Search Results for author:
Found 12 papers, 2 papers with code
Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment
In this work, we guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process.
Augmenting Vision Language Pretraining by Learning Codebook with Visual Semantics
Language modality within the vision language pretraining framework is innately discretized, endowing each word in the language vocabulary a semantic meaning.
Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs
We study sequential decision making problems aimed at maximizing the expected total reward while satisfying a constraint on the expected total utility.
OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System
While existing methods can be applied for class-wise retrieval (aka.
Multi-modal Alignment using Representation Codebook
Aligning signals from different modalities is an important step in vision-language representation learning as it affects the performance of later stages such as cross-modality fusion.
Vision-Language Pre-Training with Triple Contrastive Learning
Besides CMA, TCL introduces an intra-modal contrastive objective to provide complementary benefits in representation learning.
Ranked #3 on
Zero-Shot Cross-Modal Retrieval
on COCO 2014
Bridging Gap between Image Pixels and Semantics via Supervision: A Survey
The fact that there exists a gap between low-level features and semantic meanings of images, called the semantic gap, is known for decades.
SLADE: A Self-Training Framework For Distance Metric Learning
We first train a teacher model on the labeled data and use it to generate pseudo labels for the unlabeled data.
An Interpretable Generative Model for Handwritten Digit Image Synthesis
An interpretable generative model for handwritten digits synthesis is proposed in this work.
Interpretable Convolutional Neural Networks via Feedforward Design
To construct convolutional layers, we develop a new signal transform, called the Saab (Subspace Approximation with Adjusted Bias) transform.
PortraitGAN for Flexible Portrait Manipulation
Previous methods have dealt with discrete manipulation of facial attributes such as smile, sad, angry, surprise etc, out of canonical expressions and they are not scalable, operating in single modality.
Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for Isolated Gesture Recognition
Recently, the popularity of depth-sensors such as Kinect has made depth videos easily available while its advantages have not been fully exploited.
