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Transformer Decoders with MultiModal Regularization for Cross-Modal Food Retrieval

Transformer Decoders with MultiModal Regularization for Cros...

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

作者： Shukor, Mustafa Couairon, Guillaume Grechka, Asya Cord, Matthieu Sorbonne Univ Paris France Meta AI New York NY USA Meero Paris France Valeoai Paris France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Cross-modal image-recipe retrieval has gained significant attention in recent years. Most work focuses on improving cross-modal embeddings using unimodal encoders, that allow for efficient retrieval in large-scale databases, leaving aside cross-attention between modalities which is more computationally expensive. We propose a new retrieval framework, T-Food (Transformer Decoders with MultiModal Regularization for Cross-Modal Food Retrieval) that exploits the interaction between modalities in a novel regularization scheme, while using only unimodal encoders at test time for efficient retrieval. We also capture the intra-dependencies between recipe entities with a dedicated recipe encoder, and propose new variants of triplet losses with dynamic margins that adapt to the difficulty of the task. Finally, we leverage the power of the recent vision and Language Pretraining (VLP) models such as CLIP for the image encoder. Our approach outperforms existing approaches by a large margin on the Recipe1M dataset. Specifically, we achieve absolute improvements of 8.1 % (72.6 R@1) and +10.9 % (44.6 R@1) on the 1k and 10k test sets respectively. The code is available here:https://***/mshukor/TFood.

关键词： computer vision Databases conferences Computational modeling Transfer learning Transformers Decoding

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Is Neuron Coverage Needed to Make Person Detection More Robust?

Is Neuron Coverage Needed to Make Person Detection More Robu...

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

作者： Pavlitskaya, Svetlana Yikmis, Siyar Zoellner, J. Marius FZI Res Ctr Informat Technol D-76131 Karlsruhe Germany

ISBN: (纸本)9781665487399

The growing use of deep neural networks (DNNs) in safety- and security-critical areas like autonomous driving raises the need for their systematic testing. Coverage-guided testing (CGT) is an approach that applies mutation or fuzzing according to a predefined coverage metric to find inputs that cause misbehavior. With the introduction of a neuron coverage metric, CGT has also recently been applied to DNNs. In this work, we apply CGT to the task of person detection in crowded scenes. The proposed pipeline uses YOLOv3 for person detection and includes finding DNN bugs via sampling and mutation, and subsequent DNN retraining on the updated training set. To be a bug, we require a mutated image to cause a significant performance drop compared to a clean input. In accordance with the CGT, we also consider an additional requirement of increased coverage in the bug definition. In order to explore several types of robustness, our approach includes natural image transformations, corruptions, and adversarial examples generated with the Daedalus attack. The proposed framework has uncovered several thousand cases of incorrect DNN behavior. The relative change in mAP performance of the retrained models reached on average between 26.21% and 64.24% for different robustness types. However, we have found no evidence that the investigated coverage metrics can be advantageously used to improve robustness.

关键词： Measurement Deep learning computer vision computer bugs Neurons Neural networks Training data

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Human vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor Scenes

Human Vision Based 3D Point Cloud Semantic Segmentation of L...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Yoo, Sunghwan Jeong, Yeonjeong Jameela, Maryam Sohn, Gunho York University Department of Earth and Space Science and Engineering TorontoONM3J 1P3 Canada

ISBN: (纸本)9798350302493

This paper proposes EyeNet, a novel semantic segmentation network for point clouds that addresses the critical yet often overlooked parameter of coverage area size. Inspired by human peripheral vision, EyeNet overcomes the limitations of conventional networks by introducing a simple but efficient multi-scale input and a parallel processing network with connection blocks between parallel streams. The proposed approach effectively addresses the challenges of dense point clouds, as demonstrated by our ablation studies and state-of-the-art performance on Large-Scale Outdoor datasets. © 2023 ieee.

关键词： Semantic Segmentation

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Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation

Multi-Head Distillation for Continual Unsupervised Domain Ad...

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

作者： Saporta, Antoine Douillard, Arthur Vu, Tuan-Hung Perez, Patrick Cord, Matthieu Sorbonne Univ Paris France valeo ai Paris France Heuritech Paris France

ISBN: (纸本)9781665487399

Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain. Beyond the traditional scope of UDA with a single source domain and a single target domain, real-world perception systems face a variety of scenarios to handle, from varying lighting conditions to many cities around the world. In this context, UDAs with several domains increase the challenges with the addition of distribution shifts within the different target domains. This work focuses on a novel framework for learning UDA, continuous UDA, in which models operate on multiple target domains discovered sequentially, without access to previous target domains. We propose MuHDi, for Multi-Head Distillation, a method that solves the catastrophic forgetting problem, inherent in continual learning tasks. MuHDi performs distillation at multiple levels from the previous model as well as an auxiliary target-specialist segmentation head. We report both extensive ablation and experiments on challenging multi-target UDA semantic segmentation benchmarks to validate the proposed learning scheme and architecture.

