pwc-archive/papers-with-abstracts huggingface dataset:
Generative Adversarial Networks for Stain Normalisation in Histopathology
Authors: Jack Breen, Kieran Zucker, Katie Allen, Nishant Ravikumar, Nicolas M. Orsi
Date: 2023-08-05 00:00:00

The rapid growth of digital pathology in recent years has provided an ideal opportunity for the development of artificial intelligence-based tools to improve the accuracy and efficiency of clinical diagnoses. One of the significant roadblocks to current research is the high level of visual variability across digital pathology images, causing models to generalise poorly to unseen data. Stain normalisation aims to standardise the visual profile of digital pathology images without changing the structural content of the images. In this chapter, we explore different techniques which have been used for stain normalisation in digital pathology, with a focus on approaches which utilise generative adversarial networks (GANs). Typically, GAN-based methods outperform non-generative approaches but at the cost of much greater computational requirements. However, it is not clear which method is best for stain normalisation in general, with different GAN and non-GAN approaches outperforming each other in different scenarios and according to different performance metrics. This is an ongoing field of study as researchers aim to identify a method which efficiently and effectively normalises pathology images to make AI models more robust and generalisable.
Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation
Authors: Tianhang Liu, Hechen Li, Long Bai, Yanan Wu, An Wang, Mobarakol Islam, Hongliang Ren
Date: 2023-08-05 00:00:00

Robot-assisted airway intubation application needs high accuracy in locating targets and organs. Two vital landmarks, nostrils and glottis, can be detected during the intubation to accommodate the stages of nasal intubation. Automated landmark detection can provide accurate localization and quantitative evaluation. The Detection Transformer (DeTR) leads object detectors to a new paradigm with long-range dependence. However, current DeTR requires long iterations to converge, and does not perform well in detecting small objects. This paper proposes a transformer-based landmark detection solution with deformable DeTR and the semantic-aligned-matching module for detecting landmarks in robot-assisted intubation. The semantics aligner can effectively align the semantics of object queries and image features in the same embedding space using the most discriminative features. To evaluate the performance of our solution, we utilize a publicly accessible glottis dataset and automatically annotate a nostril detection dataset. The experimental results demonstrate our competitive performance in detection accuracy. Our code is publicly accessible.
Learning Unified Decompositional and Compositional NeRF for Editable Novel View Synthesis
Authors: Yuxin Wang, Wayne Wu, Dan Xu
Date: 2023-08-05 00:00:00
Proceeding: ICCV 2023 1
Tasks: NeRF, Novel View Synthesis

Implicit neural representations have shown powerful capacity in modeling real-world 3D scenes, offering superior performance in novel view synthesis. In this paper, we target a more challenging scenario, i.e., joint scene novel view synthesis and editing based on implicit neural scene representations. State-of-the-art methods in this direction typically consider building separate networks for these two tasks (i.e., view synthesis and editing). Thus, the modeling of interactions and correlations between these two tasks is very limited, which, however, is critical for learning high-quality scene representations. To tackle this problem, in this paper, we propose a unified Neural Radiance Field (NeRF) framework to effectively perform joint scene decomposition and composition for modeling real-world scenes. The decomposition aims at learning disentangled 3D representations of different objects and the background, allowing for scene editing, while scene composition models an entire scene representation for novel view synthesis. Specifically, with a two-stage NeRF framework, we learn a coarse stage for predicting a global radiance field as guidance for point sampling, and in the second fine-grained stage, we perform scene decomposition by a novel one-hot object radiance field regularization module and a pseudo supervision via inpainting to handle ambiguous background regions occluded by objects. The decomposed object-level radiance fields are further composed by using activations from the decomposition module. Extensive quantitative and qualitative results show the effectiveness of our method for scene decomposition and composition, outperforming state-of-the-art methods for both novel-view synthesis and editing tasks.
A Comprehensive Analysis of Real-World Image Captioning and Scene Identification
Authors: Sai Suprabhanu Nallapaneni, Subrahmanyam Konakanchi
Date: 2023-08-05 00:00:00
Tasks: Descriptive, Image Captioning, Image Retrieval, Retrieval

