pwc-archive/papers-with-abstracts huggingface dataset:
Cascade Learning Localises Discriminant Features in Visual Scene Classification
Authors: Junwen Wang, Katayoun Farrahi
Date: 2023-11-21 00:00:00
Tasks: Classification, object-detection, Object Detection, Scene Classification

Lack of interpretability of deep convolutional neural networks (DCNN) is a well-known problem particularly in the medical domain as clinicians want trustworthy automated decisions. One way to improve trust is to demonstrate the localisation of feature representations with respect to expert labeled regions of interest. In this work, we investigate the localisation of features learned via two varied learning paradigms and demonstrate the superiority of one learning approach with respect to localisation. Our analysis on medical and natural datasets show that the traditional end-to-end (E2E) learning strategy has a limited ability to localise discriminative features across multiple network layers. We show that a layer-wise learning strategy, namely cascade learning (CL), results in more localised features. Considering localisation accuracy, we not only show that CL outperforms E2E but that it is a promising method of predicting regions. On the YOLO object detection framework, our best result shows that CL outperforms the E2E scheme by $2\%$ in mAP.
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
Authors: Mu Chen, Zhedong Zheng, Yi Yang
Date: 2023-11-21 00:00:00
Tasks: Depth Estimation, Domain Adaptation, Scene Segmentation, Synthetic-to-Real Translation, Unsupervised Domain Adaptation

Scene segmentation via unsupervised domain adaptation (UDA) enables the transfer of knowledge acquired from source synthetic data to real-world target data, which largely reduces the need for manual pixel-level annotations in the target domain. To facilitate domain-invariant feature learning, existing methods typically mix data from both the source domain and target domain by simply copying and pasting the pixels. Such vanilla methods are usually sub-optimal since they do not take into account how well the mixed layouts correspond to real-world scenarios. Real-world scenarios are with an inherent layout. We observe that semantic categories, such as sidewalks, buildings, and sky, display relatively consistent depth distributions, and could be clearly distinguished in a depth map. Based on such observation, we propose a depth-aware framework to explicitly leverage depth estimation to mix the categories and facilitate the two complementary tasks, i.e., segmentation and depth learning in an end-to-end manner. In particular, the framework contains a Depth-guided Contextual Filter (DCF) forndata augmentation and a cross-task encoder for contextual learning. DCF simulates the real-world layouts, while the cross-task encoder further adaptively fuses the complementing features between two tasks. Besides, it is worth noting that several public datasets do not provide depth annotation. Therefore, we leverage the off-the-shelf depth estimation network to generate the pseudo depth. Extensive experiments show that our proposed methods, even with pseudo depth, achieve competitive performance on two widely-used bench-marks, i.e. 77.7 mIoU on GTA to Cityscapes and 69.3 mIoU on Synthia to Cityscapes.
BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse Multiview Videos
Authors: Georgios Albanis, Nikolaos Zioulis, Kostas Kolomvatsos
Date: 2023-11-21 00:00:00
Tasks: 3D human pose and shape estimation, 3D Human Pose Estimation, Markerless Motion Capture

Capturing smooth motions from videos using markerless techniques typically involves complex processes such as temporal constraints, multiple stages with data-driven regression and optimization, and bundle solving over temporal windows. These processes can be inefficient and require tuning multiple objectives across stages. In contrast, BundleMoCap introduces a novel and efficient approach to this problem. It solves the motion capture task in a single stage, eliminating the need for temporal smoothness objectives while still delivering smooth motions. BundleMoCap outperforms the state-of-the-art without increasing complexity. The key concept behind BundleMoCap is manifold interpolation between latent keyframes. By relying on a local manifold smoothness assumption, we can efficiently solve a bundle of frames using a single code. Additionally, the method can be implemented as a sliding window optimization and requires only the first frame to be properly initialized, reducing the overall computational burden. BundleMoCap's strength lies in its ability to achieve high-quality motion capture results with simplicity and efficiency. More details can be found at https://moverseai.github.io/bundle/.
Similar Document Template Matching Algorithm
Authors: Harshitha Yenigalla, Bommareddy Revanth Srinivasa Reddy, Batta Venkata Rahul, Nannapuraju Hemanth Raju
Date: 2023-11-21 00:00:00
Tasks: Fraud Detection, Optical Character Recognition (OCR), SSIM, Template Matching

