Search Results for author:
Found 26 papers, 12 papers with code
BADGR: Bundle Adjustment Diffusion Conditioned by GRadients for Wide-Baseline Floor Plan Reconstruction
Reconstructing precise camera poses and floor plan layouts from wide-baseline RGB panoramas is a difficult and unsolved problem.
Ranked #1 on
Panorama Pose Estimation (N-view)
on ZInd
(Mean Translation Error (1 pano per space) metric)
CrossFusion: A Multi-Scale Cross-Attention Convolutional Fusion Model for Cancer Survival Prediction
Cancer survival prediction from whole slide images (WSIs) is a challenging task in computational pathology due to the large size, irregular shape, and high granularity of the WSIs.
RemInD: Remembering Anatomical Variations for Interpretable Domain Adaptive Medical Image Segmentation
This work presents a novel Bayesian framework for unsupervised domain adaptation (UDA) in medical image segmentation.
PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathology
PathFinder integrates four AI agents, the Triage Agent, Navigation Agent, Description Agent, and Diagnosis Agent, that collaboratively navigate WSIs, gather evidence, and provide comprehensive diagnoses with natural language explanations.
MedicalNarratives: Connecting Medical Vision and Language with Localized Narratives
We propose MedicalNarratives, a dataset curated from medical pedagogical videos similar in nature to data collected in Think-Aloud studies and inspired by Localized Narratives, which collects grounded image-text data by curating instructors' speech and mouse cursor movements synchronized in time.
Generating Seamless Virtual Immunohistochemical Whole Slide Images with Content and Color Consistency
Immunohistochemical (IHC) stains play a vital role in a pathologist's analysis of medical images, providing crucial diagnostic information for various diseases.
Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration
We propose a novel hierarchical variational auto-encoding architecture to realise the inference procedure of the latent variables, where the registration parameters can be explicitly estimated in a mathematically interpretable fashion.
Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos
Training multi-model models for histopathology requires instruction tuning datasets, which currently contain information for individual image patches, without a spatial grounding of the concepts within each patch and without a wider view of the WSI.
Quilt-1M: One Million Image-Text Pairs for Histopathology
From YouTube, we curate QUILT: a large-scale vision-language dataset consisting of $802, 144$ image and text pairs.
Multi-modal Masked Autoencoders Learn Compositional Histopathological Representations
However, the domain shift between natural images and digital pathology images requires further research in designing MAE for patch-level WSIs.
Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer's Disease
On top of BAR, we propose using a soft-label-capable supervised contrastive loss, aiming to learn the relative similarity of representations that reflect how mixed are the synthetic MRIs using our soft labels.
Learning Oculomotor Behaviors from Scanpath
Identifying oculomotor behaviors relevant for eye-tracking applications is a critical but often challenging task.
Semi-supervised Synthesis of High-Resolution Editable Textures for 3D Humans
Given a segmentation mask defining the layout of the semantic regions in the texture map, our network generates high-resolution textures with a variety of styles, that are then used for rendering purposes.
HATNet: An End-to-End Holistic Attention Network for Diagnosis of Breast Biopsy Images
HATNet extends the bag-of-words approach and uses self-attention to encode global information, allowing it to learn representations from clinically relevant tissue structures without any explicit supervision.
Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting
Traditional methods for image-based 3D face reconstruction and facial motion retargeting fit a 3D morphable model (3DMM) to the face, which has limited modeling capacity and fail to generalize well to in-the-wild data.
Sparsely Grouped Input Variables for Neural Networks
In genomic analysis, biomarker discovery, image recognition, and other systems involving machine learning, input variables can often be organized into different groups by their source or semantic category.
Learning Stylized Character Expressions from Humans
We present DeepExpr, a novel expression transfer system from humans to multiple stylized characters via deep learning.
A Facial Affect Analysis System for Autism Spectrum Disorder
In this paper, we introduce an end-to-end machine learning-based system for classifying autism spectrum disorder (ASD) using facial attributes such as expressions, action units, arousal, and valence.
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4. 4% higher accuracy with 6x fewer FLOPs.
Ranked #41 on
Semantic Segmentation
on PASCAL VOC 2012 test
Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images
In this paper, we introduce a conceptually simple network for generating discriminative tissue-level segmentation masks for the purpose of breast cancer diagnosis.
ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints.
Ranked #48 on
Semantic Segmentation
on PASCAL VOC 2012 test
Identifying Most Walkable Direction for Navigation in an Outdoor Environment
We present an approach for identifying the most walkable direction for navigation using a hand-held camera.
Learning to Segment Breast Biopsy Whole Slide Images
We trained and applied an encoder-decoder model to semantically segment breast biopsy images into biologically meaningful tissue labels.
Coherent Parametric Contours for Interactive Video Object Segmentation
Interactive video segmentation systems aim at producing sub-pixel-level object boundaries for visual effect applications.
Generating Notifications for Missing Actions: Don't Forget to Turn the Lights Off!
The overall prediction accuracy is 46. 2% when only 10 frames of an action are seen (2/3 of a sec).
Supervised Semantic Gradient Extraction Using Linear-Time Optimization
This paper proposes a new supervised semantic edge and gradient extraction approach, which allows the user to roughly scribble over the desired region to extract semantically-dominant and coherent edges in it.
