pwc-archive/papers-with-abstracts huggingface dataset:
Multi-queue Momentum Contrast for Microvideo-Product Retrieval
Authors: Yali Du, Yinwei Wei, Wei Ji, Fan Liu, Xin Luo, Liqiang Nie
Date: 2022-12-22 00:00:00
Tasks: Representation Learning, Retrieval

The booming development and huge market of micro-videos bring new e-commerce channels for merchants. Currently, more micro-video publishers prefer to embed relevant ads into their micro-videos, which not only provides them with business income but helps the audiences to discover their interesting products. However, due to the micro-video recording by unprofessional equipment, involving various topics and including multiple modalities, it is challenging to locate the products related to micro-videos efficiently, appropriately, and accurately. We formulate the microvideo-product retrieval task, which is the first attempt to explore the retrieval between the multi-modal and multi-modal instances. A novel approach named Multi-Queue Momentum Contrast (MQMC) network is proposed for bidirectional retrieval, consisting of the uni-modal feature and multi-modal instance representation learning. Moreover, a discriminative selection strategy with a multi-queue is used to distinguish the importance of different negatives based on their categories. We collect two large-scale microvideo-product datasets (MVS and MVS-large) for evaluation and manually construct the hierarchical category ontology, which covers sundry products in daily life. Extensive experiments show that MQMC outperforms the state-of-the-art baselines. Our replication package (including code, dataset, etc.) is publicly available at https://github.com/duyali2000/MQMC.
IPProtect: protecting the intellectual property of visual datasets during data valuation
Authors: Gursimran Singh, Chendi Wang, Ahnaf Tazwar, Lanjun Wang, Yong Zhang
Date: 2022-12-22 00:00:00
Tasks: Data Valuation

Data trading is essential to accelerate the development of data-driven machine learning pipelines. The central problem in data trading is to estimate the utility of a seller's dataset with respect to a given buyer's machine learning task, also known as data valuation. Typically, data valuation requires one or more participants to share their raw dataset with others, leading to potential risks of intellectual property (IP) violations. In this paper, we tackle the novel task of preemptively protecting the IP of datasets that need to be shared during data valuation. First, we identify and formalize two kinds of novel IP risks in visual datasets: data-item (image) IP and statistical (dataset) IP. Then, we propose a novel algorithm to convert the raw dataset into a sanitized version, that provides resistance to IP violations, while at the same time allowing accurate data valuation. The key idea is to limit the transfer of information from the raw dataset to the sanitized dataset, thereby protecting against potential intellectual property violations. Next, we analyze our method for the likely existence of a solution and immunity against reconstruction attacks. Finally, we conduct extensive experiments on three computer vision datasets demonstrating the advantages of our method in comparison to other baselines.
Vision-Based Environmental Perception for Autonomous Driving
Authors: Fei Liu, Zihao Lu, Xianke Lin
Date: 2022-12-22 00:00:00
Tasks: Autonomous Driving, Depth Estimation, Object, object-detection, Object Detection

Visual perception plays an important role in autonomous driving. One of the primary tasks is object detection and identification. Since the vision sensor is rich in color and texture information, it can quickly and accurately identify various road information. The commonly used technique is based on extracting and calculating various features of the image. The recent development of deep learning-based method has better reliability and processing speed and has a greater advantage in recognizing complex elements. For depth estimation, vision sensor is also used for ranging due to their small size and low cost. Monocular camera uses image data from a single viewpoint as input to estimate object depth. In contrast, stereo vision is based on parallax and matching feature points of different views, and the application of deep learning also further improves the accuracy. In addition, Simultaneous Location and Mapping (SLAM) can establish a model of the road environment, thus helping the vehicle perceive the surrounding environment and complete the tasks. In this paper, we introduce and compare various methods of object detection and identification, then explain the development of depth estimation and compare various methods based on monocular, stereo, and RDBG sensors, next review and compare various methods of SLAM, and finally summarize the current problems and present the future development trends of vision technologies.
Offline Clustering Approach to Self-supervised Learning for Class-imbalanced Image Data
Authors: Hye-min Chang, Sungkyun Chang
Date: 2022-12-22 00:00:00
Tasks: Clustering, Self-Supervised Learning

Class-imbalanced datasets are known to cause the problem of model being biased towards the majority classes. In this project, we set up two research questions: 1) when is the class-imbalance problem more prevalent in self-supervised pre-training? and 2) can offline clustering of feature representations help pre-training on class-imbalanced data? Our experiments investigate the former question by adjusting the degree of {\it class-imbalance} when training the baseline models, namely SimCLR and SimSiam on CIFAR-10 database. To answer the latter question, we train each expert model on each subset of the feature clusters. We then distill the knowledge of expert models into a single model, so that we will be able to compare the performance of this model to our baselines.
Novel Deep Learning Framework For Bovine Iris Segmentation
Authors: Heemoon Yoon, Mira Park, Sang-Hee Lee
Date: 2022-12-22 00:00:00
Tasks: Decoder, Deep Learning, Iris Segmentation, Segmentation

