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BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond

BasicVSR: The Search for Essential Components in Video Super...

引用

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

作者： Chan, Kelvin C. K. Wang, Xintao Yu, Ke Dong, Chao Loy, Chen Change Nanyang Technol Univ S Lab Singapore Singapore Tencent PCG Appl Res Ctr Shenzhen Peoples R China Chinese Univ Hong Kong CUHK SenseTime Joint Lab Hong Kong Peoples R China Chinese Acad Sci SIAT SenseTime Joint Lab Shenzhen Key Lab Comp Vis & Pattern Recognit Shenzhen Inst Adv Technol Beijing Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches.

关键词： computer vision Systematics Superresolution Pipelines Noise reduction computer architecture pattern recognition

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NeRD: Neural 3D Reflection Symmetry Detector

NeRD: Neural 3D Reflection Symmetry Detector

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

作者： Zhou, Yichao Liu, Shichen Ma, Yi Univ Calif Berkeley Berkeley CA 94720 USA Univ Southern Calif Los Angeles CA 90089 USA

ISBN: (纸本)9781665445092

Recent advances have shown that symmetry, a structural prior that most objects exhibit, can support a variety of single-view 3D understanding tasks. However, detecting 3D symmetry from an image remains a challenging task. Previous works either assume the symmetry is given or detect the symmetry with a heuristic-based method. In this paper, we present NeRD, a Neural 3D Reflection Symmetry Detector, which combines the strength of learning-based recognition and geometry-based reconstruction to accurately recover the normal direction of objects' mirror planes. Specifically, we enumerate the symmetry planes with a coarse-to-fine strategy and find the best ones by building 3D cost volumes to examine the intra-image pixel correspondence from the symmetry. Our experiments show that the symmetry planes detected with our method are significantly more accurate than the planes from direct CNN regression on both synthetic and real datasets. More importantly, we also demonstrate that the detected symmetry can be used to improve the performance of downstream tasks such as pose estimation and depth map regression by a wide margin over existing methods. The code of this paper has been made public at https://***/zhou13/nerd.

关键词： computer vision Three-dimensional displays Costs Pose estimation Detectors Reflection pattern recognition

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Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation

Diagnostic Benchmark and Iterative Inpainting for Layout-Gui...

引用

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

作者： Jaemin Cho Linjie Li Zhengyuan Yang Zhe Gan Lijuan Wang Mohit Bansal UNC Chapel Hill Microsoft Research

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Spatial control is a core capability in controllable image generation. Advancements in layout-guided image generation have shown promising results on in-distribution (ID) datasets with similar spatial configurations. However, it is unclear how these models perform when facing out-of-distribution (OOD) samples with arbitrary, unseen layouts. In this paper, we propose LayoutBench, a diagnostic benchmark for layout-guided image generation that examines four categories of spatial control skills: number, position, size, and shape. We benchmark two recent representative layout-guided image generation methods and observe that the good ID layout control may not generalize well to arbitrary layouts in the wild (e.g., objects at the boundary). Next, we propose IterInpaint, a new baseline that generates foreground and background regions step-by-step via inpainting, demonstrating stronger generalizability than existing models on OOD layouts in LayoutBench. We perform quantitative and qualitative evaluation and fine- grained analysis on the four LayoutBench skills to pinpoint the weaknesses of existing models. We show comprehensive ablation studies on IterInpaint, including training task ratio, crop&paste vs. repaint, and generation order. Lastly, we evaluate the zero-shot performance of different pretrained layout-guided image generation models on LayoutBench-COCO, our new benchmark for OOD layouts with real objects, where our IterInpaint consistently outperforms SOTA baselines in all four splits.

关键词： Training computer vision Image synthesis Shape conferences Layout Benchmark testing

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Unsupervised Human Pose Estimation through Transforming Shape Templates

Unsupervised Human Pose Estimation through Transforming Shap...

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

作者： Schmidtke, Luca Vlontzos, Athanasios Ellershaw, Simon Lukens, Anna Arichi, Tomoki Kainz, Bernhard Imperial Coll London London England Kings Coll London London England Evelina London Childrens Hosp London England

ISBN: (纸本)9781665445092

Human pose estimation is a major computer vision problem with applications ranging from augmented reality and video capture to surveillance and movement tracking. In the medical context, the latter may be an important biomarker for neurological impairments in infants. Whilst many methods exist, their application has been limited by the need for well annotated large datasets and the inability to generalize to humans of different shapes and body compositions, e.g. children and infants. In this paper we present a novel method for learning pose estimators for human adults and infants in an unsupervised fashion. We approach this as a learnable template matching problem facilitated by deep feature extractors. Human-interpretable landmarks are estimated by transforming a template consisting of predefined body parts that are characterized by 2D Gaussian distributions. Enforcing a connectivity prior guides our model to meaningful human shape representations. We demonstrate the effectiveness of our approach on two different datasets including adults and infants.

