Search Results for author:
Found 21 papers, 13 papers with code
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
The versatile toolkit also fosters technique sharing across different text generation tasks.
Differentiable Expected BLEU for Text Generation
Neural text generation models such as recurrent networks are typically trained by maximizing data log-likelihood based on cross entropy.
Data-to-Text Generation with Style Imitation
That is, the model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the content record.
Representation Learning from Limited Educational Data with Crowdsourced Labels
Extensive experiments conducted on three real-world data sets demonstrate the superiority of our framework on learning representations from limited data with crowdsourced labels, comparing with various state-of-the-art baselines.
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning
Extensive experiments on two real-world conversation datasets show that our framework significantly reduces gender bias in dialogue models while maintaining the response quality.
Dense Label Encoding for Boundary Discontinuity Free Rotation Detection
Rotation detection serves as a fundamental building block in many visual applications involving aerial image, scene text, and face etc.
Ranked #29 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Parallel Multi-Resolution Fusion Network for Image Inpainting
Conventional deep image inpainting methods are based on auto-encoder architecture, in which the spatial details of images will be lost in the down-sampling process, leading to the degradation of generated results.
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design.
Ranked #16 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence
Taking the perspective that horizontal detection is a special case for rotated object detection, in this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology, in terms of the relation between rotation and horizontal detection.
Ranked #14 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Towards the Memorization Effect of Neural Networks in Adversarial Training
In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples.
Imbalanced Adversarial Training with Reweighting
Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks.
Dual-Path Image Inpainting With Auxiliary GAN Inversion
Different from feed-forward methods, they seek for a closest latent code to the corrupted image and feed it to a pretrained generator.
The KFIoU Loss for Rotated Object Detection
This is in contrast to recent Gaussian modeling based rotation detectors e. g. GWD loss and KLD loss that involve a human-specified distribution distance metric which require additional hyperparameter tuning that vary across datasets and detectors.
Enhancing Adversarial Training with Feature Separability
Deep Neural Network (DNN) are vulnerable to adversarial attacks.
Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization
Existing detection methods commonly use a parameterized bounding box (BBox) to model and detect (horizontal) objects and an additional rotation angle parameter is used for rotated objects.
Toward Degree Bias in Embedding-Based Knowledge Graph Completion
It aims to predict unseen edges by learning representations for all the entities and relations in a KG.
MocFormer: A Two-Stage Pre-training-Driven Transformer for Drug-Target Interactions Prediction
Drug-target interactions (DTIs) is essential for advancing pharmaceuticals.
DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated Content
This paper explores the image synthesis capabilities of GPT-4, a leading multi-modal large language model.
Self-supervised learning of video representations from a child's perspective
These results suggest that important temporal aspects of a child's internal model of the world may be learnable from their visual experience using highly generic learning algorithms and without strong inductive biases.
A systematic investigation of learnability from single child linguistic input
However, a significant gap exists between the training data for these models and the linguistic input a child receives.
CosmicMan: A Text-to-Image Foundation Model for Humans
We present CosmicMan, a text-to-image foundation model specialized for generating high-fidelity human images.
