Search Results for author:
Found 27 papers, 22 papers with code
COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0. 90, an AUC of 0. 98, and an accuracy of 0. 89.
Anything-3D: Towards Single-view Anything Reconstruction in the Wild
3D reconstruction from a single-RGB image in unconstrained real-world scenarios presents numerous challenges due to the inherent diversity and complexity of objects and environments.
Dataset Distillation via Factorization
In this paper, we study \xw{dataset distillation (DD)}, from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline.
Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans
Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain.
Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection
In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD).
Deep Model Reassembly
Given a collection of heterogeneous models pre-trained from distinct sources and with diverse architectures, the goal of DeRy, as its name implies, is to first dissect each model into distinctive building blocks, and then selectively reassemble the derived blocks to produce customized networks under both the hardware resource and performance constraints.
On the Generation of Medical Dialogues for COVID-19
On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT.
On the Generation of Medical Dialogs for COVID-19
Training complex dialog generation models on small datasets bears high risk of overfitting.
Hash3D: Training-free Acceleration for 3D Generation
The evolution of 3D generative modeling has been notably propelled by the adoption of 2D diffusion models.
Factorizing Knowledge in Neural Networks
The core idea of KF lies in the modularization and assemblability of knowledge: given a pretrained network model as input, KF aims to decompose it into several factor networks, each of which handles only a dedicated task and maintains task-specific knowledge factorized from the source network.
SG-Former: Self-guided Transformer with Evolving Token Reallocation
At the heart of our approach is to utilize a significance map, which is estimated through hybrid-scale self-attention and evolves itself during training, to reallocate tokens based on the significance of each region.
Single View Metrology in the Wild
Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity.
Dataset Factorization for Condensation
In this paper, we study dataset distillation (DD), from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline.
Diffusion Model as Representation Learner
In this paper, we conduct an in-depth investigation of the representation power of DPMs, and propose a novel knowledge transfer method that leverages the knowledge acquired by generative DPMs for recognition tasks.
Distribution Shift Inversion for Out-of-Distribution Prediction
Machine learning society has witnessed the emergence of a myriad of Out-of-Distribution (OoD) algorithms, which address the distribution shift between the training and the testing distribution by searching for a unified predictor or invariant feature representation.
XRayGAN: Consistency-preserving Generation of X-ray Images from Radiology Reports
To address this problem, we develop methods to generate view-consistent, high-fidelity, and high-resolution X-ray images from radiology reports to facilitate radiology training of medical students.
Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
Learning with Recoverable Forgetting
Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge.
Neural Point Process for Learning Spatiotemporal Event Dynamics
The key construction of our approach is the nonparametric space-time intensity function, governed by a latent process.
Unsegment Anything by Simulating Deformation
Foundation segmentation models, while powerful, pose a significant risk: they enable users to effortlessly extract any objects from any digital content with a single click, potentially leading to copyright infringement or malicious misuse.
Stochastic Gradient Variance Reduction by Solving a Filtering Problem
Deep neural networks (DNN) are typically optimized using stochastic gradient descent (SGD).
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms
There has not been a clear understanding on what properties of data and tasks render one approach outperforms the other.
Neural Point Process for Forecasting Spatiotemporal Events
To the best of our knowledge, this is the first neural point process model that can jointly predict both the space and time of events.
DSRNA: Differentiable Search of Robust Neural Architectures
To address this problem, we propose methods to perform differentiable search of robust neural architectures.
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation
Medical report generation is one of the most challenging tasks in medical image analysis.
Diffusion Probabilistic Model Made Slim
Despite the recent visually-pleasing results achieved, the massive computational cost has been a long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits their applications on resource-limited platforms.
Relation Rectification in Diffusion Model
Despite their exceptional generative abilities, large text-to-image diffusion models, much like skilled but careless artists, often struggle with accurately depicting visual relationships between objects.