Iris segmentation is the initial step to identify biometric of animals to establish a traceability system of livestock. In this study, we propose a novel deep learning framework for pixel-wise segmentation with minimum use of annotation labels using BovineAAEyes80 public dataset. In the experiment, U-Net with VGG16 backbone was selected as the best combination of encoder and decoder model, demonstrating a 99.50% accuracy and a 98.35% Dice coefficient score. Remarkably, the selected model accurately segmented corrupted images even without proper annotation data. This study contributes to the advancement of the iris segmentation and the development of a reliable DNNs training framework.
Cattle Detection Occlusion Problem
Authors: Aparna Mendu, Bhavya Sehgal, Vaishnavi Mendu
Date: 2022-12-21 00:00:00
Tasks: Face Detection, Management, object-detection, Object Detection

The management of cattle over a huge area is still a challenging problem in the farming sector. With evolution in technology, Unmanned aerial vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative to manual animal censuses for livestock estimation since they are less risky and expensive.This paper evaluated and compared the cutting-edge object detection algorithms, YOLOv7,RetinaNet with ResNet50 backbone, RetinaNet with EfficientNet and mask RCNN. It aims to improve the occlusion problem that is to detect hidden cattle from a huge dataset captured by drones using deep learning algorithms for accurate cattle detection. Experimental results showed YOLOv7 was superior with precision of 0.612 when compared to the other two algorithms. The proposed method proved superior to the usual competing algorithms for cow face detection, especially in very difficult cases.
DExT: Detector Explanation Toolkit
Authors: Deepan Chakravarthi Padmanabhan, Paul G. Plöger, Octavio Arriaga, Matias Valdenegro-Toro
Date: 2022-12-21 00:00:00
Tasks: Object

State-of-the-art object detectors are treated as black boxes due to their highly non-linear internal computations. Even with unprecedented advancements in detector performance, the inability to explain how their outputs are generated limits their use in safety-critical applications. Previous work fails to produce explanations for both bounding box and classification decisions, and generally make individual explanations for various detectors. In this paper, we propose an open-source Detector Explanation Toolkit (DExT) which implements the proposed approach to generate a holistic explanation for all detector decisions using certain gradient-based explanation methods. We suggests various multi-object visualization methods to merge the explanations of multiple objects detected in an image as well as the corresponding detections in a single image. The quantitative evaluation show that the Single Shot MultiBox Detector (SSD) is more faithfully explained compared to other detectors regardless of the explanation methods. Both quantitative and human-centric evaluations identify that SmoothGrad with Guided Backpropagation (GBP) provides more trustworthy explanations among selected methods across all detectors. We expect that DExT will motivate practitioners to evaluate object detectors from the interpretability perspective by explaining both bounding box and classification decisions.
ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement
Authors: Wei-Ning Hsu, Tal Remez, Bowen Shi, Jacob Donley, Yossi Adi
Date: 2022-12-21 00:00:00
Tasks: Audio-Visual Speech Recognition, Resynthesis, Speech Enhancement, speech-recognition, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Video Synchronization, Visual Speech Recognition

Prior works on improving speech quality with visual input typically study each type of auditory distortion separately (e.g., separation, inpainting, video-to-speech) and present tailored algorithms. This paper proposes to unify these subjects and study Generalized Speech Enhancement, where the goal is not to reconstruct the exact reference clean signal, but to focus on improving certain aspects of speech. In particular, this paper concerns intelligibility, quality, and video synchronization. We cast the problem as audio-visual speech resynthesis, which is composed of two steps: pseudo audio-visual speech recognition (P-AVSR) and pseudo text-to-speech synthesis (P-TTS). P-AVSR and P-TTS are connected by discrete units derived from a self-supervised speech model. Moreover, we utilize self-supervised audio-visual speech model to initialize P-AVSR. The proposed model is coined ReVISE. ReVISE is the first high-quality model for in-the-wild video-to-speech synthesis and achieves superior performance on all LRS3 audio-visual enhancement tasks with a single model. To demonstrates its applicability in the real world, ReVISE is also evaluated on EasyCom, an audio-visual benchmark collected under challenging acoustic conditions with only 1.6 hours of training data. Similarly, ReVISE greatly suppresses noise and improves quality. Project page: https://wnhsu.github.io/ReVISE.
Artistic Arbitrary Style Transfer
Authors: Weiting Li, Rahul Vyas, Ramya Sree Penta
Date: 2022-12-21 00:00:00
Tasks: Style Transfer

