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Found 29 papers, 13 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.
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.
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.
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.
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
Intervention-based Recurrent Casual Model for Non-stationary Video Causal Discovery
Non-stationary casual structures are prevalent in real-world physical systems.
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.
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.
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)
Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation
In this paper, we propose a fast image segmentation method based on a novel super boundary-to-pixel direction (super-BPD) and a customized segmentation algorithm with super-BPD.
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 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.
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.
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.
Learning and Using the Arrow of Time
We seek to understand the arrow of time in videos -- what makes videos look like they are playing forwards or backwards?
Ranked #42 on
Self-Supervised Action Recognition
on UCF101
Video Enhancement with Task-Oriented Flow
Many video enhancement algorithms rely on optical flow to register frames in a video sequence.
Ranked #7 on
Video Frame Interpolation
on Middlebury
Understanding Intra-Class Knowledge Inside CNN
Convolutional Neural Network (CNN) has been successful in image recognition tasks, and recent works shed lights on how CNN separates different classes with the learned inter-class knowledge through visualization.
Seeing the Arrow of Time
We explore whether we can observe Time's Arrow in a temporal sequence--is it possible to tell whether a video is running forwards or backwards?
A Video Representation Using Temporal Superpixels
We develop a generative probabilistic model for temporally consistent superpixels in video sequences.
