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Camera Pose Estimation using Implicit Distortion Models

Camera Pose Estimation using Implicit Distortion Models

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Pan, Linfei Pollefeys, Marc Larsson, Viktor Swiss Fed Inst Technol Zurich Switzerland Microsoft Redmond WA USA Lund Univ Lund Sweden

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout the image. The proposed model is effectively parameter-free and allows us to optimize the 6 degree-of-freedom camera pose without explicitly knowing the intrinsic calibration. We show that the method is applicable to a wide selection of cameras with varying distortion and in multiple applications, such as visual localization and structure-from-motion.

关键词： Solid modeling computer vision Visualization Computational modeling Pose estimation Cameras Distortion

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CamoFocus: Enhancing Camouflage Object Detection with Split-Feature Focal Modulation and Context Refinement

CamoFocus: Enhancing Camouflage Object Detection with Split-...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Khan, Abbas Khan, Mustaqeem Gueaieb, Wail El Saddik, Abdulmotaleb De Masi, Giulia Karr, Fakhri MBZUAI Abu Dhabi U Arab Emirates Univ Ottawa Ottawa ON Canada Technol Innovat Inst Abu Dhabi U Arab Emirates Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9798350318920;9798350318937

Camouflage Object Detection (COD) involves the challenge of isolating a target object from a visually similar background, presenting a formidable challenge for learning algorithms. Drawing inspiration from state-of-the-art (SOTA) Focal Modulation Networks, our objective is to proficiently modulate the foreground and background components, thereby capturing the distinct features of each. We introduce a Feature Split and Modulation (FSM) module to attain this goal. This module efficiently separates the object from the background by utilizing foreground and background modulators guided by a supervisory mask. For enhanced feature refinement, we propose a Context Refinement Module (CRM), which considers features acquired from FSM across various spatial scales, leading to comprehensive enrichment and highly accurate prediction maps. Through extensive experimentation, we showcase the superiority of CamoFocus over recent SOTA COD methods. Our evaluations encompass diverse benchmark datasets, including CAMO, COD10K, CHAMELEON, and NC4K. The findings underscore the potential and significance of the proposed CamoFocus model and establish its efficacy in addressing the critical challenges of camouflage object detection.

关键词： Algorithms Algorithms and algorithms Applications Biomedical / healthcare / medicine formulations Image recognition and understanding Machine learning architectures

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DDAM-PS: Diligent Domain Adaptive Mixer for Person Search

DDAM-PS: Diligent Domain Adaptive Mixer for Person Search

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Almansoori, Mohammed Khaleed Fiaz, Mustansar Cholakkal, Hisham Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates IBM Corp Armonk NY USA

ISBN: (纸本)9798350318920;9798350318937

Person search (PS) is a challenging computer vision problem where the objective is to achieve joint optimization for pedestrian detection and re-identification (ReID). Although previous advancements have shown promising performance in the field under fully and weakly supervised learning fashion, there exists a major gap in investigating the domain adaptation ability of PS models. In this paper, we propose a diligent domain adaptive mixer (DDAM) for person search (DDAP-PS) framework that aims to bridge a gap to improve knowledge transfer from the labeled source domain to the unlabeled target domain. Specifically, we introduce a novel DDAM module that generates moderate mixed-domain representations by combining source and target domain representations. The proposed DDAM module encourages domain mixing to minimize the distance between the two extreme domains, thereby enhancing the ReID task. To achieve this, we introduce two bridge losses and a disparity loss. The objective of the two bridge losses is to guide the moderate mixed-domain representations to maintain an appropriate distance from both the source and target domain representations. The disparity loss aims to prevent the moderate mixed-domain representations from being biased towards either the source or target domains, thereby avoiding overfitting. Furthermore, we address the conflict between the two subtasks, localization and ReID, during domain adaptation. To handle this cross-task conflict, we forcefully decouple the normaware embedding, which aids in better learning of the moderate mixed-domain representation. We conduct experiments to validate the effectiveness of our proposed method. Our approach demonstrates favorable performance on the challenging PRW and CUHK-SYSU datasets. Our source code is publicly available at https://***/mustansarfiaz/DDAM-PS.

