Search Results for author:
Found 22 papers, 6 papers with code
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.
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
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.
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.
Textual Relationship Modeling for Cross-Modal Information Retrieval
A joint neural model is proposed to learn feature representation individually in each modality.
Multimedia
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.
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.
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.
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.
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.
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.
FollowMeUp Sports: New Benchmark for 2D Human Keypoint Recognition
Human pose estimation has made significant advancement in recent years.
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.
Multi-Person Pose Estimation
Vocal Bursts Intensity Prediction
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.
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.
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.
Boosting Semantic Segmentation from the Perspective of Explicit Class Embeddings
Following this observation, we propose ECENet, a new segmentation paradigm, in which class embeddings are obtained and enhanced explicitly during interacting with multi-stage image features.
CADReN: Contextual Anchor-Driven Relational Network for Controllable Cross-Graphs Node Importance Estimation
CADReN is also proven to match the performance of previous models on single-graph NIE task.
LogicalDefender: Discovering, Extracting, and Utilizing Common-Sense Knowledge
Experiments show that our model has achieved better logical performance, and the extracted logical knowledge can be effectively applied to other scenarios.
From Image to Video, what do we need in multimodal LLMs?
Multimodal Large Language Models (MLLMs) have demonstrated profound capabilities in understanding multimodal information, covering from Image LLMs to the more complex Video LLMs.
