pwc-archive/papers-with-abstracts huggingface dataset:
TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild Videos
Authors: Yufu Wang, ZiYun Wang, Lingjie Liu, Kostas Daniilidis
Date: 2024-03-26 00:00:00
Tasks: 3D Human Pose Estimation

We propose TRAM, a two-stage method to reconstruct a human's global trajectory and motion from in-the-wild videos. TRAM robustifies SLAM to recover the camera motion in the presence of dynamic humans and uses the scene background to derive the motion scale. Using the recovered camera as a metric-scale reference frame, we introduce a video transformer model (VIMO) to regress the kinematic body motion of a human. By composing the two motions, we achieve accurate recovery of 3D humans in the world space, reducing global motion errors by a large margin from prior work. https://yufu-wang.github.io/tram4d/
Residual-based Language Models are Free Boosters for Biomedical Imaging
Authors: Zhixin Lai, Jing Wu, Suiyao Chen, Yucheng Zhou, Naira Hovakimyan
Date: 2024-03-26 00:00:00

In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of language or textual data. The approach diverges from established methodologies by utilizing a frozen transformer block, extracted from pre-trained LLMs, as an innovative encoder layer for the direct processing of visual tokens. This strategy represents a significant departure from the standard multi-modal vision-language frameworks, which typically hinge on language-driven prompts and inputs. We found that these LLMs could boost performance across a spectrum of biomedical imaging applications, including both 2D and 3D visual classification tasks, serving as plug-and-play boosters. More interestingly, as a byproduct, we found that the proposed framework achieved superior performance, setting new state-of-the-art results on extensive, standardized datasets in MedMNIST-2D and 3D. Through this work, we aim to open new avenues for employing LLMs in biomedical imaging and enriching the understanding of their potential in this specialized domain.
OVER-NAV: Elevating Iterative Vision-and-Language Navigation with Open-Vocabulary Detection and StructurEd Representation
Authors: Ganlong Zhao, Guanbin Li, Weikai Chen, Yizhou Yu
Date: 2024-03-26 00:00:00
Proceeding: CVPR 2024 1
Tasks: Vision and Language Navigation

Recent advances in Iterative Vision-and-Language Navigation (IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes. Although the long-term memory aligns better with the persistent nature of the VLN task, it poses more challenges on how to utilize the highly unstructured navigation memory with extremely sparse supervision. Towards this end, we propose OVER-NAV, which aims to go over and beyond the current arts of IVLN techniques. In particular, we propose to incorporate LLMs and open-vocabulary detectors to distill key information and establish correspondence between multi-modal signals. Such a mechanism introduces reliable cross-modal supervision and enables on-the-fly generalization to unseen scenes without the need of extra annotation and re-training. To fully exploit the interpreted navigation data, we further introduce a structured representation, coded Omnigraph, to effectively integrate multi-modal information along the tour. Accompanied with a novel omnigraph fusion mechanism, OVER-NAV is able to extract the most relevant knowledge from omnigraph for a more accurate navigating action. In addition, OVER-NAV seamlessly supports both discrete and continuous environments under a unified framework. We demonstrate the superiority of OVER-NAV in extensive experiments.
Labeling subtypes in a Parkinson's Cohort using Multifeatures in MRI - Integrating Grey and White Matter Information
Authors: Tanmayee Samantaray, Jitender Saini, Pramod Kumar Pal, Bithiah Grace Jaganathan, Vijaya V Saradhi, Gupta CN
Date: 2024-03-26 00:00:00

Thresholding of networks has long posed a challenge in brain connectivity analysis. Weighted networks are typically binarized using threshold measures to facilitate network analysis. Previous studies on MRI-based brain networks have predominantly utilized density or sparsity-based thresholding techniques, optimized within specific ranges derived from network metrics such as path length, clustering coefficient, and small-world index. Thus, determination of a single threshold value for facilitating comparative analysis of networks remains elusive. To address this, our study introduces Mutual K-Nearest Neighbor (MKNN)-based thresholding for brain network analysis. Here, nearest neighbor selection is based on the highest correlation between features of brain regions. Construction of brain networks was accomplished by computing Pearson correlations between grey matter volume and white matter volume for each pair of brain regions. Structural MRI data from 180 Parkinsons patients and 70 controls from the NIMHANS, India were analyzed. Subtypes within Parkinsons disease were identified based on grey and white matter volume atrophy using source-based morphometric decomposition. The loading coefficients were correlated with clinical features to discern clinical relationship with the deciphered subtypes. Our data-mining approach revealed: Subtype A (N = 51, intermediate type), Subtype B (N = 57, mild-severe type with mild motor symptoms), and Subtype AB (N = 36, most-severe type with predominance in motor impairment). Subtype-specific weighted matrices were binarized using MKNN-based thresholding for brain network analysis. Permutation tests on network metrics of resulting bipartite graphs demonstrated significant group differences in betweenness centrality and participation coefficient. The identified hubs were specific to each subtype, with some hubs conserved across different subtypes.
Staircase Localization for Autonomous Exploration in Urban Environments
Authors: Jinrae Kim, Sunggoo Jung, Sung-Kyun Kim, Youdan Kim, Ali-akbar Agha-mohammadi
Date: 2024-03-26 00:00:00
Tasks: Line Segment Detection, object-detection, Object Detection

