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Open-world Instance Segmentation: Top-down Learning with Bottom-up Supervision

Open-world Instance Segmentation: Top-down Learning with Bot...

引用

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

作者： Tarun Kalluri Weiyao Wang Heng Wang Manmohan Chandraker Lorenzo Torresani Du Tran UC San Diego Meta AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Top-down instance segmentation architectures excel with predefined closed-world taxonomies but exhibit biases and performance degradation in open-world scenarios. In this work, we introduce bottom-Up and top-Down Open-world Segmentation (UDOS), a novel approach that combines classical bottom-up segmentation methods within a top-down learning framework. UDOS leverages a top-down network trained with weak supervision derived from class-agnostic bottom-up segmentation to predict object parts. These part-masks undergo affinity-based grouping and refinement to generate precise instance-level segmentations. UDOS balances the efficiency of top-down architectures with the capacity to handle unseen categories through bottom-up supervision. We validate UDOS on challenging datasets (MS-COCO, LVIS, ADE20k, UVO, and OpenImages), achieving superior performance over state-of-the-art methods in cross-category and cross-dataset transfer tasks. Our code and models will be publicly available.

关键词： Instance segmentation Degradation computer vision Codes conferences Computational modeling Taxonomy

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A Lightweight Spatiotemporal Network for Online Eye Tracking with Event Camera

A Lightweight Spatiotemporal Network for Online Eye Tracking...

引用

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

作者： Yan Ru Pei Sasskia Brüers Sébastien Crouzet Douglas McLelland Olivier Coenen Brainchip Inc. Laguna Hills CA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Event-based data are commonly encountered in edge computing environments where efficiency and low latency are critical. To interface with such data and leverage their rich temporal features, we propose a causal spatiotemporal convolutional network. This solution targets efficient implementation on edge-appropriate hardware with limited resources in three ways: 1) deliberately targets a simple architecture and set of operations (convolutions, ReLU activations) 2) can be configured to perform online inference efficiently via buffering of layer outputs 3) can achieve more than 90% activation sparsity through regularization during training, enabling very significant efficiency gains on event-based processors. In addition, we propose a general affine augmentation strategy acting directly on the events, which alleviates the problem of dataset scarcity for event-based systems. We apply our model on the AIS 2024 event-based eye tracking challenge, reaching a score of 0.9916 p10 accuracy on the Kaggle private testset.

关键词： Training Program processors Gaze tracking Streaming media Spatiotemporal phenomena pattern recognition Convolutional neural networks

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Demographic Bias Effects on Face Image Synthesis

Demographic Bias Effects on Face Image Synthesis

引用

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

作者： Roberto Leyva Victor Sanchez Gregory Epiphaniou Carsten Maple WMG University of Warwick UK Computer Science University of Warwick UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Face image synthesis has shown remarkable progress in recent years. However, the effect that the demographics of the data used to train synthesizers has on the generation of new face images remains an open question. This paper investigates the effects of the training set demographics in the face image synthesis task. To this end, we propose a strategy that allows synthesizing face images for specific groups of people with a high visual quality. The strategy uses an unsupervised learning approach to discover groups of people in the training set based on Bayesian inference via a probabilistic mixture model. If labels are available to define the groups, our strategy can also exploit such information in lieu of unsupervised learning. Once the groups are defined, our strategy trains a Generative Adversarial Network on each group to generate new face images with specific characteristics. Our results show remarkable performance in terms of image quality compared to several state-of-the-art baselines. More importantly, our strategy allows synthesizing face images with reduced demographic biases.

关键词： Training Image quality Visualization Image synthesis Face recognition Synthesizers Mixture models

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Label Efficient Lifelong Multi-View Broiler Detection

Label Efficient Lifelong Multi-View Broiler Detection

引用

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

作者： Thorsten Cardoen Sam Leroux Pieter Simoens Department of Information and Technology IDLab Ghent University - Imec Ghent Belgium

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Broiler localization is crucial for welfare monitoring, particularly in identifying issues such as wet litter. We focus on multi-camera detection systems since multiple viewpoints not only ensure comprehensive pen coverage but also reduce occlusions caused by lighting, feeder and drinking equipment. Previous multi-view detection studies localize subjects either by aggregating ground plane projections of single-view predictions or by developing end-to-end multi-view detectors capable of directly generating predictions. However, single-view detections may suffer from reduced accuracy due to occlusions, and obtaining ground plane labels for training end-to-end multi-view detectors is challenging. In this paper, we combine the strengths of both approaches by using the readily available aggregated single-view detections as labels for training a multi-view detector. Our approach alleviates the need for hard-to-acquire ground-plane labels. Through experiments on a real-world broiler dataset, we demonstrate the effectiveness of our approach.

