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VLM-PL: Advanced Pseudo Labeling approach for Class Incremental Object Detection via vision-Language Model

VLM-PL: Advanced Pseudo Labeling approach for Class Incremen...

引用

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

作者： Junsu Kim Yunhoe Ku Jihyeon Kim Junuk Cha Seungryul Baek UNIST MODULABS

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of Class Incremental Object Detection (CIOD), creating models that can continuously learn like humans is a major challenge. Pseudo-labeling methods, although initially powerful, struggle with multi-scenario incremental learning due to their tendency to forget past knowledge. To overcome this, we introduce a new approach called vision-Language Model assisted Pseudo-Labeling (VLM-PL). This technique uses vision-Language Model (VLM) to verify the correctness of pseudo ground-truths (GTs) without requiring additional model training. VLM-PL starts by deriving pseudo GTs from a pre-trained detector. Then, we generate custom queries for each pseudo GT using carefully designed prompt templates that combine image and text features. This allows the VLM to classify the correctness through its responses. Furthermore, VLM-PL integrates refined pseudo and real GTs from upcoming training, effectively combining new and old knowledge. Extensive experiments conducted on the Pascal VOC and MS COCO datasets not only highlight VLM-PL’s exceptional performance in multi-scenario but also illuminate its effectiveness in dual-scenario by achieving state-of-the-art results in both.

关键词： Training computer vision Incremental learning conferences Computational modeling Object detection Detectors

来源：评论

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Feedback control of event cameras

Feedback control of event cameras

引用

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

作者： Delbruck, Tobi Graca, Rui Paluch, Marcin UZH ETH Zurich Inst Neuroinformat Zurich Switzerland

ISBN: (纸本)9781665448994

Dynamic vision sensor event cameras produce a variable data rate stream of brightness change events. Event production at the pixel level is controlled by threshold, bandwidth, and refractory period bias current parameter settings. Biases must be adjusted to match application requirements and the optimal settings depend on many factors. As a first step towards automatic control of biases, this paper proposes fixed-step feedback controllers that use measurements of event rate and noise. The controllers regulate the event rate within an acceptable range using threshold and refractory period control, and regulate noise using bandwidth control. Experiments demonstrate model validity and feedback control.

关键词： computer vision Current measurement conferences Bandwidth Production vision sensors Cameras

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Large-Scale Bidirectional Training for Zero-Shot Image Captioning

Large-Scale Bidirectional Training for Zero-Shot Image Capti...

引用

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

作者： Taehoon Kim Mark Marsden Pyunghwan Ahn Sangyun Kim Sihaeng Lee Alessandra Sala Seung Hwan Kim LG AI Research Shutterstock

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

When trained on large-scale datasets, image captioning models can understand the content of images from a general domain but often fail to generate accurate, detailed captions. To improve performance, pretraining-and-finetuning has been a key strategy for image captioning. However, we find that large-scale bidirectional training between image and text enables zero-shot image captioning. In this paper, we introduce Bidirectional Image Text Training in largER Scale, BITTERS, an efficient training and inference framework for zero-shot image captioning. We also propose a new evaluation benchmark which comprises of high quality datasets and an extensive set of metrics to properly evaluate zero-shot captioning accuracy and societal bias. We additionally provide an efficient finetuning approach for keyword extraction. We show that careful selection of large-scale training set and model architecture is the key to achieving zero-shot image captioning.

关键词： Training Measurement Accuracy conferences computer architecture Feature extraction Data models

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Automatic Play Segmentation of Hockey Videos

Automatic Play Segmentation of Hockey Videos

引用

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

作者： Pidaparthy, Hemanth Dowling, Michael H. Elder, James H. York Univ N York ON Canada Queens Univ Kingston ON Canada

ISBN: (纸本)9781665448994

Most team sports such as hockey involve periods of active play interleaved with breaks in play. When watching a game remotely, many fans would prefer an abbreviated game showing only periods of active play. Here we address the problem of identifying these periods in order to produce a time-compressed viewing experience. Our approach is based on a hidden Markov model of play state driven by deep visual and optional auditory cues. We find that our deep visual cues generalize well across different cameras and that auditory cues can improve performance but only if unsupervised methods are used to adapt emission distributions to domain shift across games. Our system achieves temporal compression rates of 20-50% at a recall of 96%.

