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Pose Estimation for Two-View Panoramas based on Keypoint Matching: a Comparative Study and Critical Analysis

Pose Estimation for Two-View Panoramas based on Keypoint Mat...

引用

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

作者： Murrugarra-Llerena, Jeffri da Silveira, Thiago L. T. Jung, Claudio R. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Pose estimation is a crucial problem in several computer vision and robotics applications. For the two-view scenario, the typical pipeline consists of finding point correspondences between the two views and using them to estimate the pose. However, most available keypoint extraction and matching methods were designed to work with perspective images and may fail under not-affine distortions present in wide-angle or omnidirectional media, which are becoming increasingly popular in recent years. This paper presents a comprehensive comparative analysis of different keypoint matching algorithms for panoramas coupled to different linear and non-linear approaches for pose estimation. As an additional contribution, we explore a recent approach for mitigating spherical distortions using tangent plane projections, which can be coupled with any planar descriptor, and allows the adaptation of recent learning-based methods. We evaluate the combination of keypoint matching and pose estimation methods using the rotation and translation error of the estimated pose in different scenarios (indoor and outdoor), and our results indicate that SPHORB and "tangent SIFT" are competitive algorithms. We also show that tangent plane adaptations frequently present competitive results, and some optimization steps consistently improve the performance in all methods. We provide code at https://***/Artcs1/Keypoints

关键词： Learning systems Couplings computer vision Planarization Pose estimation Nonlinear distortion Pipelines

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Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for vision Decoding

Seeing Beyond the Brain: Conditional Diffusion Model with Sp...

引用

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

作者： Chen, Zijiao Qing, Jiaxin Xiang, Tiange Yue, Wan Lin Zhou, Juan Helen Natl Univ Singapore Singapore Singapore Chinese Univ Hong Kong Hong Kong Peoples R China Stanford Univ Stanford CA USA

ISBN: (纸本)9798350301298

Decoding visual stimuli from brain recordings aims to deepen our understanding of the human visual system and build a solid foundation for bridging human and computer vision through the Brain-computer Interface. However, reconstructing high-quality images with correct semantics from brain recordings is a challenging problem due to the complex underlying representations of brain signals and the scarcity of data annotations. In this work, we present MinD-Vis: Sparse Masked Brain Modeling with Double-Conditioned Latent Diffusion Model for Human vision Decoding. Firstly, we learn an effective self-supervised representation of fMRI data using mask modeling in a large latent space inspired by the sparse coding of information in the primary visual cortex. Then by augmenting a latent diffusion model with double-conditioning, we show that MinD-Vis can reconstruct highly plausible images with semantically matching details from brain recordings using very few paired annotations. We benchmarked our model qualitatively and quantitatively;the experimental results indicate that our method outperformed state-of-the-art in both semantic mapping (100-way semantic classification) and generation quality (FID) by 66% and 41% respectively. An exhaustive ablation study was also conducted to analyze our framework.

关键词： cell microscopy Medical and biological vision

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SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos

SoccerNet-Tracking: Multiple Object Tracking Dataset and Ben...

引用

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

作者： Cioppa, Anthony Giancola, Silvio Deliege, Adrien Kang, Le Zhou, Xin Cheng, Zhiyu Ghanem, Bernard Van Droogenbroeck, Marc Univ Liege Liege Belgium KAUST Thuwal Saudi Arabia Baidu Res Beijing Peoples R China

ISBN: (纸本)9781665487399

Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation. Video processing can help automating the extraction of those information, without the need of any invasive sensor, hence applicable to any team on any stadium. Yet, the availability of datasets to train learnable models and benchmarks to evaluate methods on a common testbed is very limited. In this work, we propose a novel dataset for multiple object tracking composed of 200 sequences of 30s each, representative of challenging soccer scenarios, and a complete 45-minutes half-time for long-term tracking. The dataset is fully annotated with bounding boxes and tracklet IDs, enabling the training of MOT baselines in the soccer domain and a full benchmarking of those methods on our segregated challenge sets. Our analysis shows that multiple player, referee and ball tracking in soccer videos is far from being solved, with several improvement required in case of fast motion or in scenarios of severe occlusion.

关键词： Training computer vision conferences Benchmark testing Object tracking Data mining Sports

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Embedding Arithmetic of Multimodal Queries for Image Retrieval

Embedding Arithmetic of Multimodal Queries for Image Retriev...

