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Semi-Stereo: A Universal Stereo Matching Framework for Imperfect Data via Semi-supervised Learning

Semi-Stereo: A Universal Stereo Matching Framework for Imper...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xin Yue Zongqing Lu Xiangru Lin Wenjia Ren Zhijing Shao Haonan Hu Yu Zhang Qingmin Liao Tsinghua University Prometheus Vision Technology Co. Ltd.

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Data matters in deep-learning-based binocular stereo matching. Obtaining a perfect dataset for stereo matching is hard and thus imperfect data is common in existing benchmark datasets, such as KITTI, ETH3D and Middlebury. The imperfectness typically has two forms: sparse-labeled data or even unlabeled data. Current stereo matching networks ignore the supervision from these imperfect data itself, even the semi-supervised networks often suffer from confirmation bias in the predictions. Besides, current methods lack a unified solution to utilize the supervision signal from those imperfect data. To mitigate this research gap, we propose Semi-Stereo, the first unified stereo matching framework empowered by the teacher-student paradigm where the teacher and the student networks are trained in a mutual-beneficial manner. To explore the rich knowledge in imperfect data, we propose a consistency regularization module with weak-strong augmentation strategies. Further, in order for the teacher to provide more reliable pseudo labels, we design a confidence module, powered by left-right consistency (LRC) check and disparity distribution entropy (DDE). Extensive experiments demonstrate Semi-Stereo produces accurate and consistent predictions in untrained semantic regions and improves the performance of baseline networks in multiple tasks, including domain adaptation and domain generalization.

关键词： Measurement computer vision conferences computer network reliability Semantics Semisupervised learning Benchmark testing

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IMDeception: Grouped Information Distilling Super-Resolution Network

IMDeception: Grouped Information Distilling Super-Resolution...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Mustafa Ayazoğ lu Aselsan Research Ankara Turkey

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Single-Image-Super-Resolution (SISR) is a classical computer vision problem that has benefited from the recent advancements in deep learning methods, especially the advancements of convolutional neural networks (CNN). Although state-of-the-art methods improve the performance of SISR on several datasets, direct application of these networks for practical use is still an issue due to heavy computational load. For this purpose, recently, researchers have focused on more efficient and high-performing network structures. Information multi-distilling network (IMDN) is one of the highly efficient SISR networks with high performance and low computational load. IMDN achieves this efficiency with various mechanisms such as Intermediate Information Collection (IIC), working in a global setting, Progressive Refinement Module (PRM), and Contrast Aware Channel Attention (CCA), employed in a local setting. These mechanisms, however, do not equally contribute to the efficiency and performance of IMDN. In this work, we propose the Global Progressive Refinement Module (GPRM) as a less parameter-demanding alternative to the IIC module for feature aggregation. To further decrease the number of parameters and floating point operations per second (FLOPS), we also propose Grouped Information Distilling Blocks (GIDB). Using the proposed structures, we design an efficient SISR network called IMDeception. Experiments reveal that the proposed network performs on par with state-of-the-art models despite having a limited number of parameters and FLOPS. Furthermore, using grouped convolutions as a building block of GIDB increases room for further optimization during deployment. To show its potential, the proposed model was deployed on NVIDIA Jetson Xavier AGX and it has been shown that it can run in real-time on this edge device.

关键词： Deep learning computer vision conferences Superresolution Real-time systems Hardware Timing

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in2IN: Leveraging individual Information to Generate Human INteractions

in2IN: Leveraging individual Information to Generate Human I...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Pablo Ruiz-Ponce German Barquero Cristina Palmero Sergio Escalera José García-Rodríguez Universidad de Alicante San Vicente del Raspeig Spain Universitat de Barcelona Barcelona Spain Computer Vision Center Cerdanyola del Vallès Spain

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Generating human-human motion interactions conditioned on textual descriptions is a very useful application in many areas such as robotics, gaming, animation, and the metaverse. Alongside this utility also comes a great diffi-culty in modeling the highly dimensional inter-personal dynamics. In addition, properly capturing the intra-personal diversity of interactions has a lot of challenges. Current methods generate interactions with limited diversity of intra-person dynamics due to the limitations of the available datasets and conditioning strategies. For this, we introduce in2IN, a novel diffusion model for human-human motion generation which is conditioned not only on the textual description of the overall interaction but also on the individual descriptions of the actions performed by each person involved in the interaction. To train this model, we use a large language model to extend the InterHuman dataset with individual descriptions. As a result, in2IN achieves state-of-the- art performance in the InterHuman dataset. Furthermore, in order to increase the intra-personal diversity on the existing interaction datasets, we propose DualMDM, a model composition technique that combines the motions generated with in2IN and the motions generated by a single-person motion prior pre-trained on HumanML3D. As a result, DualMDM generates motions with higher individual diversity and improves control over the intra-person dynamics while maintaining inter-personal coherence.

