldi Rita Cucchiara University of Modena and Reggio Emilia Italy University of Pisa Italy IIT-CNR Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal LLMs are the natural evolution of LLMs, and enlarge their capabilities so as to work beyond the pure textual modality. As research is being carried out to design novel architectures and vision-and-language adapters, in this paper we concentrate on endowing such models with the capability of answering questions that require external knowledge. Our approach, termed Wiki-LLaVA, aims at integrating an external knowledge source of multimodal documents, which is accessed through a hierarchical retrieval pipeline. Relevant passages, using this approach, are retrieved from the external knowledge source and employed as additional context for the LLM, augmenting the effectiveness and precision of generated dialogues. We conduct extensive experiments on datasets tailored for visual question answering with external data and demonstrate the appropriateness of our approach.

关键词： Visualization computer vision Adaptation models conferences Pipelines computer architecture Question answering (information retrieval)

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An analysis of best-practice strategies for replay and rehearsal in continual learning

An analysis of best-practice strategies for replay and rehea...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alexander Krawczyk Alexander Gepperth Applied Computer Science Department University of Applied Sciences Fulda Fulda Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study is in the context of class-incremental continual learning using replay, which has seen notable progress in recent years, fueled by concepts like conditional, latent or maximally interfered replay. However, there are many design choices to take when it comes to implementing replay strategies, with potentially very different outcomes in the various class-incremental scenarios. Some of the obvious design choices in replay are the use of experience replay (ER), the use of different generators like GANs –vs– VAEs for generative replay, or whether to re-initialize generators after each task. For replay strategies in general, it is an open question how many samples to generate for each new task, and what weights to give generated and new samples in the loss. On top of this, there are many possible CL evaluation protocols differing in the amount of tasks, the balancing of tasks or fundamental complexity (e.g., MNIST -vs- latent CIFAR/SVHN), and thus few generic conclusions about best practices for replay/rehearsal have found consensus in the literature. This study aims at establishing such best-practices by conducting an extensive set of representative replay experiments.

关键词： Continuing education computer vision Protocols conferences Generators pattern recognition Complexity theory

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Network Space Search for Pareto-Efficient Spaces

Network Space Search for Pareto-Efficient Spaces

引用

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

作者： Hong, Min-Fong Chen, Hao-Yun Chen, Min-Hung Xu, Yu-Syuan Kuo, Hsien-Kai Tsai, Yi-Min Chen, Hung-Jen Jou, Kevin MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9781665448994

Network spaces have been known as a critical factor in both handcrafted network designs or defining search spaces for Neural Architecture Search (NAS). However, an effective space involves tremendous prior knowledge and/or manual effort, and additional constraints are required to discover efficiency-aware architectures. In this paper, we define a new problem, Network Space Search (NSS), as searching for favorable network spaces instead of a single architecture. We propose an NSS method to directly search for efficient-aware network spaces automatically, reducing the manual effort and immense cost in discovering satisfactory ones. The resultant network spaces, named Elite Spaces, are discovered from Expanded Search Space with minimal human expertise imposed. The Pareto-efficient Elite Spaces are aligned with the Pareto front under various complexity constraints and can be further served as NAS search spaces, benefiting differentiable NAS approaches (e.g. In CIFAR-100, an averagely 2.3% lower error rate and 3.7% closer to target constraint than the baseline with around 90% fewer samples required to find satisfactory networks). Moreover, our NSS approach is capable of searching for superior spaces in future unexplored spaces, revealing great potential in searching for network spaces automatically.

关键词： computer vision Error analysis conferences Focusing Manuals computer architecture Search problems

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GRIB: Combining Global Reception and Inductive Bias For Human Segmentation and Matting

GRIB: Combining Global Reception and Inductive Bias For Huma...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yezhi Shen Weichen Xu Qian Lin Jan P. Allebach Fengqing Zhu Purdue University West Lafayette IN USA HP Inc Palo Alto CA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Human video segmentation and matting are challenging computer vision tasks, with many applications such as background replacement or background editing. Numerous methods have been proposed for human segmentation and matting in either portrait or first-person view videos. In this paper, we propose a real-time network that performs first-person view hand and manipulated object segmentation as well as second-person view human video matting. We introduce a global reception inductive bias block in the network’s encoder that aggregates the pixel features at short, medium, and long ranges. Furthermore, we propose a multi-target optimization method that fully leverages segmentation and matting labels to accelerate training. Our model outperforms existing real-time methods by achieving 93.9% mIoU on HP-Portrait, 95.1% mIoU on VideoMatte as well as 72.7% mIoU on EgoHOS datasets and achieves faster runtime.