This study outlines a comprehensive methodology for verifying medical documents, integrating advanced techniques in template extraction, comparison, and fraud detection. It begins with template extraction using sophisticated region-of-interest (ROI) methods, incorporating contour analysis and edge identification. Pre-processing steps ensure template clarity through morphological operations and adaptive thresholding. The template comparison algorithm utilizes advanced feature matching with key points and descriptors, enhancing robustness through histogram-based analysis for accounting variations. Fraud detection involves the SSIM computation and OCR for textual information extraction. The SSIM quantifies structural similarity, aiding in potential match identification. OCR focuses on critical areas like patient details, provider information, and billing amounts. Extracted information is compared with a reference dataset, and confidence thresholding ensures reliable fraud detection. Adaptive parameters enhance system flexibility for dynamic adjustments to varying document layouts. This methodology provides a robust approach to medical document verification, addressing complexities in template extraction, comparison, fraud detection, and adaptability to diverse document structures.
Visually Guided Object Grasping
Authors: Radu Horaud, Fadi Dornaika, Bernard Espiau
Date: 2023-11-21 00:00:00
Tasks: Object

In this paper we present a visual servoing approach to the problem of object grasping and more generally, to the problem of aligning an end-effector with an object. First we extend the method proposed by Espiau et al. [1] to the case of a camera which is not mounted onto the robot being controlled and we stress the importance of the real-time estimation of the image Jacobian. Second, we show how to represent a grasp or more generally, an alignment between two solids in 3-D projective space using an uncalibrated stereo rig. Such a 3-D projective representation is view-invariant in the sense that it can be easily mapped into an image set-point without any knowledge about the camera parameters. Third, we perform an analysis of the performances of the visual servoing algorithm and of the grasping precision that can be expected from this type of approach.
Hand-Eye Calibration
Authors: Radu Horaud, Fadi Dornaika
Date: 2023-11-21 00:00:00
Tasks: Translation

Whenever a sensor is mounted on a robot hand it is important to know the relationship between the sensor and the hand. The problem of determining this relationship is referred to as hand-eye calibration, which is important in at least two types of tasks: (i) map sensor centered measurements into the robot workspace and (ii) allow the robot to precisely move the sensor. In the past some solutions were proposed in the particular case of a camera. With almost no exception, all existing solutions attempt to solve the homogeneous matrix equation AX=XB. First we show that there are two possible formulations of the hand-eye calibration problem. One formulation is the classical one that we just mentioned. A second formulation takes the form of the following homogeneous matrix equation: MY=M'YB. The advantage of the latter is that the extrinsic and intrinsic camera parameters need not be made explicit. Indeed, this formulation directly uses the 3 by 4 perspective matrices (M and M') associated with two positions of the camera. Moreover, this formulation together with the classical one cover a wider range of camera-based sensors to be calibrated with respect to the robot hand. Second, we develop a common mathematical framework to solve for the hand-eye calibration problem using either of the two formulations. We present two methods, (i) a rotation then translation and (ii) a non-linear solver for rotation and translation. Third, we perform a stability analysis both for our two methods and for the classical linear method of Tsai and Lenz (1989). In the light of this comparison, the non-linear optimization method, that solves for rotation and translation simultaneously, seems to be the most robust one with respect to noise and to measurement errors.
Polyhedral Object Recognition by Indexing
Authors: Radu Horaud, Humberto Sossa
Date: 2023-11-21 00:00:00
Tasks: Object, Object Recognition