关键词： Algorithms Algorithms Image recognition and understanding Video recognition and understanding

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Let's Observe Them Over Time: An Improved Pedestrian Attribute recognition Approach

Let's Observe Them Over Time: An Improved Pedestrian Attribu...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Thakare, Kamalakar Vijay Dogra, Debi Prosad Choi, Heeseung Kim, Haksub Kim, Ig-Jae Indian Inst Technol Bhubaneswar 752050 Odisha India Korea Inst Sci & Technol Artificial Intelligence & Robot Inst Seoul 02792 South Korea Yonsei Univ Yonsei KIST Convergence Res Inst Seoul 03722 South Korea

ISBN: (纸本)9798350318920;9798350318937

Despite poor image quality, occlusions, and small training datasets, recent pedestrian attribute recognition (PAR) methods have achieved considerable performance. However, leveraging only spatial information of different attributes limits their reliability and generalizability. This paper introduces a multi-perspective approach to reduce over-dependence on spatial clues of a single perspective and exploits other aspects available in multiple perspectives. In order to tackle image quality and occlusions, we exploit different spatial clues present across images and handpick the best attribute-specific features to classify. Precisely, we extract the class-activation energy of each attribute and correlate it with the corresponding energy present across other images using the proposed Self-Attentive Cross Relation Module. In the next stage, we fuse this correlation information with similar clues accumulated from the other images. Lastly, we train a classification neural network using combined correlation information with two different losses. We have validated our method on four widely used PAR datasets, namely Market1501, PETA, PA-100k, and Duke. Our method achieves superior performance over most existing methods, demonstrating the effectiveness of a multi-perspective approach in PAR.

关键词： Algorithms Algorithms Algorithms and algorithms Biometrics body pose face formulations gesture Image recognition and understanding Machine learning architectures

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MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable Uncertainty

MonoProb: Self-Supervised Monocular Depth Estimation with In...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Marsal, Remi Chabot, Florian Loesch, Angelique Grolleau, William Sahbi, Hichem Univ Paris Saclay LIST CEA F-91120 Palaiseau France Sorbonne Univ CNRS LIP6 c F-75005 Paris France

ISBN: (纸本)9798350318920;9798350318937

Self-supervised monocular depth estimation methods aim to be used in critical applications such as autonomous vehicles for environment analysis. To circumvent the potential imperfections of these approaches, a quantification of the prediction confidence is crucial to guide decision-making systems that rely on depth estimation. In this paper, we propose MonoProb, a new unsupervised monocular depth estimation method that returns an interpretable uncertainty, which means that the uncertainty reflects the expected error of the network in its depth predictions. We rethink the stereo or the structure-from-motion paradigms used to train unsupervised monocular depth models as a probabilistic problem. Within a single forward pass inference, this model provides a depth prediction and a measure of its confidence, without increasing the inference time. We then improve the performance on depth and uncertainty with a novel self-distillation loss for which a student is supervised by a pseudo ground truth that is a probability distribution on depth output by a teacher. To quantify the performance of our models we design new metrics that, unlike traditional ones, measure the absolute performance of uncertainty predictions. Our experiments highlight enhancements achieved by our method on standard depth and uncertainty metrics as well as on our tailored metrics. https://***/CEA-LIST/MonoProb

关键词： 3D computer vision Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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Kaizen: Practical self-supervised continual learning with continual fine-tuning

Kaizen: Practical self-supervised continual learning with co...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Tang, Chi Ian Qendrol, Lorena Spathis, Dimitris Kawsar, Fahim Mascolo, Cecilia Mathur, Akhil Nokia Bell Labs Cambridge England Univ Cambridge Cambridge England

ISBN: (纸本)9798350318920;9798350318937

Self-supervised learning (SSL) has shown remarkable performance in computer vision tasks when trained offline. However, in a Continual Learning (CL) scenario where new data is introduced progressively, models still suffer from catastrophic forgetting. Retraining a model from scratch to adapt to newly generated data is time-consuming and inefficient. Previous approaches suggested re-purposing self-supervised objectives with knowledge distillation to mitigate forgetting across tasks, assuming that labels from all tasks are available during fine-tuning. In this paper, we generalize self-supervised continual learning in a practical setting where available labels can be leveraged in any step of the SSL process. With an increasing number of continual tasks, this offers more flexibility in the pre-training and fine-tuning phases. With Kaizen(1), we introduce a training architecture that is able to mitigate catastrophic forgetting for both the feature extractor and classifier with a carefully designed loss function. By using a set of comprehensive evaluation metrics reflecting different aspects of continual learning, we demonstrated that Kaizen significantly outperforms previous SSL models in competitive vision benchmarks, with up to 16.5% accuracy improvement on split CIFAR-100. Kaizen is able to balance the trade-off between knowledge retention and learning from new data with an end-to-end model, paving the way for practical deployment of continual learning systems.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

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BPKD: Boundary Privileged Knowledge Distillation For Semantic Segmentation

BPKD: Boundary Privileged Knowledge Distillation For Semanti...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Liu, Liyang Wang, Zihan Minh Hieu Phan Zhang, Bowen Ge, Jinchao Liu, Yifan Univ Adelaide Adelaide SA Australia Univ Queensland Brisbane Qld Australia CSIROs Data61 Eveleigh Australia

