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Found 69 papers, 24 papers with code
Two Stream Active Query Suggestion for Active Learning in Connectomics
As a use case, we build an end-to-end active learning framework with our query suggestion method for 3D synapse detection and mitochondria segmentation in connectomics.
What makes domain generalization hard?
Here we present SemanticDG (Semantic Domain Generalization): a benchmark with 15 photo-realistic domains with the same geometry, scene layout and camera parameters as the popular 3D ScanNet dataset, but with controlled domain shifts in lighting, materials, and viewpoints.
Diagnosing Ensemble Few-Shot Classifiers
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance.
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI).
Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN
Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming.
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
Simultaneously, PNGAN establishes a pixel-level adversarial training to conduct noise domain alignment.
Revisiting RCAN: Improved Training for Image Super-Resolution
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight.
Texture-Based Error Analysis for Image Super-Resolution
Our key contribution is to leverage a texture classifier, which enables us to assign patches with semantic labels, to identify the source of SR errors both globally and locally.
YouMVOS: An Actor-Centric Multi-Shot Video Object Segmentation Dataset
Many video understanding tasks require analyzing multi-shot videos, but existing datasets for video object segmentation (VOS) only consider single-shot videos.
PyTorch Connectomics: A Scalable and Flexible Segmentation Framework for EM Connectomics
We present PyTorch Connectomics (PyTC), an open-source deep-learning framework for the semantic and instance segmentation of volumetric microscopy images, built upon PyTorch.
Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation
Motivated by this, we propose a video instance segmentation method that alleviates the problem due to missing detections.
Ranked #16 on
Video Instance Segmentation
on YouTube-VIS validation
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available.
MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification
We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D.
GenNI: Human-AI Collaboration for Data-Backed Text Generation
These controls allow users to globally constrain model generations, without sacrificing the representation power of the deep learning models.
Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data
Inspection of tissues using a light microscope is the primary method of diagnosing many diseases, notably cancer.
Intervention-based Recurrent Casual Model for Non-stationary Video Causal Discovery
Non-stationary casual structures are prevalent in real-world physical systems.
To Which Out-Of-Distribution Object Orientations Are DNNs Capable of Generalizing?
In contrast, for DNNs, it remains unknown how generalization abilities are distributed among OoD orientations.
Asymmetric 3D Context Fusion for Universal Lesion Detection
The proposed A3D consistently outperforms symmetric context fusion operators by considerable margins, and establishes a new \emph{state of the art} on DeepLesion.
RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans
Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive, as 24 ribs are typically elongated and oblique in 3D volumes.
NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale
Segmenting 3D cell nuclei from microscopy image volumes is critical for biological and clinical analysis, enabling the study of cellular expression patterns and cell lineages.
Developmental Stage Classification of EmbryosUsing Two-Stream Neural Network with Linear-Chain Conditional Random Field
In this work, we propose a two-stream model for developmental stage classification.
AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions
Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries.
Small in-distribution changes in 3D perspective and lighting fool both CNNs and Transformers
Despite training with a large-scale (0. 5 million images), unbiased dataset of camera and light variations, in over 71% cases CMA-Search can find camera parameters in the vicinity of a correctly classified image which lead to in-distribution misclassifications with < 3. 6% change in parameters.
VICE: Visual Identification and Correction of Neural Circuit Errors
A connectivity graph of neurons at the resolution of single synapses provides scientists with a tool for understanding the nervous system in health and disease.
Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
Guided depth super-resolution (GDSR) is an essential topic in multi-modal image processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones collected with suboptimal conditions with the help of HR RGB images of the same scene.
PhotoApp: Photorealistic Appearance Editing of Head Portraits
We present an approach for high-quality intuitive editing of the camera viewpoint and scene illumination in a portrait image.
Consistent Recurrent Neural Networks for 3D Neuron Segmentation
We present a recurrent network for the 3D reconstruction of neurons that sequentially generates binary masks for every object in an image with spatio-temporal consistency.
Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution
Specifically, we propose a dynamic high-pass filtering (HPF) module that locally applies adaptive filter weights for each spatial location and channel group to preserve high-frequency signals.
Context Reasoning Attention Network for Image Super-Resolution
However, the basic convolutional layer in CNNs is designed to extract local patterns, lacking the ability to model global context.
On the Capability of CNNs to Generalize to Unseen Category-Viewpoint Combinations
We find that learning category and viewpoint in separate networks compared to a shared one leads to an increase in selectivity and invariance, as separate networks are not forced to preserve information about both category and viewpoint.
Learning Vector Quantized Shape Code for Amodal Blastomere Instance Segmentation
To alleviate the problem, we propose to classify input features into intermediate shape codes and recover complete object shapes from them.
MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM Images
On MitoEM, we find existing instance segmentation methods often fail to correctly segment mitochondria with complex shapes or close contacts with other instances.
Ranked #2 on
3D Instance Segmentation
on MitoEM
(AP75-R-Test metric)
Monocular Reconstruction of Neural Face Reflectance Fields
The reflectance field of a face describes the reflectance properties responsible for complex lighting effects including diffuse, specular, inter-reflection and self shadowing.
When and how CNNs generalize to out-of-distribution category-viewpoint combinations
In this paper, we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and 3D viewpoint on OOD combinations, and identifying the neural mechanisms that facilitate such OOD generalization.
Automated Measurements of Key Morphological Features of Human Embryos for IVF
A major challenge in clinical In-Vitro Fertilization (IVF) is selecting the highest quality embryo to transfer to the patient in the hopes of achieving a pregnancy.