In computer vision, the indexing problem is the problem of recognizing a few objects in a large database of objects while avoiding the help of the classical image-feature-to-object-feature matching paradigm. In this paper we address the problem of recognizing 3-D polyhedral objects from 2-D images by indexing. Both the objects to be recognized and the images are represented by weighted graphs. The indexing problem is therefore the problem of determining whether a graph extracted from the image is present or absent in a database of model graphs. We introduce a novel method for performing this graph indexing process which is based both on polynomial characterization of binary and weighted graphs and on hashing. We describe in detail this polynomial characterization and then we show how it can be used in the context of polyhedral object recognition. Next we describe a practical recognition-by-indexing system that includes the organization of the database, the representation of polyhedral objects in terms of 2-D characteristic views, the representation of this views in terms of weighted graphs, and the associated image processing. Finally, some experimental results allow the evaluation of the system performance.
GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning
Authors: Jiaxi Lv, Yi Huang, Mingfu Yan, Jiancheng Huang, Jianzhuang Liu, Yifan Liu, Yafei Wen, Xiaoxin Chen, Shifeng Chen
Date: 2023-11-21 00:00:00
Tasks: Image Generation, Text-to-Video Generation, Video Generation

Recent advances in text-to-video generation have harnessed the power of diffusion models to create visually compelling content conditioned on text prompts. However, they usually encounter high computational costs and often struggle to produce videos with coherent physical motions. To tackle these issues, we propose GPT4Motion, a training-free framework that leverages the planning capability of large language models such as GPT, the physical simulation strength of Blender, and the excellent image generation ability of text-to-image diffusion models to enhance the quality of video synthesis. Specifically, GPT4Motion employs GPT-4 to generate a Blender script based on a user textual prompt, which commands Blender's built-in physics engine to craft fundamental scene components that encapsulate coherent physical motions across frames. Then these components are inputted into Stable Diffusion to generate a video aligned with the textual prompt. Experimental results on three basic physical motion scenarios, including rigid object drop and collision, cloth draping and swinging, and liquid flow, demonstrate that GPT4Motion can generate high-quality videos efficiently in maintaining motion coherency and entity consistency. GPT4Motion offers new insights in text-to-video research, enhancing its quality and broadening its horizon for further explorations.
Bridging Generalization Gaps in High Content Imaging Through Online Self-Supervised Domain Adaptation
Authors: Johan Fredin Haslum, Christos Matsoukas, Karl-Johan Leuchowius, Kevin Smith
Date: 2023-11-21 00:00:00
Tasks: Domain Adaptation, Drug Discovery

High Content Imaging (HCI) plays a vital role in modern drug discovery and development pipelines, facilitating various stages from hit identification to candidate drug characterization. Applying machine learning models to these datasets can prove challenging as they typically consist of multiple batches, affected by experimental variation, especially if different imaging equipment have been used. Moreover, as new data arrive, it is preferable that they are analyzed in an online fashion. To overcome this, we propose CODA, an online self-supervised domain adaptation approach. CODA divides the classifier's role into a generic feature extractor and a task-specific model. We adapt the feature extractor's weights to the new domain using cross-batch self-supervision while keeping the task-specific model unchanged. Our results demonstrate that this strategy significantly reduces the generalization gap, achieving up to a 300% improvement when applied to data from different labs utilizing different microscopes. CODA can be applied to new, unlabeled out-of-domain data sources of different sizes, from a single plate to multiple experimental batches.
Crowd management, crime detection, work monitoring using aiml
Authors: P. R. Adithya, Dheepak. S, B. Akash, Harshini. V, Sai Lakshana
Date: 2023-11-21 00:00:00
Tasks: Management

This research endeavors to harness the potential of existing Closed-Circuit Television (CCTV) networks for a comprehensive approach to crowd management, crime prevention, and workplace monitoring through the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. The primary objective is to develop and implement advanced algorithms capable of real-time analysis of video feeds, enabling the identification and assessment of crowd dynamics, early detection of potential criminal activities, and continuous monitoring of workplace environments. By leveraging AI/ML, the project aims to optimize surveillance capabilities, thereby enhancing public safety measures and improving organizational productivity. This initiative underscores the transformative impact that intelligent video analytics can have on existing infrastructure, mitigating the need for extensive system overhauls while significantly advancing security and operational efficiency.
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning
Authors: Sanaz Karimijafarbigloo, Reza Azad, Yury Velichko, Ulas Bagci, Dorit Merhof
Date: 2023-11-21 00:00:00
Tasks: Contrastive Learning, Image Segmentation, Medical Image Segmentation, Organ Segmentation, Semantic Segmentation

Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-labels for effective unsupervised data use. To address these challenges, we introduce two distinct subnetworks designed to explore and exploit the discrepancies between them, ultimately correcting the erroneous prediction results. More specifically, we identify regions of inconsistent predictions and initiate a targeted verification training process. This procedure strategically fine-tunes and harmonizes the predictions of the subnetworks, leading to enhanced utilization of contextual information. Furthermore, to adaptively fine-tune the network's representational capacity and reduce prediction uncertainty, we employ a self-supervised contrastive learning paradigm. For this, we use the network's confidence to distinguish between reliable and unreliable predictions. The model is then trained to effectively minimize unreliable predictions. Our experimental results for organ segmentation, obtained from clinical MRI and CT scans, demonstrate the effectiveness of our approach when compared to state-of-the-art methods. The codebase is accessible on \href{https://github.com/xmindflow/SSL-contrastive}{GitHub}.
Density-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection
Authors: Tong Zhao, Qiang Fang, Shuohao Shi, Xin Xu
Date: 2023-11-21 00:00:00
Tasks: Object, object-detection, Object Detection, Oriented Object Detection, Pseudo Label, Semi-Supervised Object Detection

Recently, dense pseudo-label, which directly selects pseudo labels from the original output of the teacher model without any complicated post-processing steps, has received considerable attention in semi-supervised object detection (SSOD). However, for the multi-oriented and dense objects that are common in aerial scenes, existing dense pseudo-label selection methods are inefficient because they ignore the significant density difference. Therefore, we propose Density-Guided Dense Pseudo Label Selection (DDPLS) for semi-supervised oriented object detection. In DDPLS, we design a simple but effective adaptive mechanism to guide the selection of dense pseudo labels. Specifically, we propose the Pseudo Density Score (PDS) to estimate the density of potential objects and use this score to select reliable dense pseudo labels. On the DOTA-v1.5 benchmark, the proposed method outperforms previous methods especially when labeled data are scarce. For example, it achieves 49.78 mAP given only 5\% of annotated data, which surpasses previous state-of-the-art method given 10\% of annotated data by 1.15 mAP. Our codes is available at https://github.com/Haru-zt/DDPLS.
Surgical Temporal Action-aware Network with Sequence Regularization for Phase Recognition
Authors: Zhen Chen, Yuhao Zhai, Jun Zhang, Jinqiao Wang
Date: 2023-11-21 00:00:00
Tasks: Surgical phase recognition

To assist surgeons in the operating theatre, surgical phase recognition is critical for developing computer-assisted surgical systems, which requires comprehensive understanding of surgical videos. Although existing studies made great progress, there are still two significant limitations worthy of improvement. First, due to the compromise of resource consumption, frame-wise visual features are extracted by 2D networks and disregard spatial and temporal knowledge of surgical actions, which hinders subsequent inter-frame modeling for phase prediction. Second, these works simply utilize ordinary classification loss with one-hot phase labels to optimize the phase predictions, and cannot fully explore surgical videos under inadequate supervision. To overcome these two limitations, we propose a Surgical Temporal Action-aware Network with sequence Regularization, named STAR-Net, to recognize surgical phases more accurately from input videos. Specifically, we propose an efficient multi-scale surgical temporal action (MS-STA) module, which integrates visual features with spatial and temporal knowledge of surgical actions at the cost of 2D networks. Moreover, we devise the dual-classifier sequence regularization (DSR) to facilitate the training of STAR-Net by the sequence guidance of an auxiliary classifier with a smaller capacity. Our STAR-Net with MS-STA and DSR can exploit visual features of surgical actions with effective regularization, thereby leading to the superior performance of surgical phase recognition. Extensive experiments on a large-scale gastrectomy surgery dataset and the public Cholec80 benchmark prove that our STAR-Net significantly outperforms state-of-the-arts of surgical phase recognition.
TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction using Vision-Based Tactile Sensing
Authors: Mauro Comi, Yijiong Lin, Alex Church, Alessio Tonioni, Laurence Aitchison, Nathan F. Lepora
Date: 2023-11-21 00:00:00
Tasks: 3D Shape Reconstruction

