Search Results for author:
Found 18 papers, 6 papers with code
Sparse Double Descent: Where Network Pruning Aggravates Overfitting
People usually believe that network pruning not only reduces the computational cost of deep networks, but also prevents overfitting by decreasing model capacity.
Reasoning with Multi-Structure Commonsense Knowledge in Visual Dialog
In our model, the external knowledge is represented with sentence-level facts and graph-level facts, to properly suit the scenario of the composite of dialog history and image.
Can network pruning benefit deep learning under label noise?
Network pruning is a widely-used technique to reduce the computational cost of over-parameterized neural networks.
KBGN: Knowledge-Bridge Graph Network for Adaptive Vision-Text Reasoning in Visual Dialogue
Visual dialogue is a challenging task that needs to extract implicit information from both visual (image) and textual (dialogue history) contexts.
DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue
The ability of generating detailed and non-repetitive responses is crucial for the agent to achieve human-like conversation.
Multi-Level Network for High-Speed Multi-Person Pose Estimation
In multi-person pose estimation, the left/right joint type discrimination is always a hard problem because of the similar appearance.
FollowMeUp Sports: New Benchmark for 2D Human Keypoint Recognition
Human pose estimation has made significant advancement in recent years.
DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue
More importantly, we can tell which modality (visual or semantic) has more contribution in answering the current question by visualizing the gate values.
Ranked #6 on
Visual Dialog
on VisDial v0.9 val
Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering
Upon the constructed graph, we propose a Scene Graph Convolutional Network (SceneGCN) to jointly reason the object properties and relational semantics for the correct answer.
A sequential guiding network with attention for image captioning
The recent advances of deep learning in both computer vision (CV) and natural language processing (NLP) provide us a new way of understanding semantics, by which we can deal with more challenging tasks such as automatic description generation from natural images.
Pixel Level Data Augmentation for Semantic Image Segmentation using Generative Adversarial Networks
Semantic segmentation is one of the basic topics in computer vision, it aims to assign semantic labels to every pixel of an image.
Textual Relationship Modeling for Cross-Modal Information Retrieval
A joint neural model is proposed to learn feature representation individually in each modality.
Multimedia
Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval
A dual-path neural network model is proposed for couple feature learning in cross-modal information retrieval.
Text Generation Based on Generative Adversarial Nets with Latent Variable
We propose the VGAN model where the generative model is composed of recurrent neural network and VAE.
Data Augmentation in Emotion Classification Using Generative Adversarial Networks
In this paper, we propose a data augmentation method using generative adversarial networks (GAN).
Logical Parsing from Natural Language Based on a Neural Translation Model
Under our learning policy, the Seq2Seq model can learn mappings gradually with noises.
Generative Cooperative Net for Image Generation and Data Augmentation
In this paper, we explore a generative model for the task of generating unseen images with desired features.
Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks
Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a potential application in digital entertainment.
