Search Results for author:
Found 9 papers, 4 papers with code
URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark
We propose a unified referring video object segmentation network (URVOS).
Ranked #6 on
Referring Expression Segmentation
on DAVIS 2017 (val)
(using extra training data)
Relaxed Contrastive Learning for Federated Learning
We propose a novel contrastive learning framework to effectively address the challenges of data heterogeneity in federated learning.
Online Backfilling with No Regret for Large-Scale Image Retrieval
Backfilling is the process of re-extracting all gallery embeddings from upgraded models in image retrieval systems.
Information-Theoretic Bias Reduction via Causal View of Spurious Correlation
We propose an information-theoretic bias measurement technique through a causal interpretation of spurious correlation, which is effective to identify the feature-level algorithmic bias by taking advantage of conditional mutual information.
InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering
We present an information-theoretic regularization technique for few-shot novel view synthesis based on neural implicit representation.
Unsupervised Learning of Debiased Representations with Pseudo-Attributes
Dataset bias is a critical challenge in machine learning since it often leads to a negative impact on a model due to the unintended decision rules captured by spurious correlations.
Learning to Optimize Domain Specific Normalization for Domain Generalization
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to individual domains.
Ranked #3 on
Unsupervised Domain Adaptation
on PACS
Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
In the first stage, we estimate pseudo-labels for the examples in the target domain using an external unsupervised domain adaptation algorithm---for example, MSTN or CPUA---integrating the proposed domain-specific batch normalization.
Learning for Single-Shot Confidence Calibration in Deep Neural Networks through Stochastic Inferences
The proposed loss function enables us to learn deep neural networks that predict confidence calibrated scores using a single inference.