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

作者： Couairon, Guillaume Douze, Matthijs Cord, Matthieu Schwenk, Holger Meta AI Menlo Pk CA 94025 USA Sorbonne Univ Paris France Valeo St Ouen Sur Seine France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Latent text representations exhibit geometric regularities, such as the famous analogy: queen is to king what woman is to man. Such structured semantic relations were not demonstrated on image representations. Recent works aiming at bridging this semantic gap embed images and text into a multimodal space, enabling the transfer of text-defined transformations to the image modality. We introduce the SIMAT dataset to evaluate the task of Image Retrieval with Multimodal queries. SIMAT contains 6k images and 18k textual transformation queries that aim at either replacing scene elements or changing pairwise relationships between scene elements. The goal is to retrieve an image consistent with the (source image, text transformation) query. We use an image/text matching oracle (OSCAR) to assess whether the image transformation is successful. The SIMAT dataset will be publicly available. We use SIMAT to evaluate the geometric properties of multimodal embedding spaces trained with an image/text matching objective, like CLIP. We show that vanilla CLIP embeddings are not very well suited to transform images with delta vectors, but that a simple finetuning on the COCO dataset can bring dramatic improvements. We also study whether it is beneficial to leverage pretrained universal sentence encoders (FastText, LASER and LaBSE).

关键词： computer vision conferences Semantics Image retrieval Lasers Transforms Image representation

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Ensemble Spatial and Temporal vision Transformer for Action Units Detection

Ensemble Spatial and Temporal Vision Transformer for Action ...

引用

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

作者： Tu Vu, Ngoc Thong Huynh, Van Nghia Nguyen, Trong Kim, Soo-Hyung Chonnam National University Department of Ai Convergence Korea Republic of

ISBN: (纸本)9798350302493

Facial Action Units detection (FAUs) represents a fine-grained classification problem that involves identifying different units on the human face, as defined by the Facial Action Coding System. In this paper, we present a simple yet efficient vision Transformer-based approach for addressing the task of Action Units (AU) detection in the context of Affective Behavior Analysis in-the-wild (ABAW) competition. We employ the Video vision Transformer(ViViT) Network to capture the temporal facial change in the video. Besides, to reduce massive size of the vision Transformers model, we replace the ViViT feature extraction layers with the CNN backbone (Regnet). Our model outperform the baseline model of ABAW 2023 challenge [8], with a notable 14% difference in result. Our team has achieved a position within the top 5 teams in the ABAW 2023 competition, scoring slightly below the top three and four teams by a narrow margin of 0.27% and 0.43%, respectively. © 2023 ieee.

关键词： computer vision

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Identity Preserving Loss for Learned Image Compression

Identity Preserving Loss for Learned Image Compression

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

作者： Xiao, Jiuhong Aggarwal, Lavisha Banerjee, Prithviraj Aggarwal, Manoj Medioni, Gerard NYU 550 1St Ave New York NY 10012 USA Amazon Seattle WA 98109 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep learning model inference on embedded devices is challenging due to the limited availability of computation resources. A popular alternative is to perform model inference on the cloud, which requires transmitting images from the embedded device to the cloud. Image compression techniques are commonly employed in such cloud-based architectures to reduce transmission latency over low bandwidth networks. This work proposes an end-to-end image compression framework that learns domain-specific features to achieve higher compression ratios than standard HEVC/JPEG compression techniques while maintaining accuracy on downstream tasks (e.g., recognition). Our framework does not require fine-tuning of the downstream task, which allows us to drop-in any off-the-shelf downstream task model without retraining. We choose faces as an application domain due to the ready availability of datasets and off-the-shelf recognition models as representative downstream tasks. We present a novel Identity Preserving Reconstruction (IPR) loss function which achieves Bits-Per-Pixel (BPP) values that are similar to 38% and similar to 42% of CRF-23 HEVC compression for LFW (low-resolution) and CelebA-HQ (high-resolution) datasets, respectively, while maintaining parity in recognition accuracy. The superior compression ratio is achieved as the model learns to retain the domain-specific features (e.g., facial features) while sacrificing details in the background. Furthermore, images reconstructed by our proposed compression model are robust to changes in downstream model architectures. We show at-par recognition performance on the LFW dataset with an unseen recognition model while retaining a lower BPP value of similar to 38% of CRF-23 HEVC compression.

关键词： Image coding Image recognition Face recognition Computational modeling Intellectual property computer architecture Robustness

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Architectural Backdoors in Neural Networks

Architectural Backdoors in Neural Networks

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

作者： Bober-Irizar, Mikel Shumailov, Ilia Zhao, Yiren Mullins, Robert Papernot, Nicolas Univ Cambridge Cambridge England Vector Inst Toronto ON Canada Univ Oxford Oxford England Imperial Coll London London England Univ Toronto Toronto ON Canada

ISBN: (纸本)9798350301298

Machine learning is vulnerable to adversarial manipulation. Previous literature demonstrated that at the training stage attackers can manipulate data [14] and data sampling procedures [29] to control model behaviour. A common attack goal is to plant backdoors i.e. force the victim model to learn to recognise a trigger known only by the adversary. In this paper, we introduce a new class of backdoor attacks that hide inside model architectures i.e. in the inductive bias of the functions used to train. These backdoors are simple to implement, for instance by publishing open-source code for a backdoored model architecture that others will reuse unknowingly. We demonstrate that model architectural backdoors represent a real threat and, unlike other approaches, can survive a complete re-training from scratch. We formalise the main construction principles behind architectural backdoors, such as a connection between the input and the output, and describe some possible protections against them. We evaluate our attacks on computer vision benchmarks of different scales and demonstrate the underlying vulnerability is pervasive in a variety of common training settings.

