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On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders

On Disentanglement and Mutual Information in Semi-Supervised...

引用

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

作者： Gordon Rodriguez, Elliott Columbia Univ Dept Stat New York NY 10027 USA

ISBN: (纸本)9781665448994

In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.

关键词： computer vision conferences pattern recognition Mutual information

来源：评论

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VMCML: Video and Music Matching via Cross-Modality Lifting

VMCML: Video and Music Matching via Cross-Modality Lifting

引用

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

作者： Lee, Yi-Shan Tseng, Wei-Cheng Wang, Fu-En Sun, Min Natl Tsing Hua Univ Hsinchu Taiwan Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9798350365474

We propose a content-based system for matching video and background music. The system aims to address the challenges in music recommendation for new users or new music give short-form videos. To this end, we propose a cross-modal framework VMCML (Video and Music Matching via Cross-Modality Lifting) that finds a shared embedding space between video and music representations. To ensure the embedding space can be effectively shared by both representations, we leverage CosFace loss based on margin-based cosine similarity loss. Furthermore, to confirm the music is not the original sound of the video and that more than one video is matched to the same music, we follow the rule and collect videos and music from a well-known multi-media platform. That is because there are limitations of previous datasets. We establish a large-scale dataset called MSV, which provide 390 individual music and the corresponding matched 150,000 videos. We conduct extensive experiments on Youtube-8M and our MSV datasets. Our quantitative and qualitative results demonstrate the effectiveness of our proposed framework and achieve state-of-the-art video and music matching performance.

关键词： computer music

来源：评论

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Multi-Modal Hit Detection and Positional Analysis in Padel Competitions

Multi-Modal Hit Detection and Positional Analysis in Padel C...

引用

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

作者： Decorte, Robbe Pare, Martin Vanhaeverbeke, Jelle Taelman, Joachim Slembrouck, Maarten Verstockt, Steven Univ Ghent imec IDLab Ghent Belgium

ISBN: (纸本)9798350365474

Padel is a rapidly growing racquet sport and has gained popularity globally due to its accessibility and exciting gameplay dynamics. Effective coordination between teammates hinges on maintaining an appropriate distance, allowing for seamless transitions between offensive and defensive maneuvers. A balanced inter-player distance and distance to the net not only facilitates efficient communication but also enhances the team's ability to exploit openings in the opponent's defense while minimizing vulnerabilities. We introduce a new open dataset of padel rallies with annotations for hits and player-ball interactions, a predictive model for detecting hits based on audio signals, a reidentification algorithm for pose tracking, and a framework for calculating inter-player and player-net distances during rallies. Our predictive model achieves an average F1-score of 92% for hit detection, demonstrating robust performance across different match conditions. Furthermore, we develop a system for accurately assigning hits to individual players, achieving an overall accuracy of 83.70% for player-specific assignment and 86.83% for team-based assignment.

关键词： computer vision Deep Learning Player Tracking Sound Event Detection Sports Data Science

来源：评论

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Rethinking the Self-Attention in vision Transformers

Rethinking the Self-Attention in Vision Transformers

引用

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

作者： Kim, Kyungmin Wu, Bichen Dai, Xiaoliang Zhang, Peizhao Yan, Zhicheng Vajda, Peter Kim, Seon Yonsei Univ Seoul South Korea Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665448994

Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on image (ImageNet-1K) and video (Kinetics-400) understanding show we can achieve 95% sparsity on the self-attention maps while maintaining the performance drop to be less than 2 points. This motivates us to rethink the role of self-attention in vision transformer models.

关键词： computer vision conferences Computational modeling pattern recognition

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ZInD-Tell: Towards Translating Indoor Panoramas into Descriptions

ZInD-Tell: Towards Translating Indoor Panoramas into Descrip...

引用

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

作者： Deb, Tonmoay Wang, Lichen Bessinger, Zachary Khosravan, Naji Penner, Eric Kang, Sing Bing Northwestern Univ Evanston IL 60208 USA Zillow Grp Seattle WA USA

ISBN: (纸本)9798350365474

This paper focuses on bridging the gap between natural language descriptions, 360 degrees panoramas, room shapes, and layouts/floorplans of indoor spaces. To enable new multimodal (image, geometry, language) research directions in indoor environment understanding, we propose a novel extension to the Zillow Indoor Dataset (ZInD) which we call ZInD-Tell1. We first introduce an effective technique for extracting geometric information from ZInD's raw structural data, which facilitates the generation of accurate ground truth descriptions using GPT-4. A human-in-the-loop approach is then employed to ensure the quality of these descriptions. To demonstrate the vast potential of our dataset, we introduce the ZInD-Tell benchmark, focusing on two exemplary tasks: language-based home retrieval and indoor description generation. Furthermore, we propose an end-to-end, zero-shot baseline model, ZInD-Agent, designed to process an unordered set of panorama images and generate home descriptions. ZInD-Agent outperforms naive methods in both tasks, hence, can be considered as a complement to the naive to show potential use of the data and impact of geometry. We believe this work initiates new trajectories in leveraging computer vision techniques to analyze indoor panorama images descriptively by learning the latent relation between vision, geometry, and language modalities.