关键词： Pediatrics computer vision Three-dimensional displays Shape Tracking Surveillance Pose estimation

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ACRE: Abstract Causal REasoning Beyond Covariation

ACRE: Abstract Causal REasoning Beyond Covariation

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

作者： Zhang, Chi Jia, Baoxiong Edmonds, Mark Zhu, Song-Chun Zhu, Yixin UCLA Ctr Vis Cognit Learning & Auton Los Angeles CA 90095 USA

ISBN: (纸本)9781665445092

Causal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especially in scenarios with only sparse and limited data. Humans, even young toddlers, can induce causal relationships surprisingly well in various settings despite its notorious difficulty. However, in contrast to the commonplace trait of human cognition is the lack of a diagnostic benchmark to measure causal induction for modern Artificial Intelligence (AI) systems. Therefore, in this work, we introduce the Abstract Causal REasoning (ACRE) dataset for systematic evaluation of current vision systems in causal induction. Motivated by the stream of research on causal discovery in Blicket experiments, we query a visual reasoning system with the following four types of questions in either an independent scenario or an interventional scenario: direct, indirect, screening-off, and backward-blocking, intentionally going beyond the simple strategy of inducing causal relationships by covariation. By analyzing visual reasoning architectures on this testbed, we notice that pure neural models tend towards an associative strategy under their chance-level performance, whereas neuro-symbolic combinations struggle in backward-blocking reasoning. These deficiencies call for future research in models with a more comprehensive capability of causal induction.

关键词： Visualization Pediatrics Systematics Benchmark testing Particle measurements Cognition pattern recognition

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BABEL: Bodies, Action and Behavior with English Labels

BABEL: Bodies, Action and Behavior with English Labels

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

作者： Punnakkal, Abhinanda R. Chandrasekaran, Arjun Athanasiou, Nikos Quiros-Ramirez, Alejandra Black, Michael J. Max Planck Inst Intelligent Syst Tubingen Germany Univ Konstanz Constance Germany

ISBN: (数字)9781665445092

ISBN: (纸本)9781665445092

Understanding the semantics of human movement - the what, how and why of the movement - is an important problem that requires datasets of human actions with semantic labels. Existing datasets take one of two approaches. Large-scale video datasets contain many action labels but do not contain ground-truth 3D human motion. Alternatively, motion-capture (mocap) datasets have precise body motions but are limited to a small number of actions. To address this, we present BABEL, a large dataset with language labels describing the actions being performed in mocap sequences. BABEL consists of language labels for over 43 hours of mocap sequences from AMASS, containing over 250 unique actions. Each action label in BABEL is precisely aligned with the duration of the corresponding action in the mocap sequence. BABELalso allows overlap of multiple actions, that may each span different durations. This results in a total of over 66000 action segments. The dense annotations can be leveraged for tasks like action recognition, temporal localization, motion synthesis, etc. To demonstrate the value of BABEL as a benchmark, we evaluate the performance of models on 3D action recognition. We demonstrate that BABEL poses interesting learning challenges that are applicable to real-world scenarios, and can serve as a useful benchmark for progress in 3D action recognition.

关键词： Location awareness Solid modeling computer vision Three-dimensional displays Motion segmentation Semantics Benchmark testing

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Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero-shot Medical Image Segmentation

Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP f...

引用

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

作者： Sidra Aleem Fangyijie Wang Mayug Maniparambil Eric Arazo Julia Dietlmeier Kathleen Curran Noel E. O’Connor Suzanne Little ML-Labs Dublin City University ML-Labs University College Dublin Centre for Applied AI (CeADAR) University College Dublin Ireland Insight SFI Centre for Data Analytics Dublin City University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt-driven segmentation model, excels in segmentation tasks across diverse domains, while CLIP is renowned for its zero-shot recognition capabilities. However, their unified potential has not yet been explored in medical image segmentation. To adapt SAM, to medical imaging, existing methods primarily rely on tuning strategies that require extensive data or prior prompts tailored to the specific task, making it particularly challenging when only a limited number of data samples are available. This work presents an in-depth exploration of integrating SAM and CLIP into a unified framework for medical image segmentation. Specifically, we propose a simple unified framework, SaLIP, for organ segmentation. Initially, SAM is used for part-based segmentation within the image, followed by CLIP to retrieve the mask corresponding to the region of interest (ROI) from the pool of SAM’s generated masks. Finally, SAM is prompted by the retrieved ROI to segment a specific organ. Thus, SaLIP is training/fine-tuning free and does not rely on domain expertise or labeled data for prompt engineering. Our method shows substantial enhancements in zero-shot segmentation, showcasing notable improvements in DICE scores across diverse segmentation tasks like brain (63.46%), lung (50.11%), and fetal head (30.82%), when compared to un-prompted SAM. Code and text prompts are available at SaLIP.