关键词： accountability ethics in vision fairness privacy Transparency

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Filtering, Distillation, and Hard Negatives for vision-Language Pre-Training

Filtering, Distillation, and Hard Negatives for Vision-Langu...

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

作者： Radenovic, Filip Dubey, Abhimanyu Kadian, Abhishek Mihaylov, Todor Vandenhende, Simon Patel, Yash Wen, Yi Ramanathan, Vignesh Mahajan, Dhruv Meta AI New York NY 10003 USA Czech Tech Univ Prague Czech Republic

ISBN: (纸本)9798350301298

vision-language models trained with contrastive learning on large-scale noisy data are becoming increasingly popular for zero-shot recognition problems. In this paper we improve the following three aspects of the contrastive pre-training pipeline: dataset noise, model initialization and the training objective. First, we propose a straightforward filtering strategy titled Complexity, Action, and Text-spotting (CAT) that significantly reduces dataset size, while achieving improved performance across zero-shot vision-language tasks. Next, we propose an approach titled Concept Distillation to leverage strong unimodal representations for contrastive training that does not increase training complexity while outperforming prior work. Finally, we modify the traditional contrastive alignment objective, and propose an importance-sampling approach to up-sample the importance of hard-negatives without adding additional complexity. On an extensive zero-shot benchmark of 29 tasks, our Distilled and Hard-negative Training (DiHT) approach improves on 20 tasks compared to the baseline. Furthermore, for few-shot linear probing, we propose a novel approach that bridges the gap between zero-shot and few-shot performance, substantially improving over prior work. Models are available at ***/facebookresearch/diht.

关键词： and reasoning language vision

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Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflection

Fresnel Microfacet BRDF: Unification of Polari-Radiometric S...

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

作者： Ichikawa, Tomoki Fukao, Yoshiki Nobuhara, Shohei Nishino, Ko Kyoto Univ Grad Sch Informat Kyoto Japan

ISBN: (纸本)9798350301298

computer vision applications have heavily relied on the linear combination of Lambertian diffuse and microfacet specular reflection models for representing reflected radiance, which turns out to be physically incompatible and limited in applicability. In this paper, we derive a novel analytical reflectance model, which we refer to as Fresnel Microfacet BRDF model, that is physically accurate and generalizes to various real-world surfaces. Our key idea is to model the Fresnel reflection and transmission of the surface microgeometry with a collection of oriented mirror facets, both for body and surface reflections. We carefully derive the Fresnel reflection and transmission for each microfacet as well as the light transport between them in the subsurface. This physically-grounded modeling also allows us to express the polarimetric behavior of reflected light in addition to its radiometric behavior. That is, FMBRDF unifies not only body and surface reflections but also light reflection in radiometry and polarization and represents them in a single model. Experimental results demonstrate its effectiveness in accuracy, expressive power, image-based estimation, and geometry recovery.

关键词： Physics-based vision and shape-from-X

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Analysis of Temporal Tensor Datasets on Product Grassmann Manifold

Analysis of Temporal Tensor Datasets on Product Grassmann Ma...

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

作者： Batalo, Bojan Souza, Lincon S. Gatto, Bernardo B. Sogi, Naoya Fukui, Kazuhiro Univ Tsukuba Tsukuba Ibaraki Japan AIST Tokyo Japan AIST Tsukuba Ibaraki Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Growing abundance of multi-dimensional data creates a need for efficient data exploration and analysis. In this paper, we address this need by tackling the task of tensor dataset visualization and clustering, as tensors are a natural form of multi-dimensional data. Previous work has shown that representing individual tensor modes via respective linear subspaces and unifying them on the product Grassmann manifold (PGM) is an effective and memory-efficient way of representation. However, such representation may lead to loss of valuable temporal information. To address this issue, we model temporal tensor modes with a Hankel-like matrix, preserving sequence information and encoding it with a linear subspace, fully compatible with PGM. Unifying regular tensor modes and Hankel-like representation of regular tensor modes then enriches representation on the PGM, with minimal increase in computational complexity. By relying on geodesic distance on the manifold, we facilitate analysis of multi-dimensional datasets in two ways: 1) by enabling straightforward visualizations using algorithms such as t-SNE;and 2) by fostering clustering of data using distance- or similarity-based methods such as spectral clustering. We evaluate our approach on hand gesture and action recognition datasets as exemplars of temporal tensor datasets.

关键词： Manifolds Measurement Geometry Tensors Data visualization pattern recognition Gyroscopes

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