Search Results for author:
Found 9 papers, 5 papers with code
TEXTOC: Text-driven Object-Centric Style Transfer
It ensures a seamless and harmonious style transfer across object regions.
Context-Aware Video Instance Segmentation
In this paper, we introduce the Context-Aware Video Instance Segmentation (CAVIS), a novel framework designed to enhance instance association by integrating contextual information adjacent to each object.
Ranked #1 on
Video Instance Segmentation
on OVIS validation
(using extra training data)
NuTrea: Neural Tree Search for Context-guided Multi-hop KGQA
Multi-hop Knowledge Graph Question Answering (KGQA) is a task that involves retrieving nodes from a knowledge graph (KG) to answer natural language questions.
Offline-to-Online Knowledge Distillation for Video Instance Segmentation
In this paper, we present offline-to-online knowledge distillation (OOKD) for video instance segmentation (VIS), which transfers a wealth of video knowledge from an offline model to an online model for consistent prediction.
Metropolis-Hastings Data Augmentation for Graph Neural Networks
We also propose a simple and effective semi-supervised learning strategy with generated samples from MH-Aug. Our extensive experiments demonstrate that MH-Aug can generate a sequence of samples according to the target distribution to significantly improve the performance of GNNs.
ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation
In this paper, we present a direct adaptation strategy (ADAS), which aims to directly adapt a single model to multiple target domains in a semantic segmentation task without pretrained domain-specific models.
Ranked #3 on
Domain Adaptation
on GTAV to Cityscapes+Mapillary
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs.
DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation
Our model encodes individual representations of content (scene structure) and style (artistic appearance) from both source and target images.
Ranked #1 on
Domain Adaptation
on MNIST-to-MNIST-M
Building a Part-of-Speech Tagged Corpus for Drenjongke (Bhutia)
This research paper reports on the generation of the first Drenjongke corpus based on texts taken from a phrase book for beginners, written in the Tibetan script.