关键词： computer vision

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How Much You Ate? Food Portion Estimation on Spoons

How Much You Ate? Food Portion Estimation on Spoons

引用

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

作者： Sharma, Aaryam Czarnecki, Chris Chen, Yuhao Xi, Pengcheng Xu, Linlin Wong, Alexander Univ Waterloo Vis & Image Proc Lab Waterloo ON Canada Natl Res Council Canada Ottawa ON Canada

ISBN: (纸本)9798350365474

Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring through the use of images and depth cameras. However, the current state-of-the-art image-based food portion estimation algorithms assume that users take images of their meals one or two times, which can be inconvenient and fail to capture food items that are not visible from a top-down perspective, such as ingredients submerged in a stew. To address these limitations, we introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils, not requiring any change of camera perspective after installation. The shallow depth of utensils provides a more favorable angle for capturing food items, and tracking them on the utensil's surface offers a significantly more accurate estimation of dietary intake without the need for post-meal image capture. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews. Through a series of experiments, we demonstrate the exceptional potential of our method as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool.

关键词： computer-vision estimation food nutrition volumetric

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GRAFIQS: Face Image Quality Assessment Using Gradient Magnitudes

GRAFIQS: Face Image Quality Assessment Using Gradient Magnit...

引用

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

作者： Kolf, Jan Niklas Damer, Naser Boutros, Fadi Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Darmstadt Germany

ISBN: (纸本)9798350365474

Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset. To achieve that, we propose quantifying the discrepancy in Batch Normalization statistics (BNS), including mean and variance, between those recorded during FR training and those obtained by processing testing samples through the pretrained FR model. We then generate gradient magnitudes of pretrained FR weights by backpropagating the BNS through the pretrained model. The cumulative absolute sum of these gradient magnitudes serves as the FIQ for our approach. Through comprehensive experimentation, we demonstrate the effectiveness of our training-free and quality labeling-free approach, achieving competitive performance to recent state-of-the-art FIQA approaches without relying on quality labeling, the need to train regression networks, specialized architectures, or designing and optimizing specific loss functions.

关键词： Biometrics computer vision Face Image Quality Assessment Face recognition

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A Dual-Mode Approach for vision-Based Navigation in a Lunar Landing Scenario

A Dual-Mode Approach for Vision-Based Navigation in a Lunar ...

引用

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

作者： Ostrogovich, Luca Del Prete, Roberto Tomasicchio, Giuseppe Longepe, Nicolas Renga, Alfredo Univ Naples Federico II Dept Ind Engn Naples Italy Telespazio SRL Rome Italy ESA ESRIN Lab Frascati Italy

ISBN: (纸本)9798350365474

In this research, a novel approach for autonomous spacecraft navigation, particularly in lunar contexts, is presented, focusing on vision-based techniques. The system incorporates lunar crater recognition in conjunction with feature tracking to enhance the accuracy of spacecraft navigation. This system underwent comprehensive evaluation through a purpose-built software simulation, replicating lunar conditions for thorough evaluation and refinement. The methodology integrates established navigational methods with advanced artificial intelligence algorithms, resulting in significant navigational accuracy. The system demonstrates precise capabilities in determining the spacecraft position, with an average accuracy of approximately 270 m for the absolute navigation mode, while the relative mode exhibited an average error of 27.4 m and 0.8 m in determining the horizontal and vertical lander displacements relative to terrain. Initial tests on embedded systems-akin to those on-board spacecraft-were conducted. These tests are pivotal in demonstrating the system's operational viability within the constraints of limited bandwidth and rapid processing requirements characteristic of space missions. The promising results from these tests suggest potential applicability in real-world space missions, enhancing autonomous navigation capabilities in lunar and potentially other extraterrestrial environments.

关键词： Lunar Navigation Onboard AI Visual Based Navigation

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AIGeN: An Adversarial Approach for Instruction Generation in VLN

AIGeN: An Adversarial Approach for Instruction Generation in...

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

作者： Rawal, Niyati Bigazzi, Roberto Baraldi, Lorenzo Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy

ISBN: (纸本)9798350365474

In the last few years, the research interest in vision-and-Language Navigation (VLN) has grown significantly. VLN is a challenging task that involves an agent following human instructions and navigating in a previously unknown environment to reach a specified goal. Recent work in literature focuses on different ways to augment the available datasets of instructions for improving navigation performance by exploiting synthetic training data. In this work, we propose AIGeN, a novel architecture inspired by Generative Adversarial Networks (GANs) that produces meaningful and well-formed synthetic instructions to improve navigation agents' performance. The model is composed of a Transformer decoder (GPT-2) and a Transformer encoder (BERT). During the training phase, the decoder generates sentences for a sequence of images describing the agent's path to a particular point while the encoder discriminates between real and fake instructions. Experimentally, we evaluate the quality of the generated instructions and perform extensive ablation studies. Additionally, we generate synthetic instructions for 217K trajectories using AIGeN on Habitat-Matterport 3D Dataset (HM3D) and show an improvement in the performance of an off-the-shelf VLN method. The validation analysis of our proposal is conducted on REVERIE and R2R and highlights the promising aspects of our proposal, achieving state-of-the-art performance.

关键词： Generative Adversarial Networks Text Generation vision-and-Language Navigation

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

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

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

作者： Zanella, Maxime Ben Ayed, Ismail UCLouvain Louvain Belgium UMons Mons Belgium ETS Montreal Montreal PQ Canada

ISBN: (纸本)9798350365474

Recent progress in the few-shot adaptation of vision-Language 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-ofthe-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 prompt-learning 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： adapter few-shot LoRA PEFT prompt vision-Language

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