关键词： Training Adaptation models Urban areas Semantics Transfer learning computer architecture Benchmark testing

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Guided Deep Metric Learning

Guided Deep Metric Learning

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

作者： Gonzalez-Zapata, Jorge Reyes-Amezcua, Ivan Flores-Araiza, Daniel Mendez-Ruiz, Mauricio Ochoa-Ruiz, Gilberto Mendez-Vazquez, Andres CINVESTAV Unidad Guadalajara Zapopan Mexico Tecnol Monterrey Sch Engn & Sci Monterrey Mexico

ISBN: (纸本)9781665487399

Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning. However, they sometimes lack generalization properties because they are trained often using an inappropriate sample selection strategy or due to the difficulty of the dataset caused by a distributional shift in the data. These represent a significant drawback when attempting to learn the underlying data manifold. Therefore, there is a pressing need to develop better ways of obtaining generalization and representation of the underlying manifold. In this paper, we propose a novel approach to DML that we call Guided Deep Metric Learning, a novel architecture oriented to learning more compact clusters, improving generalization under distributional shifts in DML. This novel architecture consists of two independent models: A multi-branch master model, inspired from a Few-Shot Learning (FSL) perspective, generates a reduced hypothesis space based on prior knowledge from labeled data, which guides or regularizes the decision boundary of a student model during training under an offline knowledge distillation scheme. Experiments have shown that the proposed method is capable of a better manifold generalization and representation to up to 40% improvement (Recall@1, CIFAR10), using guidelines suggested by Musgrave et al. to perform a more fair and realistic comparison, which is currently absent in the literature.

关键词： Measurement Manifolds Training Analytical models Adaptation models Visualization computer architecture

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Analysis of Temporal Tensor Datasets on Product Grassmann Manifold

Analysis of Temporal Tensor Datasets on Product Grassmann Ma...

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

作者： Batalo, Bojan Souza, Lincon S. Gatto, Bernardo B. Sogi, Naoya Fukui, Kazuhiro Univ Tsukuba Tsukuba Ibaraki Japan AIST Tokyo Japan AIST Tsukuba Ibaraki Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Growing abundance of multi-dimensional data creates a need for efficient data exploration and analysis. In this paper, we address this need by tackling the task of tensor dataset visualization and clustering, as tensors are a natural form of multi-dimensional data. Previous work has shown that representing individual tensor modes via respective linear subspaces and unifying them on the product Grassmann manifold (PGM) is an effective and memory-efficient way of representation. However, such representation may lead to loss of valuable temporal information. To address this issue, we model temporal tensor modes with a Hankel-like matrix, preserving sequence information and encoding it with a linear subspace, fully compatible with PGM. Unifying regular tensor modes and Hankel-like representation of regular tensor modes then enriches representation on the PGM, with minimal increase in computational complexity. By relying on geodesic distance on the manifold, we facilitate analysis of multi-dimensional datasets in two ways: 1) by enabling straightforward visualizations using algorithms such as t-SNE;and 2) by fostering clustering of data using distance- or similarity-based methods such as spectral clustering. We evaluate our approach on hand gesture and action recognition datasets as exemplars of temporal tensor datasets.

关键词： Manifolds Measurement Geometry Tensors Data visualization pattern recognition Gyroscopes

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Remote Heart Rate Estimation by Signal Quality Attention Network

Remote Heart Rate Estimation by Signal Quality Attention Net...

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

作者： Gao, Haoyuan Wu, Xiaopei Geng, Jidong Lv, Yang Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Heart rate estimation is very important for heart health monitoring. As a non-invasive optical technology, remote photoplethysmography (rPPG) has the advantages of non-contact, portability and low-price. However, motion and noise artifacts bring additional uncertainty to the results of heart rate estimation. Based on the signal quality assessment method, we propose a new remote heart estimation algorithm by signal quality attention mechanism and long short-term memory (LSTM) networks. The model consists of three parts: firstly, an LSTM network is used to estimate the heart rate sampling point by sampling point;secondly, a similar LSTM network predicts the signal quality;finally, an attention-based model uses the heart rates and quality scores predicted above to calculate the average heart rate of a period of time. The model allocates higher weights to the reliable heart rates estimated from high-quality signals, meanwhile, ignores unreliable results estimated from low-quality signals. Experiments show that LSTM with attention mechanism accurately estimates heart rate from corruption rPPG signal and it performs well on cross-subject tasks and cross-dataset tasks. The results also demonstrate that the scores predicted by the signal quality model is valuable to extract reliable heart rate.

