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EFE: End-to-end Frame-to-Gaze Estimation

EFE: End-to-end Frame-to-Gaze Estimation

引用

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

作者： Haldun Balim Seonwook Park Xi Wang Xucong Zhang Otmar Hilliges Department of Computer Science ETH Zürich Lunit Inc. Computer Vision Lab Delft University of Technology

Despite the recent development of learning-based gaze estimation methods, most methods require one or more eye or face region crops as inputs and produce a gaze direction vector as output. Cropping results in a higher resolution in the eye regions and having fewer confounding factors (such as clothing and hair) is believed to benefit the final model performance. However, this eye/face patch cropping process is expensive, erroneous, and implementation-specific for different methods. In this paper, we propose a frame-to-gaze network that directly predicts both 3D gaze origin and 3D gaze direction from the raw frame out of the camera without any face or eye cropping. Our method demonstrates that direct gaze regression from the raw downscaled frame, from FHD/HD to VGA/HVGA resolution, is possible despite the challenges of having very few pixels in the eye region. The proposed method achieves comparable results to state-of-the-art methods in Point-of-Gaze (PoG) estimation on three public gaze datasets: GazeCapture, MPIIFaceGaze, and EVE, and generalizes well to extreme camera view changes.

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ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimization

ConPro: Learning Severity Representation for Medical Images ...

引用

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

作者： Hong Nguyen Hoang Nguyen Melinda Chang Hieu Pham Shrikanth Narayanan Michael Pazzani University of Southern California Los Angeles United States Vinuni-Illinois Smart Health Center Hanoi Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Understanding the severity of conditions shown in images in medical diagnosis is crucial, serving as a key guide for clinical assessment, treatment, as well as evaluating longitudinal progression. This paper proposes ConPrO: a novel representation learning method for severity assessment in medical images using Contrastive learning-integrated Preference Optimization. Different from conventional contrastive learning methods that maximize the distance between classes, ConPrO injects into the latent vector the distance preference knowledge between various severity classes and the normal class. We systematically examine the key components of our framework to illuminate how contrastive prediction tasks acquire valuable representations. We show that our representation learning framework offers valuable severity ordering in the feature space while outperforming previous state-of-the-art methods on classification tasks. We achieve a 6% and 20% relative improvement compared to a supervised and a self-supervised baseline, respectively. In addition, we derived discussions on severity indicators and related applications of preference comparison in the medical domain.

关键词： Representation learning Measurement Visualization Contrastive learning Prediction algorithms Vectors pattern recognition

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VisTA-SR: Improving the Accuracy and Resolution of Low-Cost Thermal Imaging Cameras for Agriculture

VisTA-SR: Improving the Accuracy and Resolution of Low-Cost ...

引用

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

作者： Heesup Yun Sassoum Lo Christine H. Diepenbrock Brian N. Bailey J. Mason Earles University of California Davis CA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal cameras are an important tool for agricultural research because they allow for non-invasive measurement of plant temperature, which relates to important photochemical, hydraulic, and agronomic traits. Utilizing low-cost thermal cameras can lower the barrier to introducing thermal imaging in agricultural research and production. This paper presents an approach to improve the temperature accuracy and image quality of low-cost thermal imaging cameras for agricultural applications. Leveraging advancements in computer vision techniques, particularly deep learning networks, we propose a method, called VisTA-SR (Visual & Thermal Alignment and Super-Resolution Enhancement) that combines RGB and thermal images to enhance the capabilities of low-resolution thermal cameras. The research includes calibration and validation of temperature measurements, acquisition of paired image datasets, and the development of a deep learning network tailored for agricultural thermal imaging. Our study addresses the challenges of image enhancement in the agricultural domain and explores the potential of low-cost thermal cameras to replace high-resolution industrial cameras. Experimental results demonstrate the effectiveness of our approach in enhancing temperature accuracy and image sharpness, paving the way for more accessible and efficient thermal imaging solutions in agriculture.

关键词： Temperature measurement Deep learning computer vision Visualization Accuracy Plants (biology) Superresolution

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Dilated Convolutional Transformer for High-Quality Image Deraining

Dilated Convolutional Transformer for High-Quality Image Der...

