Search Results for author:
Found 8 papers, 6 papers with code
Greedy Gradient Ensemble for Robust Visual Question Answering
Language bias is a critical issue in Visual Question Answering (VQA), where models often exploit dataset biases for the final decision without considering the image information.
Ranked #2 on
Visual Question Answering (VQA)
on VQA-CP
Edge-featured Graph Neural Architecture Search
Recently, researchers study neural architecture search (NAS) to reduce the dependence of human expertise and explore better GNN architectures, but they over-emphasize entity features and ignore latent relation information concealed in the edges.
General Greedy De-bias Learning
Existing de-bias learning frameworks try to capture specific dataset bias by annotations but they fail to handle complicated OOD scenarios.
Attribute Group Editing for Reliable Few-shot Image Generation
Few-shot image generation is a challenging task even using the state-of-the-art Generative Adversarial Networks (GANs).
Automatic Relation-aware Graph Network Proliferation
However, the currently used graph search space overemphasizes learning node features and neglects mining hierarchical relational information.
Ranked #2 on
Link Prediction
on TSP/HCP Benchmark set
Stable Attribute Group Editing for Reliable Few-shot Image Generation
SAGE takes use of all given few-shot images and estimates a class center embedding based on the category-relevant attribute dictionary.
Open-Set Knowledge-Based Visual Question Answering with Inference Paths
Given an image and an associated textual question, the purpose of Knowledge-Based Visual Question Answering (KB-VQA) is to provide a correct answer to the question with the aid of external knowledge bases.
Interpretable Visual Reasoning via Probabilistic Formulation under Natural Supervision
In this paper, we rethink implicit reasoning process in VQA, and propose a new formulation which maximizes the log-likelihood of joint distribution for the observed question and predicted answer.