关键词： Heart rate Training Uncertainty computer network reliability Estimation Predictive models Prediction algorithms

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One-Stage Object Referring with Gaze Estimation

One-Stage Object Referring with Gaze Estimation

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

作者： Chen, Jianhang Zhang, Xu Wu, Yue Ghosh, Shalini Natarajan, Pradeep Chang, Shih-Fu Allebach, Jan Purdue Univ W Lafayette IN 47907 USA Amazon Seattle WA 98109 USA Columbia Univ New York NY 10027 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The classic object referring task aims at localizing the referred object in the image and requires a reference image and a natural language description as inputs. Given the facts that gaze signal can be easily obtained by a modern human-computer interaction system with a camera and that human tends to look at the object when referring to it, we propose a novel gaze-assisted object referring framework. The formulation not only simplifies the state-of-the-art gaze-assisted object referring system requiring many input signals besides gaze, but also incorporates the one-stage object detection idea to improve the inference efficiency. More importantly, it implicitly considers all object candidates and thus resolves the main pain point of existing two-stage object referring solutions for proposing an appropriate number of candidates - it cannot be too large, otherwise the computational cost can be prohibitive;it cannot be too small, otherwise the chance of missing a referred object can be significant. To utilize the gaze information, we propose to build a gaze heatmap by using the anchor position encoding map and the gaze prediction result. The gaze heatmap and the language feature are then merged into the feature pyramid in the object detection as the final one-stage referring system. In the CityScapes-OR dataset, the proposed method outperforms the state-of-the-art by 7.8% for Acc@1.

关键词： Heating systems computer vision Computational modeling Natural languages Object detection Feature extraction Encoding

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An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution

An Once-for-All Budgeted Pruning Framework for ConvNets Cons...

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

作者： Sun, Wenyu Cao, Jian Xu, Pengtao Liu, Xiangcheng Zhang, Yuan Wang, Yuan Peking Univ Sch Software & Microelect Beijing Peoples R China Peking Univ Sch Integrated Circuits Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

We propose an efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network architectures close to winner tickets in the early training stage considering the effect of input resolution during the pruning process. In architecture searching stage, we measure the similarity of the pruning mask to get high-quality network architecture with low energy and time consumption. After searching stage, our proposed method randomly sample the compact architectures with different pruning rates and input resolution to achieve joint optimization. Ultimately, we can obtain a cohort of compact networks adaptive to various resolution to meet dynamic FLOPs constraints on different edge devices with only once training. The experiments based on image classification and object detection show that OFARPruning has a higher accuracy than the once-for-all compression methods such as US-Net and MutualNet (1-2% better with less FLOPs), and achieve competitive performance as the conventional pruning methods (72.6% vs. 70.5% on MobileNetv2 under 170 MFLOPs) with much higher efficiency.

关键词： Training Image resolution Adaptive systems Image edge detection Energy resolution Object detection computer architecture

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Dual Attention Poser: Dual Path Body Tracking Based on Attention

Dual Attention Poser: Dual Path Body Tracking Based on Atten...

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2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Di, Xinhan Dai, Xiaokun Zhang, Xinkang Chen, Xinrong Fudan Universiry Academy for Engineering&Technology China Deepearthgo Fudan University Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention China

ISBN: (纸本)9798350302493

Currently, mixed reality head-mounted displays tracking the full body of users is an important human-computer interaction mode through the pose of the head and the hands. Unfortunately, users' virtual representation and experience is limited due to high reconstruction error when simple transformer network architecture is applied. In this paper, we present a novel model, named Dual Attention Poser, which can learn the whole body reconstruction at a high accuracy. The proposed model consists of three key modules. Among them, dual-path attention encoder is designed to extract feature of the sparse signals. Cross attention mixer module enable the fusion of representation in the double path. Attention-gated-mlp decoder is applied to decode the hidden feature from the sparse input through attention gate. Test results on the AMASS dataset show that Dual Attention Poser can reduce the error by up to 18.2% in comparison with the state-of-the-art results. © 2023 ieee.

关键词： Network architecture

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