A staircase localization method is proposed for robots to explore urban environments autonomously. The proposed method employs a modular design in the form of a cascade pipeline consisting of three modules of stair detection, line segment detection, and stair localization modules. The stair detection module utilizes an object detection algorithm based on deep learning to generate a region of interest (ROI). From the ROI, line segment features are extracted using a deep line segment detection algorithm. The extracted line segments are used to localize a staircase in terms of position, orientation, and stair direction. The stair detection and localization are performed only with a single RGB-D camera. Each component of the proposed pipeline does not need to be designed particularly for staircases, which makes it easy to maintain the whole pipeline and replace each component with state-of-the-art deep learning detection techniques. The results of real-world experiments show that the proposed method can perform accurate stair detection and localization during autonomous exploration for various structured and unstructured upstairs and downstairs with shadows, dirt, and occlusions by artificial and natural objects.
Accuracy enhancement method for speech emotion recognition from spectrogram using temporal frequency correlation and positional information learning through knowledge transfer
Authors: Jeong-Yoon Kim, Seung-Ho Lee
Date: 2024-03-26 00:00:00
Proceeding: IEEE Access 2024 8
Tasks: Emotion Recognition, Speech Emotion Recognition, Transfer Learning

In this paper, we propose a method to improve the accuracy of speech emotion recognition (SER) by using vision transformer (ViT) to attend to the correlation of frequency (y-axis) with time (x-axis) in spectrogram and transferring positional information between ViT through knowledge transfer. The proposed method has the following originality i) We use vertically segmented patches of log-Mel spectrogram to analyze the correlation of frequencies over time. This type of patch allows us to correlate the most relevant frequencies for a particular emotion with the time they were uttered. ii) We propose the use of image coordinate encoding, an absolute positional encoding suitable for ViT. By normalizing the x, y coordinates of the image to -1 to 1 and concatenating them to the image, we can effectively provide valid absolute positional information for ViT. iii) Through feature map matching, the locality and location information of the teacher network is effectively transmitted to the student network. Teacher network is a ViT that contains locality of convolutional stem and absolute position information through image coordinate encoding, and student network is a structure that lacks positional encoding in the basic ViT structure. In feature map matching stage, we train through the mean absolute error (L1 loss) to minimize the difference between the feature maps of the two networks. To validate the proposed method, three emotion datasets (SAVEE, EmoDB, and CREMA-D) consisting of speech were converted into log-Mel spectrograms for comparison experiments. The experimental results show that the proposed method significantly outperforms the state-of-the-art methods in terms of weighted accuracy while requiring significantly fewer floating point operations (FLOPs). Overall, the proposed method offers an promising solution for SER by providing improved efficiency and performance.
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving
Authors: Junhao Zheng, Chenhao Lin, Jiahao Sun, Zhengyu Zhao, Qian Li, Chao Shen
Date: 2024-03-26 00:00:00
Proceeding: CVPR 2024 1
Tasks: Adversarial Attack, Autonomous Driving, Depth Estimation, Monocular Depth Estimation

Deep learning-based monocular depth estimation (MDE), extensively applied in autonomous driving, is known to be vulnerable to adversarial attacks. Previous physical attacks against MDE models rely on 2D adversarial patches, so they only affect a small, localized region in the MDE map but fail under various viewpoints. To address these limitations, we propose 3D Depth Fool (3D$^2$Fool), the first 3D texture-based adversarial attack against MDE models. 3D$^2$Fool is specifically optimized to generate 3D adversarial textures agnostic to model types of vehicles and to have improved robustness in bad weather conditions, such as rain and fog. Experimental results validate the superior performance of our 3D$^2$Fool across various scenarios, including vehicles, MDE models, weather conditions, and viewpoints. Real-world experiments with printed 3D textures on physical vehicle models further demonstrate that our 3D$^2$Fool can cause an MDE error of over 10 meters.
Tracing and segmentation of molecular patterns in 3-dimensional cryo-et/em density maps through algorithmic image processing and deep learning-based techniques
Authors: Salim Sazzed
Date: 2024-03-26 00:00:00
Tasks: Electron Tomography