引用

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

作者： Yufeng Li Jiyang Lu Hongming Chen Xianhao Wu Xiang Chen College of Electronic and Information Engineering Shenyang Aerospace University School of Computer Science and Engineering Nanjing University of Science and Technology

Convolutional neural networks (CNNs) and Transformers have achieved significant success in image signal processing. However, little effort has been made to effectively combine the properties of these two architectures to satisfy image deraining. In this paper, we propose an effective de-raining method, dilated convolutional Transformer (DCT), which can enlarge the receptive fields of the network to aggregate global information. The fundamental building unit of our approach is the dilformer block containing multi-dilconv sparse attention (MDSA) and multi-dilconv feed-forward network (MDFN). The MDSA calculates the multi-scale query to generate accurate similarity map so that rich multi-scale information can be better utilized for the high-quality image reconstruction. In addition, we adopt ReLU to replace the original softmax to enforce sparsity in the Transformer for better feature aggregation. The MDFN is further established to better integrate the rain information of different scales in the feature transformation. Extensive experiments on the benchmarks show the favorable performance against state-of-the-art approaches.

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Quantifying Extrinsic Curvature in Neural Manifolds

Quantifying Extrinsic Curvature in Neural Manifolds

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

作者： Francisco Acosta Sophia Sanborn Khanh Dao Duc Manu Madhav Nina Miolane Physics UC Santa Barbara Electrical and Computer Engineering UC Santa Barbara Mathematics University of British Columbia Biomedical Engineering University of British Columbia

The neural manifold hypothesis postulates that the activity of a neural population forms a low-dimensional manifold whose structure reflects that of the encoded task variables. In this work, we combine topological deep generative models and extrinsic Riemannian geometry to introduce a novel approach for studying the structure of neural manifolds. This approach (i) computes an explicit parameterization of the manifolds and (ii) estimates their local extrinsic curvature—hence quantifying their shape within the neural state space. Importantly, we prove that our methodology is invariant with respect to transformations that do not bear meaningful neuroscience information, such as permutation of the order in which neurons are recorded. We show empirically that we correctly estimate the geometry of synthetic manifolds generated from smooth deformations of circles, spheres, and tori, using realistic noise levels. We additionally validate our methodology on simulated and real neural data, and show that we recover geometric structure known to exist in hippocampal place cells. We expect this approach to open new avenues of inquiry into geometric neural correlates of perception and behavior.

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Ante-Hoc Generation of Task-Agnostic Interpretation Maps

Ante-Hoc Generation of Task-Agnostic Interpretation Maps

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

作者： Akash Guna R T Raul Benitez Sikha O K Department of Computer Science and Engineering Amrita School of Engineering Coimbatore India Departament of Automatic Control Universitat Politècnica de Catalunya-BarcelonaTech Spain

Existing explainability approaches for convolutional neural networks (CNNs) are mainly applied after training (post-hoc) which is generally unreliable. Ante-hoc explainers trained simultaneously with the CNN are more reliable. However, current ante-hoc explanation methods mainly generate inexplicit concept-based explanations tailored to specific tasks. To address these limitations, we propose a task-agnostic ante-hoc framework that can generate interpretation maps to visually explain any CNN. Our framework simultaneously trains the CNN and a weighting network - an explanation generation module. The generated maps are self-explanatory, eliminating the need for manual identification of concepts. We demonstrate that our method can interpret classification, facial landmark detection, and image captioning tasks. We show that our framework is explicit, faithful, and stable through experiments. To the best of our knowledge, this is the first ante-hoc CNN explanation strategy that produces visual explanations generic to CNNs for different tasks.

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ChatVTG: Video Temporal Grounding via Chat with Video Dialogue Large Language Models

ChatVTG: Video Temporal Grounding via Chat with Video Dialog...

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

作者： Mengxue Qu Xiaodong Chen Wu Liu Alicia Li Yao Zhao Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology University of Science and Technology of China Horace Mann School

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Video Temporal Grounding (VTG) aims to ground specific segments within an untrimmed video corresponding to the given natural language query. Existing VTG methods largely depend on supervised learning and extensive annotated data, which is labor-intensive and prone to human biases. To address these challenges, we present ChatVTG, a novel approach that utilizes Video Dialogue Large Language Models (LLMs) for zero-shot video temporal grounding. Our ChatVTG leverages Video Dialogue LLMs to generate multi-granularity segment captions and matches these captions with the given query for coarse temporal grounding, circumventing the need for paired annotation data. Furthermore, to obtain more precise temporal grounding results, we employ moment refinement for fine-grained caption proposals. Extensive experiments on three mainstream VTG datasets, including Charades-STA, ActivityNet-Captions, and TACoS, demonstrate the effectiveness of ChatVTG. Our ChatVTG surpasses the performance of current zero-shot methods.