A New Age of Computing and the Brain
In December 2014, a two-day workshop supported by the Computing Community Consortium (CCC) and the National Science Foundation's Computer and Information Science and Engineering Directorate (NSF CISE) was convened in Washington, DC, with the goal of bringing together computer scientists and brain researchers to explore these new opportunities and connections, and develop a new, modern dialogue between the two research communities.
A Topological Nomenclature for 3D Shape Analysis in Connectomics
Next, we develop nomenclature rules for pyramidal neurons and mitochondria from the reduced graph and finally learn the feature embedding for shape manipulation.
White-Box Adversarial Defense via Self-Supervised Data Estimation
In this paper, we study the problem of how to defend classifiers against adversarial attacks that fool the classifiers using subtly modified input data.
FDive: Learning Relevance Models using Pattern-based Similarity Measures
The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity.
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference
Automation of tasks can have critical consequences when humans lose agency over decision processes.
Fast Mitochondria Detection for Connectomics
High-resolution connectomics data allows for the identification of dysfunctional mitochondria which are linked to a variety of diseases such as autism or bipolar.
Debugging Sequence-to-Sequence Models with Seq2Seq-Vis
Neural attention-based sequence-to-sequence models (seq2seq) (Sutskever et al., 2014; Bahdanau et al., 2014) have proven to be accurate and robust for many sequence prediction tasks.
Parallel Separable 3D Convolution for Video and Volumetric Data Understanding
For video and volumetric data understanding, 3D convolution layers are widely used in deep learning, however, at the cost of increasing computation and training time.
Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics
While automatic text extraction works well on infographics, computer vision approaches trained on natural images fail to identify the stand-alone visual elements in infographics, or `icons'.
Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets
The few methods that computes direction along with contact location have only been demonstrated to work on either dyadic (most common in vertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on both types.
Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models
In this work, we present a visual analysis tool that allows interaction with a trained sequence-to-sequence model through each stage of the translation process.
Learning to Super-Resolve Blurry Face and Text Images
We present an algorithm to directly restore a clear high-resolution image from a blurry low-resolution input.
Understanding Infographics through Textual and Visual Tag Prediction
And second, we use these predicted text tags as a supervisory signal to localize the most diagnostic visual elements from within the infographic i. e. visual hashtags.
Learning Visual Importance for Graphic Designs and Data Visualizations
Our models are neural networks trained on human clicks and importance annotations on hundreds of designs.
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals.
Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks
Leveraging recent work on learning Bayesian neural networks, we build fast, scalable algorithms for inferring the posterior distribution over all network weights in the hierarchy.
Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking
Large databases are often organized by hand-labeled metadata, or criteria, which are expensive to collect.
Morphological Error Detection in 3D Segmentations
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology.
Guided Proofreading of Automatic Segmentations for Connectomics
Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading.
BubbleView: an interface for crowdsourcing image importance maps and tracking visual attention
In this paper, we present BubbleView, an alternative methodology for eye tracking using discrete mouse clicks to measure which information people consciously choose to examine.
A Multi-Pass Approach to Large-Scale Connectomics
The field of connectomics faces unprecedented "big data" challenges.
RhoanaNet Pipeline: Dense Automatic Neural Annotation
The best segmentation results obtained gave $V^\text{Info}_\text{F-score}$ scores of 0. 9054 and 09182 for the cortex datasets, 0. 9438 for LGN, and 0. 9150 for Cerebellum.
Icon: An Interactive Approach to Train Deep Neural Networks for Segmentation of Neuronal Structures
We present an interactive approach to train a deep neural network pixel classifier for the segmentation of neuronal structures.
LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
In this work, we present LSTMVIS, a visual analysis tool for recurrent neural networks with a focus on understanding these hidden state dynamics.
Blind Image Deblurring Using Dark Channel Prior
Therefore, enforcing the sparsity of the dark channel helps blind deblurring on various scenarios, including natural, face, text, and low-illumination images.
Ranked #6 on
Deblurring
on RealBlur-R (trained on GoPro)
Context-guided diffusion for label propagation on graphs
Existing approaches for diffusion on graphs, e. g., for label propagation, are mainly focused on isotropic diffusion, which is induced by the commonly-used graph Laplacian regularizer.
Local High-order Regularization on Data Manifolds
The iterated graph Laplacian enables high-order regularization, but it has a high computational complexity and so cannot be applied to large problems.
Semi-supervised Learning with Explicit Relationship Regularization
In many learning tasks, the structure of the target space of a function holds rich information about the relationships between evaluations of functions on different data points.
Layered RGBD Scene Flow Estimation
As consumer depth sensors become widely available, estimating scene flow from RGBD sequences has received increasing attention.
Local Layering for Joint Motion Estimation and Occlusion Detection
To handle such situations, we propose a local layering model where motion and occlusion relationships are inferred jointly.
VESICLE: Volumetric Evaluation of Synaptic Interfaces using Computer vision at Large Scale
An open challenge problem at the forefront of modern neuroscience is to obtain a comprehensive mapping of the neural pathways that underlie human brain function; an enhanced understanding of the wiring diagram of the brain promises to lead to new breakthroughs in diagnosing and treating neurological disorders.
Reconstructing Loopy Curvilinear Structures Using Integer Programming
We propose a novel approach to automated delineation of linear structures that form complex and potentially loopy networks.
A Fully-Connected Layered Model of Foreground and Background Flow
Layered models allow scene segmentation and motion estimation to be formulated together and to inform one another.
Large-Scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images
Segmentation of large-scale electron microscopy data is the main bottleneck in the analysis of these data sets.