Arbitrary Style Transfer is a technique used to produce a new image from two images: a content image, and a style image. The newly produced image is unseen and is generated from the algorithm itself. Balancing the structure and style components has been the major challenge that other state-of-the-art algorithms have tried to solve. Despite all the efforts, it's still a major challenge to apply the artistic style that was originally created on top of the structure of the content image while maintaining consistency. In this work, we solved these problems by using a Deep Learning approach using Convolutional Neural Networks. Our implementation will first extract foreground from the background using the pre-trained Detectron 2 model from the content image, and then apply the Arbitrary Style Transfer technique that is used in SANet. Once we have the two styled images, we will stitch the two chunks of images after the process of style transfer for the complete end piece.
Semi-supervised Bladder Tissue Classification in Multi-Domain Endoscopic Images
Authors: Jorge F. Lazo, Benoit Rosa, Michele Catellani, Matteo Fontana, Francesco A. Mistretta, Gennaro Musi, Ottavio De Cobelli, Michel de Mathelin, Elena De Momi
Date: 2022-12-21 00:00:00
Tasks: Classification, Generative Adversarial Network, Image-to-Image Translation, Lesion Detection

Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i.e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semi-surprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network. To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0.90, 0.88, and 0.89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0.92, 0.64, and 0.94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based bladder tissue classification when annotations are limited in multi-domain data. The dataset is available at https://zenodo.org/record/7741476#.ZBQUK7TMJ6k
MM811 Project Report: Cloud Detection and Removal in Satellite Images
Authors: Dale Chen-Song, Erfan Khalaji, Vaishali Rani
Date: 2022-12-21 00:00:00
Tasks: Cloud Detection, Cloud Removal

For satellite images, the presence of clouds presents a problem as clouds obscure more than half to two-thirds of the ground information. This problem causes many issues for reliability in a noise-free environment to communicate data and other applications that need seamless monitoring. Removing the clouds from the images while keeping the background pixels intact can help address the mentioned issues. Recently, deep learning methods have become popular for researching cloud removal by demonstrating promising results, among which Generative Adversarial Networks (GAN) have shown considerably better performance. In this project, we aim to address cloud removal from satellite images using AttentionGAN and then compare our results by reproducing the results obtained using traditional GANs and auto-encoders. We use RICE dataset. The outcome of this project can be used to develop applications that require cloud-free satellite images. Moreover, our results could be helpful for making further research improvements.
Lightweight Monocular Depth Estimation
Authors: Ruilin Ma, Shiyao Chen, Qin Zhang
Date: 2022-12-21 00:00:00
Tasks: Depth Estimation, Image Segmentation, Monocular Depth Estimation, Self-Driving Cars, Semantic Segmentation

Monocular depth estimation can play an important role in addressing the issue of deriving scene geometry from 2D images. It has been used in a variety of industries, including robots, self-driving cars, scene comprehension, 3D reconstructions, and others. The goal of our method is to create a lightweight machine-learning model in order to predict the depth value of each pixel given only a single RGB image as input with the Unet structure of the image segmentation network. We use the NYU Depth V2 dataset to test the structure and compare the result with other methods. The proposed method achieves relatively high accuracy and low rootmean-square error.
Advanced Baseline for 3D Human Pose Estimation: A Two-Stage Approach
Authors: Zichen Gui, Jungang Luo
Date: 2022-12-21 00:00:00
Tasks: 2D Human Pose Estimation, 3D Human Pose Estimation, Pose Estimation, Vocal Bursts Valence Prediction

