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ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery

ConceptHash: Interpretable Fine-Grained Hashing via Concept ...

引用

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

作者： Kam Woh Ng Xiatian Zhu Yi-Zhe Song Tao Xiang CVSSP University of Surrey iFlyTek-Surrey Joint Research Centre on Artificial Intelligence Surrey Institute for People-Centred Artificial Intelligence

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing fine-grained hashing methods typically lack code interpretability as they compute hash code bits holistically using both global and local features. To address this limitation, we propose ConceptHash, a novel method that achieves sub-code level interpretability. In ConceptHash, each sub-code corresponds to a human-understandable concept, such as an object part, and these concepts are automatically discovered without human annotations. Specifically, we leverage a vision Transformer architecture and introduce concept tokens as visual prompts, along with image patch tokens as model inputs. Each concept is then mapped to a specific sub-code at the model output, providing natural sub-code interpretability. To capture subtle visual differences among highly similar sub-categories (e.g., bird species), we incorporate language guidance to ensure that the learned hash codes are distinguishable within fine-grained object classes while maintaining semantic alignment. This approach allows us to develop hash codes that exhibit similarity within families of species while remaining distinct from species in other families. Extensive experiments on four fine-grained image retrieval benchmarks demonstrate that ConceptHash outperforms previous methods by a significant margin, offering unique sub-code interpretability as an additional benefit. Code at: https://***/kamwoh/concepthash.

关键词： Visualization computer vision Codes Semantics Image retrieval Manuals Inspection

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Video Interaction recognition using an Attention Augmented Relational Network and Skeleton Data

Video Interaction Recognition using an Attention Augmented R...

引用

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

作者： Farzaneh Askari Cyril Yared Rohit Ramaprasad Devin Garg Anjun Hu James J. Clark University of McGill Montreal QC Canada University of California San Diego San Diego CA United States University of Oxford Oxford United Kingdom

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recognizing interactions in multi-person videos, known as Video Interaction recognition (VIR), is crucial for understanding video content. Often the human skeleton pose (skeleton, for short) is a popular feature for VIR as the main feature, given its success for the task in hand. While many studies have made progress using complex architectures like Graph Neural Networks (GNN) and Transformers to capture interactions in videos, studies such as [33] that apply simple, easy to train, and adaptive architectures such as Relation reasoning Network (RN) [37], yield competitive results. Inspired by this trend, we propose the Attention Augmented Relational Network (AARN), a straightforward yet effective model that uses skeleton data to recognize interactions in videos. AARN outperforms other RN-based models and remains competitive against larger, more intricate models. We evaluate our approach on a challenging real-world Hockey Penalty Dataset (HPD), where the videos depict complex interactions between players in a non-laboratory recording setup, in addition to popular benchmark datasets demonstrating strong performance. Lastly, we show the impact of skeleton quality on the classification accuracy and the struggle of off-the-shelf pose estimators to extract precise skeleton from the challenging HPD dataset.

关键词： Adaptation models computer architecture Transformers Skeleton Graph neural networks Data models Recording

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Camera-based Recovery of Cardiovascular Signals from Unconstrained Face Videos using an Attention Network

Camera-based Recovery of Cardiovascular Signals from Unconst...

引用

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

作者： Yogesh Deshpande Surendrabikram Thapa Abhijit Sarkar A. Lynn Abbott Bradley Department of Electrical and Computer Engineering Virginia Tech USA Department of Computer Science Virginia Tech USA Virginia Tech Transportation Institute USA

This paper addresses the problem of recovering the shape morphology of blood volume pulse (BVP) information from a video of a person’s face. Video-based remote plethysmography methods have shown promising results in estimating vital signs such as heart rate and breathing rate. However, recovering the instantaneous pulse rate signals is still a challenge for the community. This is due to the fact that most of the previous methods concentrate on capturing the temporal average of the cardiovascular signals. In contrast, we present an approach in which BVP signals are extracted with a focus on the recovery of the signal shape morphology as a generalized form for the computation of physiological metrics. We also place emphasis on allowing natural movements by the subject. Furthermore, our system is capable of extracting individual BVP instances with sufficient signal detail to facilitate candidate re-identification. These improvements have resulted in part from the incorporation of a robust skin-detection module into the overall imaging-based photoplethysmography (iPPG) framework. We present extensive experimental results using the challenging UBFC-Phys dataset and the well-known COHFACE dataset. The source code is available at https://***/yogeshd21/CVPM-2023-iPPG-Paper.

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Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting

Parameter Efficient Fine-tuning of Self-supervised ViTs with...

