Search Results for author:
Found 18 papers, 10 papers with code
Temporal Meta-path Guided Explainable Recommendation
This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations.
Social and Information Networks
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs.
Video as Conditional Graph Hierarchy for Multi-Granular Question Answering
To align with the multi-granular essence of linguistic concepts in language queries, we propose to model video as a conditional graph hierarchy which weaves together visual facts of different granularity in a level-wise manner, with the guidance of corresponding textual cues.
Ranked #23 on
Video Question Answering
on NExT-QA
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation
Extensive experiments show that our attack outperforms unsupervised baseline attacks and has comparable performance with supervised attacks in multiple downstream tasks including node classification and link prediction.
Video Question Answering: Datasets, Algorithms and Challenges
Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos.
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning
Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for low-resolution images (e. g. - 256x256, 384384).
Invariant Grounding for Video Question Answering
At its core is understanding the alignments between visual scenes in video and linguistic semantics in question to yield the answer.
Equivariant and Invariant Grounding for Video Question Answering
Specifically, the equivariant grounding encourages the answering to be sensitive to the semantic changes in the causal scene and question; in contrast, the invariant grounding enforces the answering to be insensitive to the changes in the environment scene.
Finding the Most Transferable Tasks for Brain Image Segmentation
Furthermore, within the same modality, transferring from the source task that has stronger RoI shape similarity with the target task can significantly improve the final transfer performance.
An Out-of-Domain Synapse Detection Challenge for Microwasp Brain Connectomes
The size of image stacks in connectomics studies now reaches the terabyte and often petabyte scales with a great diversity of appearance across brain regions and samples.
The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons
In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories.
Contrastive Video Question Answering via Video Graph Transformer
CoVGT's uniqueness and superiority are three-fold: 1) It proposes a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations and dynamics, for complex spatio-temporal reasoning.
Ranked #11 on
Video Question Answering
on NExT-QA
(using extra training data)
X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics
Comprehensive, synapse-resolution imaging of the brain will be crucial for understanding neuronal computations and function.
Discovering Spatio-Temporal Rationales for Video Question Answering
We then conduct extensive studies to verify the importance of STR as well as the proposed answer interaction mechanism.
Redundancy-aware Transformer for Video Question Answering
This paper identifies two kinds of redundancy in the current VideoQA paradigm.
Can I Trust Your Answer? Visually Grounded Video Question Answering
We study visually grounded VideoQA in response to the emerging trends of utilizing pretraining techniques for video-language understanding.
Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation
Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability.
Low-Resource Court Judgment Summarization for Common Law Systems
Current summarization datasets are insufficient to satisfy the demands of summarizing precedents across multiple jurisdictions, especially when labeled data are scarce for many jurisdictions.