Human pose estimation has been widely applied in various industries. While recent decades have witnessed the introduction of many advanced two-dimensional (2D) human pose estimation solutions, three-dimensional (3D) human pose estimation is still an active research field in computer vision. Generally speaking, 3D human pose estimation methods can be divided into two categories: single-stage and two-stage. In this paper, we focused on the 2D-to-3D lifting process in the two-stage methods and proposed a more advanced baseline model for 3D human pose estimation, based on the existing solutions. Our improvements include optimization of machine learning models and multiple parameters, as well as introduction of a weighted loss to the training model. Finally, we used the Human3.6M benchmark to test the final performance and it did produce satisfactory results.
DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation
Authors: Feilong Tang, Qiming Huang, Jinfeng Wang, Xianxu Hou, Jionglong Su, Jingxin Liu
Date: 2022-12-21 00:00:00
Tasks: Image Segmentation, Lesion Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Transformer-based models have been widely demonstrated to be successful in computer vision tasks by modelling long-range dependencies and capturing global representations. However, they are often dominated by features of large patterns leading to the loss of local details (e.g., boundaries and small objects), which are critical in medical image segmentation. To alleviate this problem, we propose a Dual-Aggregation Transformer Network called DuAT, which is characterized by two innovative designs, namely, the Global-to-Local Spatial Aggregation (GLSA) and Selective Boundary Aggregation (SBA) modules. The GLSA has the ability to aggregate and represent both global and local spatial features, which are beneficial for locating large and small objects, respectively. The SBA module is used to aggregate the boundary characteristic from low-level features and semantic information from high-level features for better preserving boundary details and locating the re-calibration objects. Extensive experiments in six benchmark datasets demonstrate that our proposed model outperforms state-of-the-art methods in the segmentation of skin lesion images, and polyps in colonoscopy images. In addition, our approach is more robust than existing methods in various challenging situations such as small object segmentation and ambiguous object boundaries.
Scene-aware Egocentric 3D Human Pose Estimation
Authors: Jian Wang, Lingjie Liu, Weipeng Xu, Kripasindhu Sarkar, Diogo Luvizon, Christian Theobalt
Date: 2022-12-20 00:00:00
Proceeding: CVPR 2023 1
Tasks: 3D Human Pose Estimation, Depth Estimation, Egocentric Pose Estimation, Pose Estimation

Egocentric 3D human pose estimation with a single head-mounted fisheye camera has recently attracted attention due to its numerous applications in virtual and augmented reality. Existing methods still struggle in challenging poses where the human body is highly occluded or is closely interacting with the scene. To address this issue, we propose a scene-aware egocentric pose estimation method that guides the prediction of the egocentric pose with scene constraints. To this end, we propose an egocentric depth estimation network to predict the scene depth map from a wide-view egocentric fisheye camera while mitigating the occlusion of the human body with a depth-inpainting network. Next, we propose a scene-aware pose estimation network that projects the 2D image features and estimated depth map of the scene into a voxel space and regresses the 3D pose with a V2V network. The voxel-based feature representation provides the direct geometric connection between 2D image features and scene geometry, and further facilitates the V2V network to constrain the predicted pose based on the estimated scene geometry. To enable the training of the aforementioned networks, we also generated a synthetic dataset, called EgoGTA, and an in-the-wild dataset based on EgoPW, called EgoPW-Scene. The experimental results of our new evaluation sequences show that the predicted 3D egocentric poses are accurate and physically plausible in terms of human-scene interaction, demonstrating that our method outperforms the state-of-the-art methods both quantitatively and qualitatively.
Realizing Molecular Machine Learning through Communications for Biological AI: Future Directions and Challenges
Authors: Sasitharan Balasubramaniam, Samitha Somathilaka, Sehee Sun, Adrian Ratwatte, Massimiliano Pierobon
Date: 2022-12-22 00:00:00

Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various types of devices, we benefit from their use into energy-efficient algorithms for low powered devices. In this paper, we investigate a scale and medium that is far smaller than conventional devices as we move towards molecular systems that can be utilized to perform machine learning functions, i.e., Molecular Machine Learning (MML). Fundamental to the operation of MML is the transport, processing, and interpretation of information propagated by molecules through chemical reactions. We begin by reviewing the current approaches that have been developed for MML, before we move towards potential new directions that rely on gene regulatory networks inside biological organisms as well as their population interactions to create neural networks. We then investigate mechanisms for training machine learning structures in biological cells based on calcium signaling and demonstrate their application to build an Analog to Digital Converter (ADC). Lastly, we look at potential future directions as well as challenges that this area could solve.
StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation
Authors: Jean-Marie Lemercier, Julius Richter, Simon Welker, Timo Gerkmann
Date: 2022-12-22 00:00:00
Tasks: Speech Dereverberation, Speech Enhancement

Diffusion models have shown a great ability at bridging the performance gap between predictive and generative approaches for speech enhancement. We have shown that they may even outperform their predictive counterparts for non-additive corruption types or when they are evaluated on mismatched conditions. However, diffusion models suffer from a high computational burden, mainly as they require to run a neural network for each reverse diffusion step, whereas predictive approaches only require one pass. As diffusion models are generative approaches they may also produce vocalizing and breathing artifacts in adverse conditions. In comparison, in such difficult scenarios, predictive models typically do not produce such artifacts but tend to distort the target speech instead, thereby degrading the speech quality. In this work, we present a stochastic regeneration approach where an estimate given by a predictive model is provided as a guide for further diffusion. We show that the proposed approach uses the predictive model to remove the vocalizing and breathing artifacts while producing very high quality samples thanks to the diffusion model, even in adverse conditions. We further show that this approach enables to use lighter sampling schemes with fewer diffusion steps without sacrificing quality, thus lifting the computational burden by an order of magnitude. Source code and audio examples are available online (https://uhh.de/inf-sp-storm).