关键词： Visualization Fans computer vision conferences Hidden Markov models Games Cameras

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vision + Language Applications: A Survey

Vision + Language Applications: A Survey

引用

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

作者： Yutong Zhou Nobutaka Shimada Ritsumeikan University Shiga Japan

Text-to-image generation has attracted significant interest from researchers and practitioners in recent years due to its widespread and diverse applications across various industries. Despite the progress made in the domain of vision and language research, the existing literature remains relatively limited, particularly with regard to advancements and applications in this field. This paper explores a relevant research track within multimodal applications, including text, vision, audio, and others. In addition to the studies discussed in this paper, we are also committed to continually updating the latest relevant papers, datasets, application projects and corresponding information at https://***/Yutong-Zhou-cv/Awesome-Text-to-Image.

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Learnable Global Spatio-Temporal Adaptive Aggregation for Bracketing Image Restoration and Enhancement

Learnable Global Spatio-Temporal Adaptive Aggregation for Br...

引用

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

作者： Xinwei Dai Yuanbo Zhou Xintao Qiu Hui Tang Wei Deng Qingquan Gao Tong Tong Fuzhou University Imperial Vision Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Employing specific networks to address different types of degradation often proved to be complex and time-consuming in practical applications. The Bracket Image Restoration and Enhancement (BIRE) aimed to address various image restoration tasks in a unified manner by restoring clear single-frame images from multiple-frame shots, including denoising, deblurring, enhancing high dynamic range (HDR), and achieving super-resolution under various degradation conditions. In this paper, we propose LGSTANet, an efficient aggregation restoration network for BIRE. Specifically, inspired by video restoration methods, we adopt an efficient architecture comprising alignment, aggregation, and reconstruction. Additionally, we introduce a Learnable Global Spatio-Temporal Adaptive (LGSTA) aggregation module to effectively aggregate inter-frame complementary information. Furthermore, we propose an adaptive restoration modulator to address specific degradation disturbances of various types, thereby achieving high-quality restoration outcomes. Extensive experiments demonstrate the effectiveness of our method. LGSTANet outperforms other state-of-the-art methods in Bracket Image Restoration and Enhancement and achieves competitive results in the NTIRE2024 BIRE challenge.

关键词： Degradation Aggregates Perturbation methods Superresolution Noise reduction Modulation computer architecture

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Low-Rank Few-Shot Adaptation of vision-Language Models

Low-Rank Few-Shot Adaptation of Vision-Language Models

引用

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

作者： Maxime Zanella Ismail Ben Ayed UCLouvain UMons ÉTS Montréal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent progress in the few-shot adaptation of visionLanguage Models (VLMs) has further pushed their generalization capabilities, at the expense of just a few labeled samples within the target downstream task. However, this promising, already quite abundant few-shot literature has focused principally on prompt learning and, to a lesser extent, on adapters, overlooking the recent advances in Parameter-Efficient Fine-Tuning (PEFT). Furthermore, existing few-shot learning methods for VLMs often rely on heavy training procedures and/or carefully chosen, taskspecific hyper-parameters, which might impede their applicability. In response, we introduce Low-Rank Adaptation (LoRA) in few-shot learning for VLMs, and show its potential on 11 datasets, in comparison to current state-of-the-art prompt- and adapter-based approaches. Surprisingly, our simple CLIP-LoRA method exhibits substantial improvements, while reducing the training times and keeping the same hyper-parameters in all the target tasks, i.e., across all the datasets and numbers of shots. Certainly, our surprising results do not dismiss the potential of promptlearning and adapter-based research. However, we believe that our strong baseline could be used to evaluate progress in these emergent subjects in few-shot VLMs.

