pwc-archive/papers-with-abstracts huggingface dataset:
Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning
Authors: Jingqi Li, Jiaqi Gao, Yuzhen Zhang, Hongming Shan, Junping Zhang
Date: 2022-10-21 00:00:00
Tasks: Gait Recognition

As a unique biometric that can be perceived at a distance, gait has broad applications in person authentication, social security, and so on. Existing gait recognition methods suffer from changes in viewpoint and clothing and barely consider extracting diverse motion features, a fundamental characteristic in gaits, from gait sequences. This paper proposes a novel motion modeling method to extract the discriminative and robust representation. Specifically, we first extract the motion features from the encoded motion sequences in the shallow layer. Then we continuously enhance the motion feature in deep layers. This motion modeling approach is independent of mainstream work in building network architectures. As a result, one can apply this motion modeling method to any backbone to improve gait recognition performance. In this paper, we combine motion modeling with one commonly used backbone~(GaitGL) as GaitGL-M to illustrate motion modeling. Extensive experimental results on two commonly-used cross-view gait datasets demonstrate the superior performance of GaitGL-M over existing state-of-the-art methods.
Self-Supervised Pretraining on Satellite Imagery: a Case Study on Label-Efficient Vehicle Detection
Authors: Jules BOURCIER, Thomas Floquet, Gohar Dashyan, Tugdual Ceillier, Karteek Alahari, Jocelyn Chanussot
Date: 2022-10-21 00:00:00
Tasks: object-detection, Object Detection, Representation Learning, Self-Supervised Learning, vehicle detection

In defense-related remote sensing applications, such as vehicle detection on satellite imagery, supervised learning requires a huge number of labeled examples to reach operational performances. Such data are challenging to obtain as it requires military experts, and some observables are intrinsically rare. This limited labeling capability, as well as the large number of unlabeled images available due to the growing number of sensors, make object detection on remote sensing imagery highly relevant for self-supervised learning. We study in-domain self-supervised representation learning for object detection on very high resolution optical satellite imagery, that is yet poorly explored. For the first time to our knowledge, we study the problem of label efficiency on this task. We use the large land use classification dataset Functional Map of the World to pretrain representations with an extension of the Momentum Contrast framework. We then investigate this model's transferability on a real-world task of fine-grained vehicle detection and classification on Preligens proprietary data, which is designed to be representative of an operational use case of strategic site surveillance. We show that our in-domain self-supervised learning model is competitive with ImageNet pretraining, and outperforms it in the low-label regime.
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks
Authors: Moshe Eliasof, Nir Ben Zikri, Eran Treister
Date: 2022-10-21 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Superpixels, Unsupervised Image Segmentation, Unsupervised Semantic Segmentation

Unsupervised image segmentation is an important task in many real-world scenarios where labelled data is of scarce availability. In this paper we propose a novel approach that harnesses recent advances in unsupervised learning using a combination of Mutual Information Maximization (MIM), Neural Superpixel Segmentation and Graph Neural Networks (GNNs) in an end-to-end manner, an approach that has not been explored yet. We take advantage of the compact representation of superpixels and combine it with GNNs in order to learn strong and semantically meaningful representations of images. Specifically, we show that our GNN based approach allows to model interactions between distant pixels in the image and serves as a strong prior to existing CNNs for an improved accuracy. Our experiments reveal both the qualitative and quantitative advantages of our approach compared to current state-of-the-art methods over four popular datasets.
PoseScript: Linking 3D Human Poses and Natural Language
Authors: Ginger Delmas, Philippe Weinzaepfel, Thomas Lucas, Francesc Moreno-Noguer, Grégory Rogez
Date: 2022-10-21 00:00:00
Tasks: Cross-Modal Retrieval, Image Captioning, Question Answering, Retrieval, Visual Question Answering, Visual Question Answering (VQA)

Natural language plays a critical role in many computer vision applications, such as image captioning, visual question answering, and cross-modal retrieval, to provide fine-grained semantic information. Unfortunately, while human pose is key to human understanding, current 3D human pose datasets lack detailed language descriptions. To address this issue, we have introduced the PoseScript dataset. This dataset pairs more than six thousand 3D human poses from AMASS with rich human-annotated descriptions of the body parts and their spatial relationships. Additionally, to increase the size of the dataset to a scale that is compatible with data-hungry learning algorithms, we have proposed an elaborate captioning process that generates automatic synthetic descriptions in natural language from given 3D keypoints. This process extracts low-level pose information, known as "posecodes", using a set of simple but generic rules on the 3D keypoints. These posecodes are then combined into higher level textual descriptions using syntactic rules. With automatic annotations, the amount of available data significantly scales up (100k), making it possible to effectively pretrain deep models for finetuning on human captions. To showcase the potential of annotated poses, we present three multi-modal learning tasks that utilize the PoseScript dataset. Firstly, we develop a pipeline that maps 3D poses and textual descriptions into a joint embedding space, allowing for cross-modal retrieval of relevant poses from large-scale datasets. Secondly, we establish a baseline for a text-conditioned model generating 3D poses. Thirdly, we present a learned process for generating pose descriptions. These applications demonstrate the versatility and usefulness of annotated poses in various tasks and pave the way for future research in the field.
Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data
Authors: Kazuto Nakashima, Yumi Iwashita, Ryo Kurazume
Date: 2022-10-21 00:00:00
Tasks: Diversity, LIDAR Semantic Segmentation, Semantic Segmentation