关键词： Training computer vision Accuracy Runtime Optimization methods Object segmentation Streaming media

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EVA-GCN: Head Pose Estimation Based on Graph Convolutional Networks

EVA-GCN: Head Pose Estimation Based on Graph Convolutional N...

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

作者： Xin, Miao Mo, Shentong Lin, Yuanze Chinese Acad Sci Inst Automat CASIA Beijing Peoples R China Carnegie Mellon Univ Pittsburgh PA 15213 USA Beihang Univ Beijing Peoples R China

ISBN: (纸本)9781665448994

Head pose estimation is an important task in many real-world applications. Since the facial landmarks usually serve as the common input that is shared by multiple downstream tasks, utilizing landmarks to acquire high-precision head pose estimation is of practical value for many real-world applications. However, existing landmark-based methods have a major drawback in model expressive power, making them hard to achieve comparable performance to the landmark-free methods. In this paper, we propose a strong baseline method which views the head pose estimation as a graph regression problem. We construct a landmark-connection graph, and propose to leverage the Graph Convolutional Networks (GCN) to model the complex nonlinear mappings between the graph typologies and the head pose angles. Specifically, we design a novel GCN architecture which utilizes joint Edge-Vertex Attention (EVA) mechanism to overcome the unstable landmark detection. Moreover, we introduce the Adaptive Channel Attention (ACA) and the Densely-Connected Architecture (DCA) to boost the performance further. We evaluate the proposed method on three challenging benchmark datasets. Experiment results demonstrate that our method achieves better performance in comparison with the state-of-the-art landmark-based and landmark-free methods.

关键词： computer vision Adaptation models Image edge detection conferences Pose estimation computer architecture Benchmark testing

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Surgical Suturing Skill Assessment Using Estimated Hand Roll Angle from a Deep-learning computer vision Algorithm 46

Surgical Suturing Skill Assessment Using Estimated Hand Roll...

引用

46th Annual International conference of the ieee Engineering in Medicine and Biology society, EMBC 2024

作者： Gao, Jianxin Shayan, Amir Mehdi Singh, Simar P. Bible, Joe Singapogu, Ravikiran Groff, Richard E. Clemson University Department of Electrical and Computer Engineering ClemsonSC United States Clemson University Department of Bioengineering ClemsonSC United States Clemson University School of Mathematical and Statistical Sciences ClemsonSC United States

ISBN: (纸本)9798350371499

This paper proposes a deep-learning computer vision algorithm to estimate hand roll angles for metric-based assessment of surgical suturing skills. The number of rolls metric, previously calculated directly from IMU data, counts the number of hand roll reversals during a single suture. To calculate this metric using computer vision, we apply a deep-learning algorithm that can reliably estimate hand roll angles after training on suturing videos collected on the SutureCoach simulator. Results show that the estimation accuracy of the deep-learning algorithm is robust to different video backgrounds. The number of rolls metrics were used to analyze suturing performance in the SutureCoach dataset, which includes attending and resident surgeons and novices. The means of number of rolls based on computer vision differs between most skill levels at both surface and depth conditions, a pattern which holds for number of rolls based on the IMU as well. The proposed algorithm provides a solution for non-contact hand roll angle estimation, which opens up the possibility of inter-operative surgical skill assessment. The code is available at https://***/axin233/hand-roll-estimation © 2024 ieee.

关键词： Deep learning

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Image Compression with Recurrent Neural Network and Generalized Divisive Normalization

Image Compression with Recurrent Neural Network and Generali...

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

作者： Islam, Khawar Dang, L. Minh Lee, Sujin Moon, Hyeonjoon Sejong Univ Comp Vis & Pattern Recognit Lab Seoul South Korea Sejong Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (纸本)9781665448994

Image compression is a method to remove spatial redundancy between adjacent pixels and reconstruct a high-quality image. In the past few years, deep learning has gained huge attention from the research community and produced promising image reconstruction results. Therefore, recent methods focused on developing deeper and more complex networks, which significantly increased network complexity. In this paper, two effective novel blocks are developed: analysis and synthesis block that employs the convolution layer and Generalized Divisive Normalization (GDN) in the variablerate encoder and decoder side. Our network utilizes a pixel RNN approach for quantization. Furthermore, to improve the whole network, we encode a residual image using LSTM cells to reduce unnecessary information. Experimental results demonstrated that the proposed variable-rate framework with novel blocks outperforms existing methods and standard image codecs, such as George's [11] and JPEG in terms of image similarity. The project page along with code and models are available at https://***/khawar512/cvpr image compress