关键词： Training Adaptation models computer vision Design methodology conferences pattern recognition Few shot learning

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DELTA: Decoupling Long-Tailed Online Continual Learning

DELTA: Decoupling Long-Tailed Online Continual Learning

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

作者： Siddeshwar Raghavan Jiangpeng He Fengqing Zhu School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

A significant challenge in achieving ubiquitous Artificial Intelligence is the limited ability of models to rapidly learn new information in real-world scenarios where data follows long-tailed distributions, all while avoiding forgetting previously acquired knowledge. In this work, we study the under-explored problem of Long-Tailed Online Continual Learning (LTOCL), which aims to learn new tasks from sequentially arriving class-imbalanced data streams. Each data is observed only once for training without knowing the task data distribution. We present DELTA, a decoupled learning approach designed to enhance learning representations and address the substantial imbalance in LTOCL. We enhance the learning process by adapting supervised contrastive learning to attract similar samples and repel dissimilar (out-of-class) samples. Further, by balancing gradients during training using an equalization loss, DELTA significantly enhances learning outcomes and successfully mitigates catastrophic forgetting. Through extensive evaluation, we demonstrate that DELTA improves the capacity for incremental learning, surpassing existing OCL methods. Our results suggest considerable promise for applying OCL in real-world applications. Code is available online 1

关键词： Continuing education Training computer vision Incremental learning conferences Contrastive learning Data models

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Affine-based Deformable Attention and Selective Fusion for Semi-dense Matching

Affine-based Deformable Attention and Selective Fusion for S...

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

作者： Hongkai Chen Zixin Luo Yurun Tian Xuyang Bai Ziyu Wang Lei Zhou Mingmin Zhen Tian Fang David McKinnon Yanghai Tsin Long Quan Apple Inc Hong Kong University of Science and Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Identifying robust and accurate correspondences across images is a fundamental problem in computer vision that enables various downstream tasks. Recent semi-dense matching methods emphasize the effectiveness of fusing relevant cross-view information through Transformer. In this paper, we propose several improvements upon this paradigm. Firstly, we introduce affine-based local attention to model cross-view deformations. Secondly, we present selective fusion to merge local and global messages from cross attention. Apart from network structure, we also identify the importance of enforcing spatial smoothness in loss design, which has been omitted by previous works. Based on these augmentations, our network demonstrate strong matching capacity under different settings. The full version of our network achieves state-of-the-art performance among semi-dense matching methods at a similar cost to LoFTR, while the slim version reaches LoFTR baseline’s performance with only 15% computation cost and 18% parameters.

关键词： Geometry Deformable models computer vision Costs Deformation Shape Fuses

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Federated Hyperparameter Optimization through Reward-Based Strategies: Challenges and Insights

Federated Hyperparameter Optimization through Reward-Based S...

引用

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

作者： Krishna Kanth Nakka Ahmed Frikha Ricardo Mendis Xue Jiang Xuebing Zhou Huawei Munich Research Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Performing hyperparameter tuning in federated learning is often prohibitively expensive due to the substantial communication overhead associated with training a single configuration, especially with a large hyperparameter search space. To overcome this challenge, recent works explored reward-based approaches to learn a policy distribution over a set of hyperparameter configurations. These approaches enable the concurrent exploration of multiple hyperparameter configurations within a single communication round, thereby accelerating the search *** this paper, we take a deeper look at the reward-based strategies and systematically analyze them, uncovering several issues and challenges associated with their adoption in practice. Furthermore, motivated by the insights from our analysis, we propose an in-depth evaluation of policy distribution with metrics that capture rankings of standalone configurations. We contribute this critical examination and proposed evaluation metrics in order to raise awareness about the challenges and hidden issues that reward-based federated hyperparameter optimization might face and to enable a more rigorous evaluation and therefore a faster progress in this research area. We expect that the identified challenges will serve as inspiration for the development of more robust and hyperparameter-free federated hyperparameter tuning approaches.

关键词： Training computer vision Systematics Sensitivity Federated learning Face recognition conferences

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