3D LiDAR sensors are indispensable for the robust vision of autonomous mobile robots. However, deploying LiDAR-based perception algorithms often fails due to a domain gap from the training environment, such as inconsistent angular resolution and missing properties. Existing studies have tackled the issue by learning inter-domain mapping, while the transferability is constrained by the training configuration and the training is susceptible to peculiar lossy noises called ray-drop. To address the issue, this paper proposes a generative model of LiDAR range images applicable to the data-level domain transfer. Motivated by the fact that LiDAR measurement is based on point-by-point range imaging, we train an implicit image representation-based generative adversarial networks along with a differentiable ray-drop effect. We demonstrate the fidelity and diversity of our model in comparison with the point-based and image-based state-of-the-art generative models. We also showcase upsampling and restoration applications. Furthermore, we introduce a Sim2Real application for LiDAR semantic segmentation. We demonstrate that our method is effective as a realistic ray-drop simulator and outperforms state-of-the-art methods.
Distilling the Undistillable: Learning from a Nasty Teacher
Authors: Surgan Jandial, Yash Khasbage, Arghya Pal, Vineeth N Balasubramanian, Balaji Krishnamurthy
Date: 2022-10-21 00:00:00
Tasks: Knowledge Distillation

The inadvertent stealing of private/sensitive information using Knowledge Distillation (KD) has been getting significant attention recently and has guided subsequent defense efforts considering its critical nature. Recent work Nasty Teacher proposed to develop teachers which can not be distilled or imitated by models attacking it. However, the promise of confidentiality offered by a nasty teacher is not well studied, and as a further step to strengthen against such loopholes, we attempt to bypass its defense and steal (or extract) information in its presence successfully. Specifically, we analyze Nasty Teacher from two different directions and subsequently leverage them carefully to develop simple yet efficient methodologies, named as HTC and SCM, which increase the learning from Nasty Teacher by upto 68.63% on standard datasets. Additionally, we also explore an improvised defense method based on our insights of stealing. Our detailed set of experiments and ablations on diverse models/settings demonstrate the efficacy of our approach.
AROS: Affordance Recognition with One-Shot Human Stances
Authors: Abel Pacheco-Ortega, Walterio Mayol-Cuevas
Date: 2022-10-21 00:00:00
Tasks: Affordance Recognition, One-Shot Learning

We present AROS, a one-shot learning approach that uses an explicit representation of interactions between highly-articulated human poses and 3D scenes. The approach is one-shot as the method does not require re-training to add new affordance instances. Furthermore, only one or a small handful of examples of the target pose are needed to describe the interaction. Given a 3D mesh of a previously unseen scene, we can predict affordance locations that support the interactions and generate corresponding articulated 3D human bodies around them. We evaluate on three public datasets of scans of real environments with varied degrees of noise. Via rigorous statistical analysis of crowdsourced evaluations, results show that our one-shot approach outperforms data-intensive baselines by up to 80\%.
CRT-6D: Fast 6D Object Pose Estimation with Cascaded Refinement Transformers
Authors: Pedro Castro, Tae-Kyun Kim
Date: 2022-10-21 00:00:00
Tasks: 6D Pose Estimation using RGB, Object, Pose Estimation

Learning based 6D object pose estimation methods rely on computing large intermediate pose representations and/or iteratively refining an initial estimation with a slow render-compare pipeline. This paper introduces a novel method we call Cascaded Pose Refinement Transformers, or CRT-6D. We replace the commonly used dense intermediate representation with a sparse set of features sampled from the feature pyramid we call OSKFs(Object Surface Keypoint Features) where each element corresponds to an object keypoint. We employ lightweight deformable transformers and chain them together to iteratively refine proposed poses over the sampled OSKFs. We achieve inference runtimes 2x faster than the closest real-time state of the art methods while supporting up to 21 objects on a single model. We demonstrate the effectiveness of CRT-6D by performing extensive experiments on the LM-O and YCBV datasets. Compared to real-time methods, we achieve state of the art on LM-O and YCB-V, falling slightly behind methods with inference runtimes one order of magnitude higher. The source code is available at: https://github.com/PedroCastro/CRT-6D
A Survey of Dataset Refinement for Problems in Computer Vision Datasets
Authors: Zhijing Wan, Zhixiang Wang, CheukTing Chung, Zheng Wang
Date: 2022-10-21 00:00:00
Tasks: Active Learning