ISBN: (纸本)9798350318920;9798350318937

Current knowledge distillation approaches in semantic segmentation tend to adopt a holistic approach that treats all spatial locations equally. However, for dense prediction, students' predictions on edge regions are highly uncertain due to contextual information leakage, requiring higher spatial sensitivity knowledge than the body regions. To address this challenge, this paper proposes a novel approach called boundary-privileged knowledge distillation (BPKD). BPKD distills the knowledge of the teacher model's body and edges separately to the compact student model. Specifically, we employ two distinct loss functions: (i) edge loss, which aims to distinguish between ambiguous classes at the pixel level in edge regions;(ii) body loss, which utilizes shape constraints and selectively attends to the inner-semantic regions. Our experiments demonstrate that the proposed BPKD method provides extensive refinements and aggregation for edge and body regions. Additionally, the method achieves state-of-the-art distillation performance for semantic segmentation on three popular benchmark datasets, highlighting its effectiveness and generalization ability. BPKD shows consistent improvements across a diverse array of lightweight segmentation structures, including both CNNs and transformers, underscoring its architecture-agnostic adaptability. The code is available at https://***/AkideLiu/BPKD.

关键词： Algorithms Image recognition and understanding

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IFOR: Iterative Flow Minimization for Robotic Object Rearrangement

IFOR: Iterative Flow Minimization for Robotic Object Rearran...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Goyal, Ankit Mousavian, Arsalan Paxton, Chris Chao, Yu-Wei Okorn, Brian Deng, Jia Fox, Dieter NVIDIA Santa Clara CA 95051 USA Princeton Univ Princeton NJ 08544 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments. We propose IFOR, Iterative Flow Minimization for Robotic Object Rearrangement, an end-to-end method for the challenging problem of object rearrangement for unknown objects given an RGBD image of the original and final scenes. First, we learn an optical flow model based on RAFT to estimate the relative transformation of the objects purely from synthetic data. This flow is then used in an iterative minimization algorithm to achieve accurate positioning of previously unseen objects. Crucially, we show that our method applies to cluttered scenes, and in the real world, while training only on synthetic data. Videos are available at https://***/***.

关键词： Training computer vision Minimization Iterative algorithms Data models pattern recognition Optical flow

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Improving vision-and-Language Reasoning via Spatial Relations Modeling

Improving Vision-and-Language Reasoning via Spatial Relation...

引用

ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Yang, Cheng Xu, Rui Guo, Ye Huang, Peixiang Chen, Yiru Ding, Wenkui Wang, Zhongyuan Zhou, Hong Zhejiang Univ Hangzhou Peoples R China Peking Univ Beijing Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Visual commonsense reasoning (VCR) is a challenging multi-modal task, which requires high-level cognition and commonsense reasoning ability about the real world. In recent years, large-scale pre-training approaches have been developed and promoted the state-of-the-art performance of VCR. However, the existing approaches almost employ the BERT-like objectives to learn multi-modal representations. These objectives motivated from the text-domain are insufficient for the excavation on the complex scenario of visual modality. Most importantly, the spatial distribution of the visual objects is basically neglected. To address the above issue, we propose to construct the spatial relation graph based on the given visual scenario. Further, we design two pre-training tasks named object position regression (OPR) and spatial relation classification (SRC) to learn to reconstruct the spatial relation graph respectively. Quantitative analysis suggests that the proposed method can guide the representations to maintain more spatial context and facilitate the attention on the essential visual regions for reasoning. We achieve the state-of-the-art results on VCR and two other vision-and-language reasoning tasks VQA, and NLVR2.

关键词： Algorithms Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures vision + language and/or other modalities

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APES: Articulated Part Extraction from Sprite Sheets

APES: Articulated Part Extraction from Sprite Sheets

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Xu, Zhan Fisher, Matthew Zhou, Yang Aneja, Deepali Dudhat, Rushikesh Yi, Li Kalogerakis, Evangelos Univ Massachusetts Amherst MA 01003 USA Adobe Res San Jose CA USA Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Rigged puppets are one of the most prevalent representations to create 2D character animations. Creating these puppets requires partitioning characters into independently moving parts. In this work, we present a method to automatically identify such articulated parts from a small set of character poses shown in a sprite sheet, which is an illustration of the character that artists often draw before puppet creation. Our method is trained to infer articulated parts, e.g. head, torso and limbs, that can be reassembled to best reconstruct the given poses. Our results demonstrate significantly better performance than alternatives qualitatively and quantitatively. Our project page https://***/parts/includes our code and data.

关键词： Torso computer vision Codes Animation pattern recognition Sprites (computer)

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