关键词： Image segmentation Adaptation models Filtering Inference mechanisms Lung Robustness pattern recognition

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Simple In-place Data Augmentation for Surveillance Object Detection

Simple In-place Data Augmentation for Surveillance Object De...

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

作者： Munkh-Erdene Otgonbold Ganzorig Batnasan Munkhjargal Gochoo Department of Computer Science and Software Engineering United Arab Emirates University UAE Department of Electronics Mongolian University of Science and Technology Mongolia Emirates Center for Mobility Research United Arab Emirates University UAE

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Motivated by the need to improve model performance in traffic monitoring tasks with limited labeled samples, we propose a straightforward augmentation technique tailored for object detection datasets, specifically designed for stationary camera-based applications. Our approach focuses on placing objects in the same positions as the originals to ensure its effectiveness. By applying in-place augmentation on objects from the same camera input image, we address the challenge of overlapping with original and previously selected objects. Through extensive testing on two traffic monitoring datasets, we illustrate the efficacy of our augmentation strategy in improving model performance, particularly in scenarios with limited labeled samples and imbalanced class distributions. Notably, our method achieves comparable performance to models trained on the entire dataset while utilizing only 8.5 percent of the original data. Moreover, we report significant improvements, with mAP@.5 increasing from 0.4798 to 0.5025, and the mAP@.5:.95 rising from 0.29 to 0.3138 on the FishEye8K dataset. These results highlight the potential of our augmentation approach in enhancing object detection models for traffic monitoring applications.

关键词： Image segmentation Surveillance Scalability conferences Object detection Data augmentation Cameras

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TAME: Task Agnostic Continual Learning using Multiple Experts

TAME: Task Agnostic Continual Learning using Multiple Expert...

引用

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

作者： Haoran Zhu Maryam Majzoubi Arihant Jain Anna Choromanska New York University Google

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The goal of lifelong learning is to continuously learn from non-stationary distributions, where the non-stationarity is typically imposed by a sequence of distinct tasks. Prior works have mostly considered idealistic settings, where the identity of tasks is known at least at training. In this paper we focus on a fundamentally harder, so-called task-agnostic, setting where the task identities are not known and the learning machine needs to infer them from the observations. Our algorithm, which we call TAME (Task-Agnostic continual learning using Multiple Experts), automatically detects the shift in data distributions and switches between task expert networks in an online manner. At training, the strategy for switching between tasks hinges on an extremely simple observation that for each new coming task there occurs a statistically-significant deviation in the value of the loss function that marks the onset of this new task. At inference, the switching between experts is governed by the selector network that forwards the test sample to its relevant expert network. The selector network is trained on a small subset of data drawn uniformly at random. We control the growth of the task expert networks as well as selector network by employing pruning. Our experimental results show the efficacy of our approach on benchmark continual learning data sets, outperforming the previous task-agnostic methods and even the techniques that admit task identities at both training and testing, while at the same time using a comparable model size.

关键词： Training Continuing education computer vision conferences Computational modeling Switches Fasteners

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Discrimination-Aware Mechanism for Fine-grained Representation Learning

Discrimination-Aware Mechanism for Fine-grained Representati...

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

作者： Xu, Furong Wang, Meng Zhang, Wei Cheng, Yuan Chu, Wei Ant Financial Serv Grp Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Recently, with the emergence of retrieval requirements for certain individual in the same superclass, e.g., birds, persons, cars, fine-grained recognition task has attracted a significant amount of attention from academia and industry. In fine-grained recognition scenario, the inter-class differences are quite diverse and subtle, which makes it challenging to extract all the discriminative cues. Traditional training mechanism optimizes the overall discriminativeness of the whole feature. It may stop early when some feature elements has been trained to distinguish training samples well, leaving other elements insufficiently trained for a feature. This would result in a less generalizable feature extractor that only captures major discriminative cues and ignores subtle ones. Therefore, there is a need for a training mechanism that enforces the discriminativeness of all the elements in the feature to capture more the subtle visual cues. In this paper, we propose a Discrimination-Aware Mechanism (DAM) that iteratively identifies insufficiently trained elements and improves them. DAM is able to increase the number of well learned elements, which captures more visual cues by the feature extractor. In this way, a more informative representation is learned, which brings better generalization performance. We show that DAM can be easily applied to both proxy-based and pair-based loss functions, and thus can be used in most existing fine-grained recognition paradigms. Comprehensive experiments on CUB200-2011, Cars196, Market-1501, and MSMT17 datasets demonstrate the advantages of our DAM based loss over the related state-of-the-art approaches.

关键词： Training Industries Visualization computer vision Dams Feature extraction Birds

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