关键词： Image coding Recurrent neural networks Quantization (signal) Convolution Redundancy Transform coding Decoding

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Transformer-based Text Detection in the Wild

Transformer-based Text Detection in the Wild

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

作者： Raisi, Zobeir Naiel, Mohamed A. Younes, Georges Wardell, Steven Zelek, John S. Univ Waterloo Waterloo ON N2L 3G1 Canada ATS Automat Tooling Syst Inc Cambridge ON N3H 4R7 Canada

ISBN: (纸本)9781665448994

A major limitation to most state-of-the-art visual localization methods is their ineptitude to make use of ubiquitous signs and directions that are typically intuitive to humans. Localization methods can greatly benefit from a system capable of reasoning about a variety of cues beyond low-level features, such as street signs, store names, building directories, room numbers, etc. In this work, we tackle the problem of text detection in the wild, an essential step towards achieving text-based localization and mapping. While current state-of-the-art text detection methods employ ad-hoc solutions with complex multi-stage components to solve the problem, we propose a Transformer-based architecture inherently capable of dealing with multi-oriented texts in images. A central contribution to our work is the introduction of a loss function tailored to the rotated text detection problem that leverages a rotated version of a generalized intersection over union score to properly capture the rotated text regions. We evaluate our proposed model qualitatively and quantitatively on several challenging datasets namely, ICDAR15, ICDAR17, and MSRA-TD500, and show that it outperforms current state-of-the-art methods in text detection in the wild.

关键词： Location awareness Visualization computer vision conferences Buildings computer architecture Detectors

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Progressive Knowledge-Embedded Unified Perceptual Parsing for Scene Understanding

Progressive Knowledge-Embedded Unified Perceptual Parsing fo...

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

作者： Zheng, Wenbo Yan, Lan Wang, Fei-Yue Gou, Chao Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665448994

Human can naturally understand scenes in depth with the help of various knowledge accumulated and by a comprehensive visual concept organization including category labels and different-level attributes. This inspires us to unify professional knowledge at different levels with deep neural network architectures progressively for scene understanding. Different from the general embedding approaches, we construct different knowledge graphs for different levels of vision tasks by organizing the rich visual concepts accordingly. We employ a gated graph neural network and relational graph convolutional networks to propagate node messages for different levels of tasks and generate progressively different levels of knowledge representation through the graph. Compared with existing methods, our framework has a main appealing property leading to a novel progressive knowledge-embedded representation learning framework that incorporates different level knowledge graphs into the learning of networks at corresponding level. Extensive experiments on the widely used Broden+ dataset demonstrate the superiority of the proposed framework over other existing state-of-the-art methods.

关键词： Deep learning Visualization computer vision conferences Organizations Knowledge representation Logic gates

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The Myth of the Pyramid

The Myth of the Pyramid

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ramon Izquierdo-Cordova Walterio Mayol-Cuevas Department of Computer Science University of Bristol United Kingdom

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A deep-rooted strategy for building convolutional neural networks in computer vision is to increase the number of filters every time the feature map resolution is decreased. The notion ruling this pyramidal design is that the expressivity of the network increases with a higher number of filters to compensate for losses caused for lower resolutions. This paper challenges the practice by testing a set of variate distribution of filters, named filter templates, on popular CNN architectures (VGG, ResNet, MobileNet and MnasNet). The experimental results show that the superiority of the pyramidal design holds on the ImageNet dataset but fails for other datasets such as MNIST, CIFAR and TinyImageNet, and for other tasks such as audio classification. CNN models with different filter distributions deliver higher accuracy with reduced resource consumption suggesting the pyramidal design has been optimised to Imagenet and that each model-dataset pair benefits from tuning the number and distribution of filters. To further illustrate the benefits of exploring other distributions, this paper shows that the best performing model from the NASBench101 dataset can increase its accuracy over the original pyramidal design with reductions of parameters up to 68 per cent by using templates. Overall, our experiments point to new opportunities for model designers to find more efficient models.

关键词： computer vision Filters Accuracy Image resolution Computational modeling conferences pattern recognition

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