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Domain Adaptable Normalization for Semi-Supervised Action recognition in the Dark

Domain Adaptable Normalization for Semi-Supervised Action Re...

引用

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

作者： Liang, Zixi Chen, Jiajun Chen, Rui Zheng, Bingbing Zhou, Mingyue Gao, Huaien Lin, Shan Guangzhou Xi Ma Informat Technol Co Guangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Action recognition in the dark is gaining more and more attention with the rapid development of intelligent recognition applications in real-world applications, e.g. self-driving at night and night surveillance. However, limited by the expensive labeling cost, it is impractical to produce a large-scale labeled dataset only for dark environments. Therefore, a practical solution adopted is to transfer models trained from clear environments to dark environments through semi-supervised learning. However, prior works rely heavily on additional efforts such as extra annotations, or extra sensors. To this end, we proposed a novel and simple Domain Adaptable Normalization (DANorm) method to align different domains directly, which consists of feature normalization, angle constraint and the Pseudo-Label. Specifically, the proposed DANorm method enables the model automatically learning the associated features between labeled source domain and unlabeled target domain by constraining the feature subspace vectors. Experimental results show that our model achieves superiority performance on Semi-supervised ARID dataset.

关键词： Adaptation models computer vision Costs Surveillance conferences Semisupervised learning pattern recognition

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Simulated Quantization, Real Power Savings

Simulated Quantization, Real Power Savings

引用

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

作者： van Baalen, Mart Kahne, Brian Mahurin, Eric Kuzmin, Andrey Skliar, Andrii Nagel, Markus Blankevoort, Tijmen Qualcomm AI Res San Diego CA 92121 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Reduced precision hardware-based matrix multiplication accelerators are commonly employed to reduce power consumption of neural network inference. Multiplier designs used in such accelerators possess an interesting property: When the same bit is 0 for two consecutive compute cycles, the multiplier consumes less power. In this paper we show that this effect can be used to reduce power consumption of neural networks by simulating low bit-width quantization on higher bit-width hardware. We show that simulating 4 bit quantization on 8 bit hardware can yield up to 17% relative reduction in power consumption on commonly used networks. Furthermore, we show that in this context, bit operations (BOPs) are a good proxy for power efficiency, and that learning mixed-precision configurations that target lower BOPs can achieve better trade-offs between accuracy and power efficiency.

关键词： computer vision Power demand Quantization (signal) conferences Neural networks Hardware pattern recognition

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Stargazer: A Transformer-based Driver Action Detection System for Intelligent Transportation

Stargazer: A Transformer-based Driver Action Detection Syste...

引用

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

作者： Liang, Junwei Zhu, He Zhang, Enwei Zhang, Jun Tencent Youtu Lab Shenzhen Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Distracted driver actions can be dangerous and cause severe accidents. Thus, it is important to detect and eliminate distracted driving behaviors on the road to save lives. To this end, we study driver action detection using videos captured inside the vehicle. We propose Stargazer, an efficient, transformer-based system exploiting rich temporal features about the human behavioral information, with a simple yet effective action temporal localization framework. The core of our system contains an improved version of the multi-scale vision transformer network, which learns a hierarchy of robust representations. We then use a slidingwindow classification strategy to facilitate temporal localization of actions-of-interest. The proposed system wins the second place in the Naturalistic Driving Action recognition of AI City Challenge 2022 (Track 3)(1). The code and models are released(2).

关键词： Location awareness Roads Urban areas Transformer cores Transformers pattern recognition Behavioral sciences

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Medusa: Universal Feature Learning via Attentional Multitasking

Medusa: Universal Feature Learning via Attentional Multitask...

引用

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

作者： Spencer, Jaime Bowden, Richard Hadfield, Simon Univ Surrey Ctr Vis Speech & Signal Proc CVSSP Guildford Surrey England

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent approaches to multi-task learning (MTL) have focused on modelling connections between tasks at the decoder level. This leads to a tight coupling between tasks, which need retraining if a new task is inserted or removed. We argue that MTL is a stepping stone towards universal feature learning (UFL), which is the ability to learn generic features that can be applied to new tasks without retraining. We propose Medusa to realize this goal, designing task heads with dual attention mechanisms. The shared feature attention masks relevant backbone features for each task, allowing it to learn a generic representation. Meanwhile, a novel Multi-Scale Attention head allows the network to better combine per-task features from different scales when making the final prediction. We show the effectiveness of Medusa in UFL (+13.18% improvement), while maintaining MTL performance and being 25% more efficient than previous approaches.

关键词： Representation learning Couplings computer vision conferences Multitasking pattern recognition Decoding

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Artistic Style Novel View Synthesis Based on A Single Image

Artistic Style Novel View Synthesis Based on A Single Image

引用

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

作者： Tseng, Kuan-Wei Lee, Yao-Chih Chen, Chu-Song Natl Taiwan Univ Taipei Taiwan Tokyo Inst Technol Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent progress in 3D display technologies has raised the demand for stylized 3D digital content. Previous approaches either perform style transfer on stereoscopic image pairs or reconstruct 3D environment with multiple view images. In this paper, we propose a novel view stylization framework that can convert a single 2D image into multiple stylized views. It is a two-stage solution that contains view synthesis and neural style transfer. We estimate dense optical flow between the source and novel views so that the style transfer model can produce consistent results. Experimental results show that our method significantly improves the consistency among views compared to the baseline method.

关键词： computer vision Three-dimensional displays Stereo image processing conferences Computational modeling pattern recognition Optical flow

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Teaching Matters: Investigating the Role of Supervision in vision Transformers

Teaching Matters: Investigating the Role of Supervision in V...