关键词： computer vision Grounding Annotations Large language models conferences Supervised learning Natural languages

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SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes

SemiGPC: Distribution-Aware Label Refinement for Imbalanced ...

引用

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

作者： Abdelhak Lemkhenter Manchen Wang Luca Zancato Gurumurthy Swaminathan Paolo Favaro Davide Modolo AWS AI Labs

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper we introduce SemiGPC, a distribution-aware label refinement strategy based on Gaussian Processes where the predictions of the model are derived from the labels posterior distribution. Differently from other buffer-based semi-supervised methods such as Co-Match [17] and SimMatch [34], our SemiGPC includes a normalization term that addresses imbalances in the global data distribution while maintaining local sensitivity. This explicit control allows SemiGPC to be more robust to confirmation bias especially under class imbalance. We show that SemiGPC improves performance when paired with different Semi-Supervised methods such as FixMatch [23], ReMixMatch [4], SimMatch [34] and FreeMatch [32] and different pre-training strategies including MSN [2] and Dino [5]. We also show that SemiGPC achieves state of the art results under different degrees of class imbalance on standard CIFAR10-LT/CIFAR100-LT especially in the low data-regime. Using SemiGPC also results in about 2% avg. accuracy increase compared to a new competitive baseline on the more challenging benchmarks SemiAves, SemiCUB, SemiFungi [27] and Semi-iNat [26].

关键词： computer vision Sensitivity Accuracy conferences Gaussian processes Semisupervised learning Predictive models

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Content-Adaptive Downsampling in Convolutional Neural Networks

Content-Adaptive Downsampling in Convolutional Neural Networ...

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

作者： Robin Hesse Simone Schaub-Meyer Stefan Roth Department of Computer Science TU Darmstadt Hessian.AI

Many convolutional neural networks (CNNs) rely on progressive downsampling of their feature maps to increase the network’s receptive field and decrease computational cost. However, this comes at the price of losing granularity in the feature maps, limiting the ability to correctly understand images or recover fine detail in dense prediction tasks. To address this, common practice is to replace the last few downsampling operations in a CNN with dilated convolutions, allowing to retain the feature map resolution without reducing the receptive field, albeit increasing the computational cost. This allows to trade off predictive performance against cost, depending on the output feature resolution. By either regularly downsampling or not downsampling the entire feature map, existing work implicitly treats all regions of the input image and subsequent feature maps as equally important, which generally does not hold. We propose an adaptive downsampling scheme that generalizes the above idea by allowing to process informative regions at a higher resolution than less informative ones. In a variety of experiments, we demonstrate the versatility of our adaptive downsampling strategy and empirically show that it improves the cost-accuracy trade-off of various established CNNs.

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SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data

SynthASpoof: Developing Face Presentation Attack Detection B...

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

作者： Meiling Fang Marco Huber Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Recently, significant progress has been made in face presentation attack detection (PAD), which aims to secure face recognition systems against presentation attacks, owing to the availability of several face PAD datasets. However, all available datasets are based on privacy and legally-sensitive authentic biometric data with a limited number of subjects. To target these legal and technical challenges, this work presents the first synthetic-based face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset. The bona fide samples in SynthASpoof are synthetically generated and the attack samples are collected by presenting such synthetic data to capture systems in a real attack scenario. The experimental results demonstrate the feasibility of using SynthASpoof for the development of face PAD. Moreover, we boost the performance of such a solution by incorporating the domain generalization tool MixStyle into the PAD solutions. Additionally, we showed the viability of using synthetic data as a supplement to enrich the diversity of limited authentic training data and consistently enhance PAD performances. The SynthASpoof dataset, containing 25,000 bona fide and 78,800 attack samples, the implementation, and the pre-trained weights are made publicly available 1 .

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