关键词： Training Location awareness Accuracy Lighting Detectors pattern recognition Labeling

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Weakly Supervised End2End Deep Visual Odometry

Weakly Supervised End2End Deep Visual Odometry

引用

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

作者： Amin Abouee Ashwanth Ravi Lars Hinneburg Mateusz Dziwulski Florian Ölsner Jürgen Hess Stefan Milz Patrick Mäder Spleenlab GmbH Germany Technical University Ilmenau Ilmenau Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Visual odometry is an ill-posed problem and utilized in many robotics applications, especially automated driving for mapless navigation. Recent applications have shown that deep models outperform traditional approaches especially in localization accuracy and furthermore significantly reduce catastrophic failures. The disadvantage of most of these models is a strong dependence on high-quantity and high-quality ground truth data. However, accurate and dense depth ground truth data for real world datasets is difficult to obtain. As a result, deep models are often trained on synthetic data which introduces a domain gap. We present a weakly supervised approach to overcome this limitation. Our approach uses estimated optical flow for training that can be generated without the need for high-quality dense depth ground truth. Instead, it only requires ground truth poses and raw camera images for training. In the experiments, we show that our approach enables deep visual odometry to be efficiently trained on the target domain (real data) while achieving state-of-the-art performance on the KITTI dataset.

关键词： Training Accuracy Navigation Robot vision systems Cameras Data models pattern recognition

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CSANet: High Speed Channel Spatial Attention Network for Mobile ISP

CSANet: High Speed Channel Spatial Attention Network for Mob...

引用

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

作者： Hsyu, Ming-Chun Liu, Chih-Wei Chen, Chao-Hung Chen, Chao-Wei Tsai, Wen-Chia Ind Technol Res Inst Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9781665448994

The Image Signal Processor (ISP) is a customized device to restore RGB images from the pixel signals of CMOS image sensor. In order to realize this function, a series of processing units are leveraged to tackle different artifacts, such as color shifts, signal noise, moire effects, and so on, that are introduced from the photo-capturing devices. However, tuning each processing unit is highly complicated and requires a lot of experience and effort from image experts. In this paper, a novel network architecture, CSANet, with emphases on inference speed and high PSNR is proposed for end-to-end learned ISP task. The proposed CSANet applies a double attention module employing both channel and spatial attentions. Particularly, its spatial attention is simplified to a light-weighted dilated depth-wise convolution and still performs as well as others. As proof of performance, CSANet won 2nd place in the Mobile AI 2021 Learned Smartphone ISP Challenge with 1st place PSNR score.

关键词： Performance evaluation Runtime Pipelines Network architecture Service-oriented architecture pattern recognition Image restoration

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Avalanche: an End-to-End Library for Continual Learning

Avalanche: an End-to-End Library for Continual Learning

引用

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

作者： Lomonaco, Vincenzo Pellegrini, Lorenzo Cossu, Andrea Carta, Antonio Graffieti, Gabriele Hayes, Tyler L. De Lange, Matthias Masana, Marc Pomponi, Jary Van de Ven, Gido M. Mundt, Martin She, Qi Cooper, Keiland Forest, Jeremy Belouadah, Eden Calderara, Simone Parisi, German, I Cuzzolin, Fabio Tolias, Andreas S. Scardapane, Simone Antiga, Luca Ahmad, Subutai Popescu, Adrian Kanan, Christopher Van de Weijer, Joost Tuytelaars, Tinne Bacciu, Davide Maltoni, Davide Univ Pisa Pisa Italy Univ Bologna Bologna Italy Rochester Inst Technol Rochester NY 14623 USA Katholieke Univ Leuven Leuven Belgium Univ Autonoma Barcelona Barcelona Spain Sapienza Univ Rome Rome Italy Baylor Coll Med Houston TX 77030 USA Goethe Univ Frankfurt Germany ByteDance AI Lab Beijing Peoples R China Univ Calif Berkeley Berkeley CA 94720 USA NYU New York NY USA Univ Paris Saclay Paris France Univ Modena & Reggio Emilia Modena Italy Univ Hamburg Hamburg Germany Oxford Brookes Univ Oxford England Orobix Bergamo Italy Numenta Redwood City CA USA Scuola Normale Super Pisa Pisa Italy

ISBN: (纸本)9781665448994

Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.