关键词： Legged locomotion computer vision Metaverse Large language models conferences Dynamics Diversity reception

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Uncovering the Hidden Cost of Model Compression

Uncovering the Hidden Cost of Model Compression

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Diganta Misra Muawiz Chaudhary Agam Goyal Bharat Runwal Pin Yu Chen Carnegie Mellon University Landskape AI Mila - Quebec AI Institute Concordia University University of Wisconsin-Madison Mila - Quebec AI Institute IBM Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In an age dominated by resource-intensive foundation models, the ability to efficiently adapt to downstream tasks is crucial. Visual Prompting (VP), drawing inspiration from the prompting techniques employed in Large Language Models (LLMs), has emerged as a pivotal method for transfer learning in the realm of computer vision. As the importance of efficiency continues to rise, research into model compression has become indispensable in alleviating the computational burdens associated with training and deploying over-parameterized neural networks. A primary objective in model compression is to develop sparse and/or quantized models capable of matching or even surpassing the performance of their over-parameterized, full-precision counterparts. Although previous studies have explored the effects of model compression on transfer learning, its impact on visual prompting-based transfer remains unclear. This study aims to bridge this gap, shedding light on the fact that model compression detrimentally impacts the performance of visual prompting-based transfer, particularly evident in scenarios with low data volume. Furthermore, our findings underscore the adverse influence of sparsity on the calibration of downstream visual-prompted models. However, intriguingly, we also illustrate that such negative effects on calibration are not present when models are compressed via quantization. This empirical investigation underscores the need for a nuanced understanding beyond mere accuracy in sparse and quantized settings, thereby paving the way for further exploration in Visual Prompting techniques tailored for sparse and quantized models.

关键词： Visualization Adaptation models Solid modeling computer vision Costs Quantization (signal) Computational modeling

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Identifying Color in Motion in Video Sensors

Identifying Color in Motion in Video Sensors

引用

conference on computer vision and pattern recognition (CVPR)

作者： Gang Wu A. Rahimi E.Y. Chang Kingshy Goh T. Tsai Ankur Jain Yuan-Fang Wang Electrical and Computer Engineering UC Santa Barbara CA Proximex Inc Cupertino CA Computer Science UC Santa Barbara CA Intel Inc Santa Clara CA

Identifying or matching the surface color of a moving object in surveillance video is critical for achieving reliable object-tracking and searching. Traditional color models provide little help, since the surface of an object is usually not flat, the object’s motion can alter the surface’s orientation, and the lighting conditions can vary when the object moves. To tackle this research problem, we conduct extensive data mining on video clips collected under various lighting conditions and distances from several video-cameras. We observe how each of the eleven culture colors can drift in the color space when an object’s surface is in motion. In the color space, we then learn the drift pattern of each culture color for classifying unseen surface colors. Finally, we devise a distance function taking color drift into consideration to perform color identification and matching. Empirical studies show our approach to be very promising: achieving over 95% color-prediction accuracy.

关键词： Cameras Lighting Surveillance Colored noise Clothing Light sources Optical reflection computer science Data mining Shape

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Multimedia Content Representation, Classification and Security 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Bilge Gunsel Anil K. Jain A. Murat Tekalp Bülent Sankur

ISBN: (数字)9783540393931

ISBN: (纸本)9783540393924

We would like to welcome you to the proceedings of MRCS 2006, Workshop on Multimedia Content Representation, Classi?cation and Security, held Sept- ber 11–13, 2006, in Istanbul, Turkey. The goal of MRCS 2006 was to provide an erudite but friendly forum where academic and industrial researchers could interact, discuss emerging multimedia techniques and assess the signi?cance of content representation and security techniques within their problem domains. We received more than 190 submissions from 30 countries. All papers were subjected to thorough peer review. The ?nal decisions were based on the cri- cisms and recommendations of the reviewers and the relevance of papers to the goals of the conference. Only 52% of the papers submitted were accepted for inclusion in the program. In addition to the contributed papers, four distinguished researchers agreed to deliver keynote speeches, namely: – Ed Delp on multimedia security – Pierre Moulin on data hiding – John Smith on multimedia content-based indexing and search – Mar´ ?o A. T. Figueiredo on semi-supervised learning.

关键词： computer Applications Multimedia Information Systems Information Storage and Retrieval computer Communication Networks Information Systems Applications (incl. Internet) Image Processing and computer vision

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Fast Frequent Free Tree Mining in Graph Databases

Fast Frequent Free Tree Mining in Graph Databases

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IEEE International conference on Data Mining workshops (ICDM workshops)

作者： Peixiang Zhao Jeffrey Xu Yu Chinese University of Hong Kong Hong Kong China

Free tree, as a special graph which is connected, undirected and acyclic, is extensively used in domains such as computational biology, pattern recognition, computer networks, XML databases, etc. In this paper, we present a computationally efficient algorithm F3TM (fast frequent free tree mining) to discover all frequent free trees in a graph database. We focus ourselves on how to reduce the cost of candidate generation and minimize the number of candidates being generated. We prove a theorem that the completeness of frequent free trees can be guaranteed by growing vertices from a limited range of vertices in a free tree. Two pruning techniques, automorphism-based pruning and pruning based on canonical mapping are proposed which significantly reduce the cost of candidate generation. We conducted experimental studies on a real application dataset and we show that our F3TM outperforms the up-to-date algorithms by an order of magnitude