Image captioning is a computer vision task that involves generating natural language descriptions for images. This method has numerous applications in various domains, including image retrieval systems, medicine, and various industries. However, while there has been significant research in image captioning, most studies have focused on high quality images or controlled environments, without exploring the challenges of real-world image captioning. Real-world image captioning involves complex and dynamic environments with numerous points of attention, with images which are often very poor in quality, making it a challenging task, even for humans. This paper evaluates the performance of various models that are built on top of different encoding mechanisms, language decoders and training procedures using a newly created real-world dataset that consists of over 800+ images of over 65 different scene classes, built using MIT Indoor scenes dataset. This dataset is captioned using the IC3 approach that generates more descriptive captions by summarizing the details that are covered by standard image captioning models from unique view-points of the image.
SwinGar: Spectrum-Inspired Neural Dynamic Deformation for Free-Swinging Garments
Authors: TianXing Li, Rui Shi, Qing Zhu, Takashi Kanai
Date: 2023-08-05 00:00:00

Our work presents a novel spectrum-inspired learning-based approach for generating clothing deformations with dynamic effects and personalized details. Existing methods in the field of clothing animation are limited to either static behavior or specific network models for individual garments, which hinders their applicability in real-world scenarios where diverse animated garments are required. Our proposed method overcomes these limitations by providing a unified framework that predicts dynamic behavior for different garments with arbitrary topology and looseness, resulting in versatile and realistic deformations. First, we observe that the problem of bias towards low frequency always hampers supervised learning and leads to overly smooth deformations. To address this issue, we introduce a frequency-control strategy from a spectral perspective that enhances the generation of high-frequency details of the deformation. In addition, to make the network highly generalizable and able to learn various clothing deformations effectively, we propose a spectral descriptor to achieve a generalized description of the global shape information. Building on the above strategies, we develop a dynamic clothing deformation estimator that integrates frequency-controllable attention mechanisms with long short-term memory. The estimator takes as input expressive features from garments and human bodies, allowing it to automatically output continuous deformations for diverse clothing types, independent of mesh topology or vertex count. Finally, we present a neural collision handling method to further enhance the realism of garments. Our experimental results demonstrate the effectiveness of our approach on a variety of free-swinging garments and its superiority over state-of-the-art methods.
Deep Image Harmonization in Dual Color Spaces
Authors: Linfeng Tan, Jiangtong Li, Li Niu, Liqing Zhang
Date: 2023-08-05 00:00:00
Tasks: Decoder, Image Harmonization

Image harmonization is an essential step in image composition that adjusts the appearance of composite foreground to address the inconsistency between foreground and background. Existing methods primarily operate in correlated $RGB$ color space, leading to entangled features and limited representation ability. In contrast, decorrelated color space (e.g., $Lab$) has decorrelated channels that provide disentangled color and illumination statistics. In this paper, we explore image harmonization in dual color spaces, which supplements entangled $RGB$ features with disentangled $L$, $a$, $b$ features to alleviate the workload in harmonization process. The network comprises a $RGB$ harmonization backbone, an $Lab$ encoding module, and an $Lab$ control module. The backbone is a U-Net network translating composite image to harmonized image. Three encoders in $Lab$ encoding module extract three control codes independently from $L$, $a$, $b$ channels, which are used to manipulate the decoder features in harmonization backbone via $Lab$ control module. Our code and model are available at \href{https://github.com/bcmi/DucoNet-Image-Harmonization}{https://github.com/bcmi/DucoNet-Image-Harmonization}.
Thin On-Sensor Nanophotonic Array Cameras
Authors: PRANEETH CHAKRAVARTHULA, Jipeng Sun, Xiao Li, Chenyang Lei, Gene Chou, Mario Bijelic, Johannes Froesch, Arka Majumdar, Felix Heide
Date: 2023-08-05 00:00:00