Understanding the structures of biological macromolecules is highly important as they are closely associated with cellular functionalities. Comprehending the precise organization actin filaments is crucial because they form the dynamic cytoskeleton, which offers structural support to cells and connects the cell's interior with its surroundings. However, determining the precise organization of actin filaments is challenging due to the poor quality of cryo-electron tomography (cryo-ET) images, which suffer from low signal-to-noise (SNR) ratios and the presence of missing wedge, as well as diverse shape characteristics of actin filaments. To address these formidable challenges, the primary component of this dissertation focuses on developing sophisticated computational techniques for tracing actin filaments. In particular, three novel methodologies have been developed: i) BundleTrac, for tracing bundle-like actin filaments found in Stereocilium, ii) Spaghetti Tracer, for tracing filaments that move individually with loosely cohesive movements, and iii) Struwwel Tracer, for tracing randomly orientated actin filaments in the actin network. The second component of the dissertation introduces a convolutional neural network (CNN) based segmentation model to determine the location of protein secondary structures, such as helices and beta-sheets, in medium-resolution (5-10 Angstrom) 3-dimensional cryo-electron microscopy (cryo-EM) images. This methodology later evolved into a tool named DeepSSETracer. The final component of the dissertation presents a novel algorithm, cylindrical fit measure, to estimate image structure match at helix regions in medium-resolution cryo-EM images. Overall, my dissertation has made significant contributions to addressing critical research challenges in structural biology by introducing various computational methods and tools.
Decoding the visual attention of pathologists to reveal their level of expertise
Authors: Souradeep Chakraborty, Dana Perez, Paul Friedman, Natallia Sheuka, Constantin Friedman, Oksana Yaskiv, Rajarsi Gupta, Gregory J. Zelinsky, Joel H. Saltz, Dimitris Samaras
Date: 2024-03-25 00:00:00
Tasks: whole slide images

We present a method for classifying the expertise of a pathologist based on how they allocated their attention during a cancer reading. We engage this decoding task by developing a novel method for predicting the attention of pathologists as they read whole-slide Images (WSIs) of prostate and make cancer grade classifications. Our ground truth measure of a pathologists' attention is the x, y and z (magnification) movement of their viewport as they navigated through WSIs during readings, and to date we have the attention behavior of 43 pathologists reading 123 WSIs. These data revealed that specialists have higher agreement in both their attention and cancer grades compared to general pathologists and residents, suggesting that sufficient information may exist in their attention behavior to classify their expertise level. To attempt this, we trained a transformer-based model to predict the visual attention heatmaps of resident, general, and specialist (GU) pathologists during Gleason grading. Based solely on a pathologist's attention during a reading, our model was able to predict their level of expertise with 75.3%, 56.1%, and 77.2% accuracy, respectively, better than chance and baseline models. Our model therefore enables a pathologist's expertise level to be easily and objectively evaluated, important for pathology training and competency assessment. Tools developed from our model could also be used to help pathology trainees learn how to read WSIs like an expert.
AnimateMe: 4D Facial Expressions via Diffusion Models
Authors: Dimitrios Gerogiannis, Foivos Paraperas Papantoniou, Rolandos Alexandros Potamias, Alexandros Lattas, Stylianos Moschoglou, Stylianos Ploumpis, Stefanos Zafeiriou
Date: 2024-03-25 00:00:00
Tasks: Denoising, Facial expression generation

The field of photorealistic 3D avatar reconstruction and generation has garnered significant attention in recent years; however, animating such avatars remains challenging. Recent advances in diffusion models have notably enhanced the capabilities of generative models in 2D animation. In this work, we directly utilize these models within the 3D domain to achieve controllable and high-fidelity 4D facial animation. By integrating the strengths of diffusion processes and geometric deep learning, we employ Graph Neural Networks (GNNs) as denoising diffusion models in a novel approach, formulating the diffusion process directly on the mesh space and enabling the generation of 3D facial expressions. This facilitates the generation of facial deformations through a mesh-diffusion-based model. Additionally, to ensure temporal coherence in our animations, we propose a consistent noise sampling method. Under a series of both quantitative and qualitative experiments, we showcase that the proposed method outperforms prior work in 4D expression synthesis by generating high-fidelity extreme expressions. Furthermore, we applied our method to textured 4D facial expression generation, implementing a straightforward extension that involves training on a large-scale textured 4D facial expression database.
Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models
Authors: Fiona R. Kolbinger, Jiangpeng He, Jinge Ma, Fengqing Zhu
Date: 2024-03-25 00:00:00
Tasks: Anatomy, Image Segmentation, Segmentation, Semantic Segmentation, Specificity