Humans rely on their visual and tactile senses to develop a comprehensive 3D understanding of their physical environment. Recently, there has been a growing interest in exploring and manipulating objects using data-driven approaches that utilise high-resolution vision-based tactile sensors. However, 3D shape reconstruction using tactile sensing has lagged behind visual shape reconstruction because of limitations in existing techniques, including the inability to generalise over unseen shapes, the absence of real-world testing, and limited expressive capacity imposed by discrete representations. To address these challenges, we propose TouchSDF, a Deep Learning approach for tactile 3D shape reconstruction that leverages the rich information provided by a vision-based tactile sensor and the expressivity of the implicit neural representation DeepSDF. Our technique consists of two components: (1) a Convolutional Neural Network that maps tactile images into local meshes representing the surface at the touch location, and (2) an implicit neural function that predicts a signed distance function to extract the desired 3D shape. This combination allows TouchSDF to reconstruct smooth and continuous 3D shapes from tactile inputs in simulation and real-world settings, opening up research avenues for robust 3D-aware representations and improved multimodal perception in robotics. Code and supplementary material are available at: https://touchsdf.github.io/
Deep learning-based detection of morphological features associated with hypoxia in H&E breast cancer whole slide images
Authors: Petru Manescu, Joseph Geradts, Delmiro Fernandez-Reyes
Date: 2023-11-21 00:00:00
Tasks: Multiple Instance Learning, whole slide images

Hypoxia occurs when tumour cells outgrow their blood supply, leading to regions of low oxygen levels within the tumour. Calculating hypoxia levels can be an important step in understanding the biology of tumours, their clinical progression and response to treatment. This study demonstrates a novel application of deep learning to evaluate hypoxia in the context of breast cancer histomorphology. More precisely, we show that Weakly Supervised Deep Learning (WSDL) models can accurately detect hypoxia associated features in routine Hematoxylin and Eosin (H&E) whole slide images (WSI). We trained and evaluated a deep Multiple Instance Learning model on tiles from WSI H&E tissue from breast cancer primary sites (n=240) obtaining on average an AUC of 0.87 on a left-out test set. We also showed significant differences between features of hypoxic and normoxic tissue regions as distinguished by the WSDL models. Such DL hypoxia H&E WSI detection models could potentially be extended to other tumour types and easily integrated into the pathology workflow without requiring additional costly assays.
HiPose: Hierarchical Binary Surface Encoding and Correspondence Pruning for RGB-D 6DoF Object Pose Estimation
Authors: Yongliang Lin, Yongzhi Su, Praveen Nathan, Sandeep Inuganti, Yan Di, Martin Sundermeyer, Fabian Manhardt, Didier Stricker, Jason Rambach, Yu Zhang
Date: 2023-11-21 00:00:00
Proceeding: CVPR 2024 1
Tasks: Pose Estimation

In this work, we present a novel dense-correspondence method for 6DoF object pose estimation from a single RGB-D image. While many existing data-driven methods achieve impressive performance, they tend to be time-consuming due to their reliance on rendering-based refinement approaches. To circumvent this limitation, we present HiPose, which establishes 3D-3D correspondences in a coarse-to-fine manner with a hierarchical binary surface encoding. Unlike previous dense-correspondence methods, we estimate the correspondence surface by employing point-to-surface matching and iteratively constricting the surface until it becomes a correspondence point while gradually removing outliers. Extensive experiments on public benchmarks LM-O, YCB-V, and T-Less demonstrate that our method surpasses all refinement-free methods and is even on par with expensive refinement-based approaches. Crucially, our approach is computationally efficient and enables real-time critical applications with high accuracy requirements.