Today's commodity camera systems rely on compound optics to map light originating from the scene to positions on the sensor where it gets recorded as an image. To record images without optical aberrations, i.e., deviations from Gauss' linear model of optics, typical lens systems introduce increasingly complex stacks of optical elements which are responsible for the height of existing commodity cameras. In this work, we investigate \emph{flat nanophotonic computational cameras} as an alternative that employs an array of skewed lenslets and a learned reconstruction approach. The optical array is embedded on a metasurface that, at 700~nm height, is flat and sits on the sensor cover glass at 2.5~mm focal distance from the sensor. To tackle the highly chromatic response of a metasurface and design the array over the entire sensor, we propose a differentiable optimization method that continuously samples over the visible spectrum and factorizes the optical modulation for different incident fields into individual lenses. We reconstruct a megapixel image from our flat imager with a \emph{learned probabilistic reconstruction} method that employs a generative diffusion model to sample an implicit prior. To tackle \emph{scene-dependent aberrations in broadband}, we propose a method for acquiring paired captured training data in varying illumination conditions. We assess the proposed flat camera design in simulation and with an experimental prototype, validating that the method is capable of recovering images from diverse scenes in broadband with a single nanophotonic layer.
Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-Resolution
Authors: Yong liu, Hang Dong, Boyang Liang, Songwei Liu, Qingji Dong, Kai Chen, Fangmin Chen, Lean Fu, Fei Wang
Date: 2023-08-05 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Recent years have witnessed a few attempts of vision transformers for single image super-resolution (SISR). Since the high resolution of intermediate features in SISR models increases memory and computational requirements, efficient SISR transformers are more favored. Based on some popular transformer backbone, many methods have explored reasonable schemes to reduce the computational complexity of the self-attention module while achieving impressive performance. However, these methods only focus on the performance on the training platform (e.g., Pytorch/Tensorflow) without further optimization for the deployment platform (e.g., TensorRT). Therefore, they inevitably contain some redundant operators, posing challenges for subsequent deployment in real-world applications. In this paper, we propose a deployment-friendly transformer unit, namely UFONE (i.e., UnFolding ONce is Enough), to alleviate these problems. In each UFONE, we introduce an Inner-patch Transformer Layer (ITL) to efficiently reconstruct the local structural information from patches and a Spatial-Aware Layer (SAL) to exploit the long-range dependencies between patches. Based on UFONE, we propose a Deployment-friendly Inner-patch Transformer Network (DITN) for the SISR task, which can achieve favorable performance with low latency and memory usage on both training and deployment platforms. Furthermore, to further boost the deployment efficiency of the proposed DITN on TensorRT, we also provide an efficient substitution for layer normalization and propose a fusion optimization strategy for specific operators. Extensive experiments show that our models can achieve competitive results in terms of qualitative and quantitative performance with high deployment efficiency. Code is available at \url{https://github.com/yongliuy/DITN}.
Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation
Authors: Chengjia Jiang, Tao Wang, Sien Li, Jinyang Wang, Shirui Wang, Antonios Antoniou
Date: 2023-08-05 00:00:00
Tasks: Class-Incremental Semantic Segmentation, Knowledge Distillation, Semantic Segmentation

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://github.com/ChasonJiang/FSCILSS.
A Voting-Stacking Ensemble of Inception Networks for Cervical Cytology Classification
Authors: Linyi Qian, Qian Huang, Yulin Chen, Junzhou Chen
Date: 2023-08-05 00:00:00

Cervical cancer is one of the most severe diseases threatening women's health. Early detection and diagnosis can significantly reduce cancer risk, in which cervical cytology classification is indispensable. Researchers have recently designed many networks for automated cervical cancer diagnosis, but the limited accuracy and bulky size of these individual models cannot meet practical application needs. To address this issue, we propose a Voting-Stacking ensemble strategy, which employs three Inception networks as base learners and integrates their outputs through a voting ensemble. The samples misclassified by the ensemble model generate a new training set on which a linear classification model is trained as the meta-learner and performs the final predictions. In addition, a multi-level Stacking ensemble framework is designed to improve performance further. The method is evaluated on the SIPakMed, Herlev, and Mendeley datasets, achieving accuracies of 100%, 100%, and 100%, respectively. The experimental results outperform the current state-of-the-art (SOTA) methods, demonstrating its potential for reducing screening workload and helping pathologists detect cervical cancer.
Dual Degradation-Inspired Deep Unfolding Network for Low-Light Image Enhancement
Authors: Huake Wang, Xingsong Hou, Chengcu Liu, Kaibing Zhang, Xiangyong Cao, Xueming Qian
Date: 2023-08-05 00:00:00
Tasks: Image Enhancement, Low-Light Image Enhancement, Specificity

