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Found 11 papers, 5 papers with code
Intensity Field Decomposition for Tissue-Guided Neural Tomography
The network represents the intensity field as a combination of soft and hard tissue components, along with their respective textures.
DMTG: One-Shot Differentiable Multi-Task Grouping
Given N tasks, we propose to simultaneously identify the best task groups from 2^N candidates and train the model weights simultaneously in one-shot, with the high-order task-affinity fully exploited.
Aux-NAS: Exploiting Auxiliary Labels with Negligibly Extra Inference Cost
We aim at exploiting additional auxiliary labels from an independent (auxiliary) task to boost the primary task performance which we focus on, while preserving a single task inference cost of the primary task.
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL).
Complete Instances Mining for Weakly Supervised Instance Segmentation
To address this problem, we propose a novel approach for WSIS that focuses on the online refinement of complete instances through the use of MaskIoU heads to predict the integrity scores of proposals and a Complete Instances Mining (CIM) strategy to explicitly model the redundant segmentation problem and generate refined pseudo labels.
Ranked #2 on
Weakly-supervised instance segmentation
on PASCAL VOC 2012 val
(mAP@0.25 metric)
Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery
Therefore, to ensure the visibility and integrity of bridges, it is essential to perform holistic bridge detection in large-size very-high-resolution (VHR) RSIs.
Context-Aware Selective Label Smoothing for Calibrating Sequence Recognition Model
Despite the success of deep neural network (DNN) on sequential data (i. e., scene text and speech) recognition, it suffers from the over-confidence problem mainly due to overfitting in training with the cross-entropy loss, which may make the decision-making less reliable.
Deep Graph Matching under Quadratic Constraint
Recently, deep learning based methods have demonstrated promising results on the graph matching problem, by relying on the descriptive capability of deep features extracted on graph nodes.
Ranked #8 on
Graph Matching
on Willow Object Class
FGN: Fully Guided Network for Few-Shot Instance Segmentation
Few-shot instance segmentation (FSIS) conjoins the few-shot learning paradigm with general instance segmentation, which provides a possible way of tackling instance segmentation in the lack of abundant labeled data for training.
Zero-Assignment Constraint for Graph Matching with Outliers
Graph matching (GM), as a longstanding problem in computer vision and pattern recognition, still suffers from numerous cluttered outliers in practical applications.
Exemplar-based Linear Discriminant Analysis for Robust Object Tracking
However, the task in tracking is to search for a specific object, rather than an object category as in detection.