关键词： Tree graphs Databases pattern recognition computer networks Costs Data mining Computational biology XML Chemistry Frequency

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Semi-Supervised Classification Using Linear Neighborhood Propagation

Semi-Supervised Classification Using Linear Neighborhood Pro...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Fei Wang Changshui Zhang H.C. Shen Jingdong Wang State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China Department of Computer Science and Engineering and Digital Technology Center Hong Kong University of Science and Technology Hong Kong China

In this paper, we address the general problem of learning from both labeled and unlabeled data. Based on the reasonable assumption that the label of each data can be linearly reconstructed from its neighbors’ labels, we develop a novel approach, called Linear Neighborhood Propagation (LNP), to learn the linear construction weights and predict the labels of the unlabeled data from the labeled data. The main procedure is composed of two steps: (1) it computes the local neighborhood geometry according to the image information by solving a constrained least squares problem, (2) the obtained geometry will be used to predict the labels of unlabeled data by solving a linearly constrained quadratic optimization problem. LNP has at least three characteristics. Firstly, it is much practical in that only one free parameter, the size of neighborhood, requires user-tuning. Secondly it can be easily extended to out-of-sample examples. Finally the labels achieved from LNP can be sufficiently smooth with respect to the intrinsic structure collectively revealed by the labeled and unlabeled points. Many promising experimental results, such as object recognition, data ranking and interactive image segmentation, demonstrate the effectiveness and efficiency of the proposed approach.

关键词： Image reconstruction Computational geometry Information geometry Constraint optimization Image segmentation Semisupervised learning Laboratories Intelligent systems Automation computer science

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Differential Tracking based on Spatial-Appearance Model (SAM)

Differential Tracking based on Spatial-Appearance Model (SAM...

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conference on computer vision and pattern recognition (CVPR)

作者： Ting Yu Ying Wu Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA

A fundamental issue in differential motion analysis is the compromise between the flexibility of the matching criterion for image regions and the ability of recovering the motion. Localized matching criteria, e.g., pixel-based SSD, may enable the recovery of all motion parameters, but it does not tolerate much appearance changes. On the other hand, global criteria, e.g., matching histograms, can accommodate dramatic appearance changes, but may be blind to some motion parameters, e.g., scaling and rotation. This paper presents a novel differential approach that integrates the advantages of both in a principled way based on a spatial-appearance model (SAM) that combines local appearances variations and global spatial structures. This model can capture a large variety of appearance variations that are attributed to the local non-rigidity. At the same time, this model enables efficient recovery of all motion parameters. A maximum likelihood matching criterion is defined and rigorous analytical results are obtained that lead to a closed form solution to motion tracking. Very encouraging results demonstrate the effectiveness and efficiency of the proposed method for tracking non-rigid objects that exhibit dramatic appearance deformations, large object scale changes and partial occlusions.

关键词： Motion analysis Histograms Motion estimation Closed-form solution Tracking Uncertainty Region 9 Kernel Parameter estimation computer Society

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i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable?

i-MAE: Are Latent Representations in Masked Autoencoders Lin...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Kevin Zhang Zhiqiang Shen Peking University KNQ.AI Mohamed bin Zayed University of AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Masked image modeling (MIM) has been recognized as a strong self-supervised pre-training approach in the vision domain. However, the mechanism and properties of the learned representations by such a scheme, as well as how to further enhance the representations are so far not well-explored. In this paper, we aim to explore an interactive Masked Autoencoders (i-MAE) framework to enhance the representation capability from two aspects: (1) employing a two-way image reconstruction and a latent feature reconstruction with distillation loss to learn better features; (2) proposing a semantics-enhanced sampling strategy to boost the learned semantics in MAE. Upon the proposed i-MAE architecture, we can address two critical questions to explore the behaviors of the learned representations in MAE: (1) Whether the separability of latent representations in Masked Autoencoders is helpful for model performance? We study it by forcing the input as a mixture of two images instead of one. (2) Whether we can enhance the representations in the latent feature space by controlling the degree of semantics during sampling on Masked Autoencoders? To this end, we propose a sampling strategy within a mini-batch based on the semantics of training samples to examine this aspect. Extensive experiments are conducted on CIFAR-10/100, Tiny-ImageNet and ImageNet-1K datasets to verify the observations we discovered. Furthermore, in addition to qualitatively analyzing the characteristics of the latent representations, we examine the existence of linear separability and the degree of semantics in the latent space by proposing two evaluation schemes. The surprising and consistent results across the qualitative and quantitative experiments demonstrate that i-MAE is a superior framework design for understanding MAE frameworks, as well as achieving better representational ability.

关键词： Training Measurement computer vision Image recognition conferences Semantics Aerospace electronics

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