引用

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

作者： Walmer, Matthew Suri, Saksham Gupta, Kamal Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9798350301298

vision Transformers (ViTs) have gained significant popularity in recent years and have proliferated into many applications. However, their behavior under different learning paradigms is not well explored. We compare ViTs trained through different methods of supervision, and show that they learn a diverse range of behaviors in terms of their attention, representations, and downstream performance. We also discover ViT behaviors that are consistent across supervision, including the emergence of Offset Local Attention Heads. These are self-attention heads that attend to a token adjacent to the current token with a fixed directional offset, a phenomenon that to the best of our knowledge has not been highlighted in any prior work. Our analysis shows that ViTs are highly flexible and learn to process local and global information in different orders depending on their training method. We find that contrastive self-supervised methods learn features that are competitive with explicitly supervised features, and they can even be superior for part-level tasks. We also find that the representations of reconstruction-based models show non-trivial similarity to contrastive self-supervised models.

关键词： Explainable computer vision

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Continual Hippocampus Segmentation with Transformers

Continual Hippocampus Segmentation with Transformers

引用

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

作者： Ranem, Amin Gonzalez, Camila Mukhopadhyay, Anirban Tech Univ Darmstadt GRIS Karolinenpl 5 D-64289 Darmstadt Germany

ISBN: (纸本)9781665487399

In clinical settings, where acquisition conditions and patient populations change over time, continual learning is key for ensuring the safe use of deep neural networks. Yet most existing work focuses on convolutional architectures and image classification. Instead, radiologists prefer to work with segmentation models that outline specific regions-of-interest, for which Transformer-based architectures are gaining traction. The self-attention mechanism of Transformers could potentially mitigate catastrophic forgetting, opening the way for more robust medical image segmentation. In this work, we explore how recently-proposed Transformer mechanisms for semantic segmentation behave in sequential learning scenarios, and analyse how best to adapt continual learning strategies for this setting. Our evaluation on hippocampus segmentation shows that Transformer mechanisms mitigate catastrophic forgetting for medical image segmentation compared to purely convolutional architectures, and demonstrates that regularising ViT modules should be done with caution.

关键词： Image segmentation Semantics Sociology computer architecture Artificial neural networks Transformers Task analysis

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Adaptive Bitrate Quantization Scheme Without Codebook for Learned Image Compression

Adaptive Bitrate Quantization Scheme Without Codebook for Le...

引用

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

作者： Loehdefink, Jonas Sitzmann, Jonas Baer, Andreas Fingscheidt, Tim Tech Univ Carolo Wilhelmina Braunschweig Braunschweig Germany

ISBN: (纸本)9781665487399

We propose a generic approach to quantization without codebook in learned image compression called onehot max (OHM, O) quantization. It reorganizes the feature space resulting in an additional dimension, along which vector quantization yields one-hot vectors by comparing activations. Furthermore, we show how to integrate Omega quantization into a compression system with bitrate adaptation, i.e., full control over bitrate during inference. We perform experiments on both MNIST and Kodak and report on rate-distortion trade-offs comparing with the integer rounding reference. For low bitrates (< 0.4 bpp), our proposed quantizer yields better performance while exhibiting also other advantageous training and inference properties. Code is available at https://***/ifnspaml/OHMQ.

关键词： Training computer vision Image coding Codes Vector quantization conferences Bit rate

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A-CAP: Anticipation Captioning with Commonsense Knowledge

A-CAP: Anticipation Captioning with Commonsense Knowledge

引用

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

作者： Duc Minh Vo Quoc-An Luong Sugimoto, Akihiro Nakayama, Hideki Univ Tokyo Tokyo Japan Grad Univ Adv Studies Hayama Kanagawa Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798350301298

Humans possess the capacity to reason about the future based on a sparse collection of visual cues acquired over time. In order to emulate this ability, we introduce a novel task called Anticipation Captioning, which generates a caption for an unseen oracle image using a sparsely temporally-ordered set of images. To tackle this new task, we propose a model called A-CAP, which incorporates commonsense knowledge into a pre-trained vision-language model, allowing it to anticipate the caption. Through both qualitative and quantitative evaluations on a customized visual storytelling dataset, A-CAP outperforms other image captioning methods and establishes a strong baseline for anticipation captioning. We also address the challenges inherent in this task.

关键词： and reasoning language vision

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Residual Feature Pyramid Network for Enhancement of Vascular patterns

Residual Feature Pyramid Network for Enhancement of Vascular...

引用

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

作者： Kotwal, Ketan Marcel, Sebastien Idiap Res Inst Martigny Switzerland Univ Lausanne Lausanne Switzerland

ISBN: (纸本)9781665487399

The accuracy of finger vein recognition systems gets degraded due to low and uneven contrast between veins and surroundings, often resulting in poor detection of vein patterns. We propose a finger-vein enhancement technique, ResFPN (Residual Feature Pyramid Network), as a generic preprocessing method agnostic to the recognition pipeline. A bottom-up pyramidal architecture using the novel Structure Detection block (SDBlock) facilitates extraction of veins of varied widths. Using a feature aggregation module (FAM), we combine these vein-structures, and train the proposed ResFPN for detection of veins across scales. With enhanced presentations, our experiments indicate a reduction upto 5% in the average recognition errors for commonly used recognition pipeline over two publicly available datasets. These improvements are persistent even in cross-dataset scenario where the dataset used to train the ResFPN is different from the one used for recognition.

关键词： Histograms Protocols Veins Pipelines Fingers computer architecture Network architecture

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