Large-scale datasets have played a crucial role in the advancement of computer vision. However, they often suffer from problems such as class imbalance, noisy labels, dataset bias, or high resource costs, which can inhibit model performance and reduce trustworthiness. With the advocacy of data-centric research, various data-centric solutions have been proposed to solve the dataset problems mentioned above. They improve the quality of datasets by re-organizing them, which we call dataset refinement. In this survey, we provide a comprehensive and structured overview of recent advances in dataset refinement for problematic computer vision datasets. Firstly, we summarize and analyze the various problems encountered in large-scale computer vision datasets. Then, we classify the dataset refinement algorithms into three categories based on the refinement process: data sampling, data subset selection, and active learning. In addition, we organize these dataset refinement methods according to the addressed data problems and provide a systematic comparative description. We point out that these three types of dataset refinement have distinct advantages and disadvantages for dataset problems, which informs the choice of the data-centric method appropriate to a particular research objective. Finally, we summarize the current literature and propose potential future research topics.
Video Summarization Overview
Authors: Mayu Otani, Yale Song, Yang Wang
Date: 2022-10-21 00:00:00
Tasks: Video Summarization

With the broad growth of video capturing devices and applications on the web, it is more demanding to provide desired video content for users efficiently. Video summarization facilitates quickly grasping video content by creating a compact summary of videos. Much effort has been devoted to automatic video summarization, and various problem settings and approaches have been proposed. Our goal is to provide an overview of this field. This survey covers early studies as well as recent approaches which take advantage of deep learning techniques. We describe video summarization approaches and their underlying concepts. We also discuss benchmarks and evaluations. We overview how prior work addressed evaluation and detail the pros and cons of the evaluation protocols. Last but not least, we discuss open challenges in this field.
RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control
Authors: Zhenggang Tang, Balakumar Sundaralingam, Jonathan Tremblay, Bowen Wen, Ye Yuan, Stephen Tyree, Charles Loop, Alexander Schwing, Stan Birchfield
Date: 2022-10-21 00:00:00
Tasks: 3D geometry, Model Predictive Control, NeRF

We present a system for collision-free control of a robot manipulator that uses only RGB views of the world. Perceptual input of a tabletop scene is provided by multiple images of an RGB camera (without depth) that is either handheld or mounted on the robot end effector. A NeRF-like process is used to reconstruct the 3D geometry of the scene, from which the Euclidean full signed distance function (ESDF) is computed. A model predictive control algorithm is then used to control the manipulator to reach a desired pose while avoiding obstacles in the ESDF. We show results on a real dataset collected and annotated in our lab.
Doctors Handwritten Prescription Recognition System In Multi Language Using Deep Learning
Authors: Pavithiran G, Sharan Padmanabhan, Nuvvuru Divya, Aswathy V, Irene Jerusha P, Chandar B
Date: 2022-10-21 00:00:00

Doctors typically write in incomprehensible handwriting, making it difficult for both the general public and some pharmacists to understand the medications they have prescribed. It is not ideal for them to write the prescription quietly and methodically because they will be dealing with dozens of patients every day and will be swamped with work.As a result, their handwriting is illegible. This may result in reports or prescriptions consisting of short forms and cursive writing that a typical person or pharmacist won't be able to read properly, which will cause prescribed medications to be misspelled. However, some individuals are accustomed to writing prescriptions in regional languages because we all live in an area with a diversity of regional languages. It makes analyzing the content much more challenging. So, in this project, we'll use a recognition system to build a tool that can translate the handwriting of physicians in any language. This system will be made into an application which is fully autonomous in functioning. As the user uploads the prescription image the program will pre-process the image by performing image pre-processing, and word segmentations initially before processing the image for training. And it will be done for every language we require the model to detect. And as of the deduction model will be made using deep learning techniques including CNN, RNN, and LSTM, which are utilized to train the model. To match words from various languages that will be written in the system, Unicode will be used. Furthermore, fuzzy search and market basket analysis are employed to offer an end result that will be optimized from the pharmaceutical database and displayed to the user as a structured output.
InfraRed Investigation in Singapore (IRIS) Observatory: Urban heat island contributors and mitigators analysis using neighborhood-scale thermal imaging
Authors: Miguel Martin, Vasantha Ramani, Clayton Miller
Date: 2022-10-21 00:00:00