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

作者： Reza Akbarian Bafghi Nidhin Harilal Claire Monteleoni Maziar Raissi University of Colorado Boulder INRIA Paris University of California Riverside

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Artificial neural networks often suffer from catastrophic forgetting, where learning new concepts leads to a complete loss of previously acquired knowledge. We observe that this issue is particularly magnified in vision transformers (ViTs), where post-pre-training and fine-tuning on new tasks can significantly degrade the model’s original general abilities. For instance, a DINO ViT-Base/16 pre-trained on ImageNet-1k loses over 70% accuracy on ImageNet-1k after just 10 iterations of fine-tuning on CIFAR-100. Overcoming this stability-plasticity dilemma is crucial for enabling ViTs to continuously learn and adapt to new domains while preserving their initial knowledge. In this work, we study two new parameter-efficient fine-tuning strategies: (1) Block Expansion, and (2) Low-rank adaptation (LoRA). Our experiments reveal that using either Block Expansion or LoRA on self-supervised pre-trained ViTs surpass fully fine-tuned ViTs in new domains while offering significantly greater parameter efficiency. Notably, we find that Block Expansion experiences only a minimal performance drop in the pre-training domain, thereby effectively mitigating catastrophic forgetting in pre-trained ViTs 1 .

关键词： Knowledge engineering computer vision Adaptation models conferences Learning (artificial intelligence) Artificial neural networks Transformers

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Proceedings - 2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

Proceedings - 2022 IEEE/CVF Conference on Computer Vision an...

引用

2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

ISBN: (纸本)9781665469463

The proceedings contain 2072 papers. The topics discussed include: clipped hyperbolic classifiers are super-hyperbolic classifiers;efficient deep embedded subspace clustering;noise is also useful: negative correlation-steered latent contrastive learning;active learning for open-set annotation;understanding and increasing efficiency of Frank-Wolfe adversarial training;robust optimization as data augmentation for large-scale graphs;a re-balancing strategy for class-imbalanced classification based on instance difficulty;the devil is in the margin: margin-based label smoothing for network calibration;towards better plasticity-stability trade-off in incremental learning: a simple linear connector;learning Bayesian sparse networks with full experience replay for continual learning;a variational Bayesian method for similarity learning in non-rigid image registration;learning to learn by jointly optimizing neural architecture and weights;learning to prompt for continual learning;multi-frame self-supervised depth with transformers;and rethinking Bayesian deep learning methods for semi-supervised volumetric medical image segmentation.

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OpenStory: A Large-Scale Open-Domain Dataset for Subject-Driven Visual Storytelling

OpenStory: A Large-Scale Open-Domain Dataset for Subject-Dri...

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

作者： Zilyu Ye Jinxiu Liu JinJin Cao Zhiyang Chen Ziwei Xuan Mingyuan Zhou Qi Liu Guo-Jun Qi School of Future Technology South China University of Technology Westlake University Foundation Model Research Center CASIA OPPO US Research Center

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, the advancement and evolution of generative AI have been highly compelling. In this paper, we present OpenStory, a large-scale dataset tailored for training subject-focused story visualization models to generate coherent and contextually relevant visual narratives. Addressing the challenges of maintaining subject continuity across frames and capturing compelling narratives, We propose an innovative pipeline that automates the extraction of keyframes from open-domain videos. It ingeniously employs vision-language models to generate descriptive captions, which are then refined by a large language model to ensure narrative flow and coherence. Furthermore, advanced subject masking techniques are applied to isolate and segment the primary subjects. Derived from diverse video sources, including YouTube and existing datasets, OpenStory offers a comprehensive open-domain resource, surpassing prior datasets confined to specific scenarios. With automated captioning instead of manual annotation, high-resolution imagery optimized for subject count per frame, and extensive frame sequences ensuring consistent subjects for temporal modeling, OpenStory establishes itself as an invaluable benchmark. It facilitates advancements in subject-focused story visualization, enabling the training of models capable of comprehending and generating intricate multi-modal narratives from extensive visual and textual inputs.

关键词： Training Visualization computer vision Annotations Large language models Pipelines Manuals

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Masked Autoencoders are Secretly Efficient Learners

Masked Autoencoders are Secretly Efficient Learners

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

作者： Zihao Wei Chen Wei Jieru Mei Yutong Bai Zeyu Wang Xianhang Li Hongru Zhu Huiyu Wang Alan Yuille Yuyin Zhou Cihang Xie University of Michigan Ann Arbor Johns Hopkins University UC Santa Cruz Meta

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper provides an efficiency study of training Masked Autoencoders (MAE), a framework introduced by He et al. [13] for pre-training vision Transformers (ViTs). Our results surprisingly reveal that MAE can learn at a faster speed and with fewer training samples while maintaining high performance. To accelerate its training, our changes are simple and straightforward: in the pre-training stage, we aggressively increase the masking ratio, decrease the number of training epochs, and reduce the decoder depth to lower the pre-training cost; in the fine-tuning stage, we demonstrate that layer-wise learning rate decay plays a vital role in unlocking the full potential of pre-trained models. Under this setup, we further verify the sample efficiency of MAE: training MAE is hardly affected even when using only 20% of the original training *** combining these strategies, we are able to accelerate MAE pre-training by a factor of 82 or more, with little performance drop. For example, we are able to pre-train a ViT-B in ~9 hours using a single NVIDIA A100 GPU and achieve 82.9% top-1 accuracy on the downstream ImageNet classification task. Additionally, we also verify the speed acceleration on another MAE extension, SupMAE.