Accurate identification and localization of anatomical structures of varying size and appearance in laparoscopic imaging are necessary to leverage the potential of computer vision techniques for surgical decision support. Segmentation performance of such models is traditionally reported using metrics of overlap such as IoU. However, imbalanced and unrealistic representation of classes in the training data and suboptimal selection of reported metrics have the potential to skew nominal segmentation performance and thereby ultimately limit clinical translation. In this work, we systematically analyze the impact of class characteristics (i.e., organ size differences), training and test data composition (i.e., representation of positive and negative examples), and modeling parameters (i.e., foreground-to-background class weight) on eight segmentation metrics: accuracy, precision, recall, IoU, F1 score (Dice Similarity Coefficient), specificity, Hausdorff Distance, and Average Symmetric Surface Distance. Our findings support two adjustments to account for data biases in surgical data science: First, training on datasets that are similar to the clinical real-world scenarios in terms of class distribution, and second, class weight adjustments to optimize segmentation model performance with regard to metrics of particular relevance in the respective clinical setting.
LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning
Authors: Siyuan Cheng, Guanhong Tao, Yingqi Liu, Guangyu Shen, Shengwei An, Shiwei Feng, Xiangzhe Xu, Kaiyuan Zhang, Shiqing Ma, Xiangyu Zhang
Date: 2024-03-25 00:00:00
Proceeding: CVPR 2024 1
Tasks: Backdoor Attack

Backdoor attack poses a significant security threat to Deep Learning applications. Existing attacks are often not evasive to established backdoor detection techniques. This susceptibility primarily stems from the fact that these attacks typically leverage a universal trigger pattern or transformation function, such that the trigger can cause misclassification for any input. In response to this, recent papers have introduced attacks using sample-specific invisible triggers crafted through special transformation functions. While these approaches manage to evade detection to some extent, they reveal vulnerability to existing backdoor mitigation techniques. To address and enhance both evasiveness and resilience, we introduce a novel backdoor attack LOTUS. Specifically, it leverages a secret function to separate samples in the victim class into a set of partitions and applies unique triggers to different partitions. Furthermore, LOTUS incorporates an effective trigger focusing mechanism, ensuring only the trigger corresponding to the partition can induce the backdoor behavior. Extensive experimental results show that LOTUS can achieve high attack success rate across 4 datasets and 7 model structures, and effectively evading 13 backdoor detection and mitigation techniques. The code is available at https://github.com/Megum1/LOTUS.
Deep models for stroke segmentation: do complex architectures always perform better?
Authors: Yalda Zafari-Ghadim, Ahmed Soliman, Yousif Yousif, Ahmed Ibrahim, Essam A. Rashed, Mohamed Mabrok
Date: 2024-03-25 00:00:00
Tasks: Deep Learning, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Stroke segmentation plays a crucial role in the diagnosis and treatment of stroke patients by providing spatial information about affected brain regions and the extent of damage. Segmenting stroke lesions accurately is a challenging task, given that conventional manual techniques are time consuming and prone to errors. Recently, advanced deep models have been introduced for general medical image segmentation, demonstrating promising results that surpass many state of the art networks when evaluated on specific datasets. With the advent of the vision Transformers, several models have been introduced based on them, while others have aimed to design better modules based on traditional convolutional layers to extract long-range dependencies like Transformers. The question of whether such high-level designs are necessary for all segmentation cases to achieve the best results remains unanswered. In this study, we selected four types of deep models that were recently proposed and evaluated their performance for stroke segmentation: a pure Transformer-based architecture (DAE-Former), two advanced CNN-based models (LKA and DLKA) with attention mechanisms in their design, an advanced hybrid model that incorporates CNNs with Transformers (FCT), and the well-known self-adaptive nnUNet framework with its configuration based on given data. We examined their performance on two publicly available datasets, and found that the nnUNet achieved the best results with the simplest design among all. Revealing the robustness issue of Transformers to such variabilities serves as a potential reason for their weaker performance. Furthermore, nnUNet's success underscores the significant impact of preprocessing and postprocessing techniques in enhancing segmentation results, surpassing the focus solely on architectural designs
Histogram Layers for Neural Engineered Features
Authors: Joshua Peeples, Salim Al Kharsa, Luke Saleh, Alina Zare
Date: 2024-03-25 00:00:00
Tasks: image-classification, Image Classification

In the computer vision literature, many effective histogram-based features have been developed. These engineered features include local binary patterns and edge histogram descriptors among others and they have been shown to be informative features for a variety of computer vision tasks. In this paper, we explore whether these features can be learned through histogram layers embedded in a neural network and, therefore, be leveraged within deep learning frameworks. By using histogram features, local statistics of the feature maps from the convolution neural networks can be used to better represent the data. We present neural versions of local binary pattern and edge histogram descriptors that jointly improve the feature representation and perform image classification. Experiments are presented on benchmark and real-world datasets.