This paper studies heat fluxes from contributors and mitigators of urban heat islands using thermal images and weather data. Thermal images were collected from an observatory operating on the rooftop of a building between November 2021 and April 2022. Over the same period, an automatic weather station network was used to measure weather conditions at several locations on a university campus in Singapore. From data collected by the observatory and the automatic weather station network, a method was developed to estimate the heat emitted by building facades, vegetation, and traffic. Before performing the analysis of urban heat fluxes, it was observed that the surface temperature collected from the observatory is sensitive to some variables. After the sensitivity analysis, thermal images were calibrated against measurements of the surface temperature in an outdoor environment. Finally, several contributors and mitigators of urban heat islands were analyzed from heat fluxes assessed with thermal images and weather data. According to thermal images collected by the rooftop observatory, concrete walls are an important contributor to urban heat islands due to the longwave radiation they emit at night. Vegetation, on the other hand, seems to be an effective mitigator because of latent heat fluxes generated by evapotranspiration. Traffic looks to be a negligible source of heat if considered over a small portion of a road. In the future, more efforts can be made to estimate the magnitude of the heat released by an air-conditioning system from thermal images.
Slippage-robust Gaze Tracking for Near-eye Display
Authors: Wei zhang, Jiaxi Cao, Xiang Wang, Enqi Tian, Bin Li
Date: 2022-10-20 00:00:00

In recent years, head-mounted near-eye display devices have become the key hardware foundation for virtual reality and augmented reality. Thus head-mounted gaze tracking technology has received attention as an essential part of human-computer interaction. However, unavoidable slippage of head-mounted devices (HMD) often results higher gaze tracking errors and hinders the practical usage of HMD. To tackle this problem, we propose a slippage-robust gaze tracking for near-eye display method based on the aspheric eyeball model and accurately compute the eyeball optical axis and rotation center. We tested several methods on datasets with slippage and the experimental results show that the proposed method significantly outperforms the previous method (almost double the suboptimal method).
Photo-realistic 360 Head Avatars in the Wild
Authors: Stanislaw Szymanowicz, Virginia Estellers, Tadas Baltrusaitis, Matthew Johnson
Date: 2022-10-20 00:00:00

Delivering immersive, 3D experiences for human communication requires a method to obtain 360 degree photo-realistic avatars of humans. To make these experiences accessible to all, only commodity hardware, like mobile phone cameras, should be necessary to capture the data needed for avatar creation. For avatars to be rendered realistically from any viewpoint, we require training images and camera poses from all angles. However, we cannot rely on there being trackable features in the foreground or background of all images for use in estimating poses, especially from the side or back of the head. To overcome this, we propose a novel landmark detector trained on synthetic data to estimate camera poses from 360 degree mobile phone videos of a human head for use in a multi-stage optimization process which creates a photo-realistic avatar. We perform validation experiments with synthetic data and showcase our method on 360 degree avatars trained from mobile phone videos.
Deep Learning for Diagonal Earlobe Crease Detection
Authors: Sara L. Almonacid-Uribe, Oliverio J. Santana, Daniel Hernández-Sosa, David Freire-Obregón
Date: 2022-10-20 00:00:00
Tasks: Deep Learning

An article published on Medical News Today in June 2022 presented a fundamental question in its title: Can an earlobe crease predict heart attacks? The author explained that end arteries supply the heart and ears. In other words, if they lose blood supply, no other arteries can take over, resulting in tissue damage. Consequently, some earlobes have a diagonal crease, line, or deep fold that resembles a wrinkle. In this paper, we take a step toward detecting this specific marker, commonly known as DELC or Frank's Sign. For this reason, we have made the first DELC dataset available to the public. In addition, we have investigated the performance of numerous cutting-edge backbones on annotated photos. Experimentally, we demonstrate that it is possible to solve this challenge by combining pre-trained encoders with a customized classifier to achieve 97.7% accuracy. Moreover, we have analyzed the backbone trade-off between performance and size, estimating MobileNet as the most promising encoder.
Rethinking Learning Approaches for Long-Term Action Anticipation
Authors: Megha Nawhal, Akash Abdu Jyothi, Greg Mori
Date: 2022-10-20 00:00:00
Tasks: Action Anticipation, Future prediction, Long Term Action Anticipation

Action anticipation involves predicting future actions having observed the initial portion of a video. Typically, the observed video is processed as a whole to obtain a video-level representation of the ongoing activity in the video, which is then used for future prediction. We introduce ANTICIPATR which performs long-term action anticipation leveraging segment-level representations learned using individual segments from different activities, in addition to a video-level representation. We propose a two-stage learning approach to train a novel transformer-based model that uses these two types of representations to directly predict a set of future action instances over any given anticipation duration. Results on Breakfast, 50Salads, Epic-Kitchens-55, and EGTEA Gaze+ datasets demonstrate the effectiveness of our approach.