关键词： Training computer vision Costs conferences Computational modeling Graphics processing units Transformers

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Shadow Removal via Global Residual Free Unet and Shadow Generation

Shadow Removal via Global Residual Free Unet and Shadow Gene...

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

作者： Dong Li Xin Lu Yurui Zhu Xi Wang Jie Xiao Yunpeng Zhang Xueyang Fu Zheng-Jun Zha University of Science and Technology of China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Existing shadow removal methods face challenges when confronted with real-world scenes, particularly when dealing with complex background information and high-resolution images. To address these issues, we present a shadow removal framework based on shadow generation and Global Residual Free Unet (GRFUnet), which improves shadow removal from both data and network perspectives. For data enhancement, we train a generative adversarial network for producing shadow masks, which are then multiplied with clean images to obtain new shadow images. The additional shadow data produced by this structured generation approach allows for ample constraining of the network in color aspects, thereby enhancing color consistency in the images. In terms of network architecture, we design the Global Residual Free Unet that employs a convolutional framework to sequentially conduct Spatial Interaction and Channel Evolution for effective feature extraction. Moreover, we eliminate the commonly used Global residual connections in image restoration, as we discern their in-effectiveness for the non-additive task of shadow removal. Through this methodology, we achieve excellent shadow removal results, both qualitatively and quantitatively. Our method achieves the highest PSNR in the NTIRE24-Image Shadow Removal Challenge and achieves commendable and balanced outcomes across two tracks—placing third in the fidelity track and fourth in the perceptual track.

关键词： computer vision Image color analysis conferences computer architecture Network architecture Feature extraction Generative adversarial networks

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Run-time Monitoring of 3D Object Detection in Automated Driving Systems Using Early Layer Neural Activation patterns

Run-time Monitoring of 3D Object Detection in Automated Driv...

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

作者： Hakan Yekta Yatbaz Mehrdad Dianati Konstantinos Koufos Roger Woodman WMG University of Warwick School of Electronics Electrical Engineering and Computer Science (EEECS) Queen's University of Belfast

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring the integrity of object detection for errors within the perception module of automated driving systems (ADS) is paramount for ensuring safety. Despite recent advancements in deep neural network (DNN)-based object detectors, their susceptibility to detection errors, particularly in the less-explored realm of 3D object detection, remains a significant concern. State-of-the-art integrity monitoring (also known as introspection) mechanisms in 2D object detection mainly utilise the activation patterns in the final layer of the DNN-based detector’s backbone. However, that may not sufficiently address the complexities and sparsity of data in 3D object detection. To this end, we conduct, in this article, an extensive investigation into the effects of activation patterns extracted from various layers of the backbone network for introspecting the operation of 3D object detectors. Through a comparative analysis using Kitti and NuScenes datasets with PointPillars and CenterPoint detectors, we demonstrate that using earlier layers’ activation patterns enhances the error detection performance of the integrity monitoring system, yet increases computational complexity. To address the real-time operation requirements in ADS, we also introduce a novel introspection method that combines activation patterns from multiple layers of the detector’s backbone and report its performance.

关键词： computer vision Three-dimensional displays conferences Object detection Detectors Real-time systems Safety

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Event Camera Demosaicing via Swin Transformer and Pixel-focus Loss

Event Camera Demosaicing via Swin Transformer and Pixel-focu...

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

作者： Yunfan Lu Yijie Xu Wenzong Ma Weiyu Guo Hui Xiong AI Thrust Hong Kong University of Science and Technology (Guangzhou)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent research has highlighted improvements in high-quality imaging guided by event cameras, with most of these efforts concentrating on the RGB domain. However, these advancements frequently neglect the unique challenges introduced by the inherent flaws in the sensor design of event cameras in the RAW domain. Specifically, this sensor design results in the partial loss of pixel values, posing new challenges for RAW domain processes like demosaicing. The challenge intensifies as most research in the RAW domain is based on the premise that each pixel contains a value, making the straightforward adaptation of these methods to event camera demosaicing problematic. To end this, we present a Swin-Transformer-based backbone and a pixel-focus loss function for demosaicing with missing pixel values in RAW domain processing. Our core motivation is to refine a general and widely applicable foundational model from the RGB domain for RAW domain processing, thereby broadening the model’s applicability within the entire imaging process. Our method harnesses multi-scale processing and space-to-depth techniques to ensure efficiency and reduce computing complexity. We also proposed the Pixel-focus Loss function for network fine-tuning to improve network convergence based on our discovery of a long-tailed distribution in training loss. Our method has undergone validation on the MIPI Demosaic Challenge dataset, with subsequent analytical experimentation confirming its efficacy. All code and trained models are released here: https://***/yunfanLu/ev-demosaic.

关键词： Training computer vision Computational modeling conferences computer architecture Cameras Transformers

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