关键词： Training Deep learning computer vision Machine learning algorithms conferences Collaboration Libraries

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Focusing on What Matters: Fine-grained Medical Activity recognition for Trauma Resuscitation via Actor Tracking

Focusing on What Matters: Fine-grained Medical Activity Reco...

引用

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

作者： Wenjin Zhang Keyi Li Sen Yang Sifan Yuan Ivan Marsic Genevieve J. Sippel Mary S. Kim Randall S. Burd Rutgers University Waymo Children’s National Hospital

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Trauma is a leading cause of mortality worldwide, with about 20% of these deaths being preventable. Most of these preventable deaths result from errors during the initial resuscitation of injured patients. Decision support has been evaluated as an approach to support teams during this phase to reduce errors. Existing systems require manual data entry and monitoring, which makes tasks challenging to accomplish in a time-critical setting. This paper identified the specific challenges of achieving effective decision support in trauma resuscitation based on computer vision techniques, including complex backgrounds, crowded scenes, fine-grained activities, and a scarcity of labeled data. To address the first three challenges, the proposed system involved an actor tracker that identifies individuals, allowing the system to focus on actor-specific features. Video Masked Autoencoder (Video-MAE) was used to overcome the issue of insufficient labeled data. This approach enables self-supervised learning using unlabeled video content, improving feature representation for medical activities. For more reliable performance, an ensemble fusion method was introduced. This technique combines predictions from consecutive video clips and different actors. Our method outperformed existing approaches in identifying fine-grained activities, providing a solution for activity recognition in trauma resuscitation and similar complex domains.

关键词： computer vision conferences Focusing Self-supervised learning Manuals Activity recognition Time factors

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Recognize Anything: A Strong Image Tagging Model

Recognize Anything: A Strong Image Tagging Model

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

作者： Youcai Zhang Xinyu Huang Jinyu Ma Zhaoyang Li Zhaochuan Luo Yanchun Xie Yuzhuo Qin Tong Luo Yaqian Li Shilong Liu Yandong Guo Lei Zhang OPPO Research Institute International Digital Economy Academy (IDEA) AI2Robotics

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM makes a substantial step for foundation models in computer vision, demonstrating the zero-shot ability to recognize any common category with high accuracy. By leveraging large-scale image-text pairs for training instead of manual annotations, RAM introduces a new paradigm for image *** development of RAM comprises four key steps. Firstly, annotation-free image tags are obtained at scale through automatic text semantic parsing. Subsequently, a preliminary model is trained for automatic annotation by unifying the captioning and tagging tasks, supervised by the original texts and parsed tags, respectively. Thirdly, a data engine is employed to generate additional annotations and clean incorrect ones. Lastly, the model is retrained with the processed data and fine-tuned using a smaller but higher-quality *** evaluate the tagging capability of RAM on numerous benchmarks and observe an impressive zero-shot performance, which significantly outperforms CLIP and BLIP. Remarkably, RAM even surpasses fully supervised models and exhibits a competitive performance compared with the Google tagging API. We have released RAM at https://***/ to foster the advancement of foundation models in computer vision.

关键词： Training computer vision Image recognition Annotations Computational modeling Random access memory Image annotation

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Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks

Faster Than Lies: Real-time Deepfake Detection using Binary ...

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

作者： Romeo Lanzino Federico Fontana Anxhelo Diko Marco Raoul Marini Luigi Cinque Department of Computer Science Sapienza University of Rome Rome Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency. With this in mind, unlike previous work, we introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss. Moreover, our method incorporates Fast Fourier Transform (FFT) and Local Binary pattern (LBP) as additional channel features to uncover manipulation traces in frequency and texture domains. Evaluations on COCOFake, DFFD, and CIFAKE datasets demonstrate our method’s state-of-the-art performance in most scenarios with a significant efficiency gain of up to a 20× reduction in FLOPs during inference. Finally, by exploring BNNs in deepfake detection to balance accuracy and efficiency, this work paves the way for future research on efficient deepfake detection.

关键词： Deepfakes Accuracy Fast Fourier transforms Computational modeling Neural networks Media Real-time systems

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