Although low-light image enhancement has achieved great stride based on deep enhancement models, most of them mainly stress on enhancement performance via an elaborated black-box network and rarely explore the physical significance of enhancement models. Towards this issue, we propose a Dual degrAdation-inSpired deep Unfolding network, termed DASUNet, for low-light image enhancement. Specifically, we construct a dual degradation model (DDM) to explicitly simulate the deterioration mechanism of low-light images. It learns two distinct image priors via considering degradation specificity between luminance and chrominance spaces. To make the proposed scheme tractable, we design an alternating optimization solution to solve the proposed DDM. Further, the designed solution is unfolded into a specified deep network, imitating the iteration updating rules, to form DASUNet. Based on different specificity in two spaces, we design two customized Transformer block to model different priors. Additionally, a space aggregation module (SAM) is presented to boost the interaction of two degradation models. Extensive experiments on multiple popular low-light image datasets validate the effectiveness of DASUNet compared to canonical state-of-the-art low-light image enhancement methods. Our source code and pretrained model will be publicly available.
One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer
Authors: Hang Guo, Tao Dai, Mingyan Zhu, Guanghao Meng, Bin Chen, Zhi Wang, Shu-Tao Xia
Date: 2023-08-05 00:00:00
Tasks: Contrastive Learning, Knowledge Distillation, Super-Resolution, Transfer Learning

Recognizing characters from low-resolution (LR) text images poses a significant challenge due to the information deficiency as well as the noise and blur in low-quality images. Current solutions for low-resolution text recognition (LTR) typically rely on a two-stage pipeline that involves super-resolution as the first stage followed by the second-stage recognition. Although this pipeline is straightforward and intuitive, it has to use an additional super-resolution network, which causes inefficiencies during training and testing. Moreover, the recognition accuracy of the second stage heavily depends on the reconstruction quality of the first stage, causing ineffectiveness. In this work, we attempt to address these challenges from a novel perspective: adapting the recognizer to low-resolution inputs by transferring the knowledge from the high-resolution. Guided by this idea, we propose an efficient and effective knowledge distillation framework to achieve multi-level knowledge transfer. Specifically, the visual focus loss is proposed to extract the character position knowledge with resolution gap reduction and character region focus, the semantic contrastive loss is employed to exploit the contextual semantic knowledge with contrastive learning, and the soft logits loss facilitates both local word-level and global sequence-level learning from the soft teacher label. Extensive experiments show that the proposed one-stage pipeline significantly outperforms super-resolution based two-stage frameworks in terms of effectiveness and efficiency, accompanied by favorable robustness. Code is available at https://github.com/csguoh/KD-LTR.
DeDrift: Robust Similarity Search under Content Drift
Authors: Dmitry Baranchuk, Matthijs Douze, Yash Upadhyay, I. Zeki Yalniz
Date: 2023-08-05 00:00:00
Proceeding: ICCV 2023 1

The statistical distribution of content uploaded and searched on media sharing sites changes over time due to seasonal, sociological and technical factors. We investigate the impact of this "content drift" for large-scale similarity search tools, based on nearest neighbor search in embedding space. Unless a costly index reconstruction is performed frequently, content drift degrades the search accuracy and efficiency. The degradation is especially severe since, in general, both the query and database distributions change. We introduce and analyze real-world image and video datasets for which temporal information is available over a long time period. Based on the learnings, we devise DeDrift, a method that updates embedding quantizers to continuously adapt large-scale indexing structures on-the-fly. DeDrift almost eliminates the accuracy degradation due to the query and database content drift while being up to 100x faster than a full index reconstruction.
Discrimination of Radiologists Utilizing Eye-Tracking Technology and Machine Learning: A Case Study
Authors: Stanford Martinez, Carolina Ramirez-Tamayo, Syed Hasib Akhter Faruqui, Kal L. Clark, Adel Alaeddini, Nicholas Czarnek, Aarushi Aggarwal, Sahra Emamzadeh, Jeffrey R. Mock, Edward J. Golob
Date: 2023-08-04 00:00:00
Tasks: Diagnostic, Specificity

Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists employ personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he/she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns. This discrepancy can interfere with quality improvement interventions and negatively impact patient care. This study presents a novel discretized feature encoding based on spatiotemporal binning of fixation data for efficient geometric alignment and temporal ordering of eye movement when reading chest X-rays. The encoded features of the eye-fixation data are employed by machine learning classifiers to discriminate between faculty and trainee radiologists. We include a clinical trial case study utilizing the Area Under the Curve (AUC), Accuracy, F1, Sensitivity, and Specificity metrics for class separability to evaluate the discriminability between the two subjects in regard to their level of experience. We then compare the classification performance to state-of-the-art methodologies. A repeatability experiment using a separate dataset, experimental protocol, and eye tracker was also performed using eight subjects to evaluate the robustness of the proposed approach. The numerical results from both experiments demonstrate that classifiers employing the proposed feature encoding methods outperform the current state-of-the-art in differentiating between radiologists in terms of experience level. This signifies the potential impact of the proposed method for identifying radiologists' level of expertise and those who would benefit from additional training.