Search Results for author:
Found 15 papers, 6 papers with code
Explainable Action Advising for Multi-Agent Reinforcement Learning
This allows the student to self-reflect on what it has learned, enabling advice generalization and leading to improved sample efficiency and learning performance - even in environments where the teacher is sub-optimal.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
PCL: Proxy-Based Contrastive Learning for Domain Generalization
Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains.
Ranked #17 on
Domain Generalization
on PACS
Leveraging the Graph Structure of Neural Network Training Dynamics
Understanding the training dynamics of deep neural networks (DNNs) is important as it can lead to improved training efficiency and task performance.
Prior Guided Feature Enrichment Network for Few-Shot Segmentation
It consists of novel designs of (1) a training-free prior mask generation method that not only retains generalization power but also improves model performance and (2) Feature Enrichment Module (FEM) that overcomes spatial inconsistency by adaptively enriching query features with support features and prior masks.
Ranked #66 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Tensor Low-Rank Reconstruction for Semantic Segmentation
Then we use these rank-1 tensors to recover the high-rank context features through our proposed tensor reconstruction module (TRM).
An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation
In contrast to adversarial alignment, we propose to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space adversarial perturbations.
Federated Learning with Unbiased Gradient Aggregation and Controllable Meta Updating
Federated learning (FL) aims to train machine learning models in the decentralized system consisting of an enormous amount of smart edge devices.
Reflective Decoding Network for Image Captioning
State-of-the-art image captioning methods mostly focus on improving visual features, less attention has been paid to utilizing the inherent properties of language to boost captioning performance.
Ranked #4 on
Image Captioning
on MS COCO
Region Refinement Network for Salient Object Detection
Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection.
2D Attentional Irregular Scene Text Recognizer
Irregular scene text, which has complex layout in 2D space, is challenging to most previous scene text recognizers.
Referring Image Segmentation via Recurrent Refinement Networks
We address the problem of image segmentation from natural language descriptions.
Facelet-Bank for Fast Portrait Manipulation
Digital face manipulation has become a popular and fascinating way to touch images with the prevalence of smartphones and social networks.
Situation Recognition with Graph Neural Networks
We address the problem of recognizing situations in images.
Ranked #9 on
Situation Recognition
on imSitu
Visual Question Answering with Question Representation Update (QRU)
Our method aims at reasoning over natural language questions and visual images.
Video Super-Resolution via Deep Draft-Ensemble Learning
We propose a new direction for fast video super-resolution (VideoSR) via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution.
