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Found 12 papers, 4 papers with code
Background Clustering Pre-training for Few-shot Segmentation
In this paper, we propose a new pre-training scheme for FSS via decoupling the novel classes from background, called Background Clustering Pre-Training (BCPT).
Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment
To achieve this, we introduce a pre-trained LLM to generate context descriptions, and we encourage the prompts to learn from the LLM's knowledge by alignment, as well as the alignment between prompts and local image features.
Relational Contrastive Learning for Scene Text Recognition
We argue that such prior contextual information can be interpreted as the relations of textual primitives due to the heterogeneous text and background, which can provide effective self-supervised labels for representation learning.
SLPD: Slide-level Prototypical Distillation for WSIs
Specifically, we iteratively perform intra-slide clustering for the regions (4096x4096 patches) within each WSI to yield the prototypes and encourage the region representations to be closer to the assigned prototypes.
Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images
This deficiency is a confounder that limits the performance of existing MIL methods.
Solving The Long-Tailed Problem via Intra- and Inter-Category Balance
Benchmark datasets for visual recognition assume that data is uniformly distributed, while real-world datasets obey long-tailed distribution.
Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction
When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy of an instance often depends on not only the instance itself but also its context in the corresponding bag.
Enhancing Non-mass Breast Ultrasound Cancer Classification With Knowledge Transfer
Based on the insight that mass data is sufficient and shares the same knowledge structure with non-mass data of identifying the malignancy of a lesion based on the ultrasound image, we propose a novel transfer learning framework to enhance the generalizability of the DNN model for non-mass BUS with the help of mass BUS.
Self Supervised Lesion Recognition For Breast Ultrasound Diagnosis
Since an ultrasound image only describes a partial 2D projection of a 3D lesion, such paradigm ignores the semantic relationship between different views of a lesion, which is inconsistent with the traditional diagnosis where sonographers analyze a lesion from at least two views.
Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and Curriculum Learning
To avoid overfitting on source domain, at the second stage, we propose a curriculum learning strategy to adaptively control the weighting between losses from the two domains so that the focus of the training stage is gradually shifted from source distribution to target distribution with prediction confidence boosted on the target domain.
MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response
Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy.
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Text-To-Speech Synthesis
on 20000 utterances
(using extra training data)
Decoupled Gradient Harmonized Detector for Partial Annotation: Application to Signet Ring Cell Detection
Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients.
