Search Results for author:
Found 6 papers, 0 papers with code
MGA-VQA: Multi-Granularity Alignment for Visual Question Answering
In contrast to previous works, our model splits alignment into different levels to achieve learning better correlations without needing additional data and annotations.
SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering
As a multi-modality task, it is challenging since it requires not only visual and textual understanding, but also the ability to align cross-modality representations.
FLAR: A Unified Prototype Framework for Few-Sample Lifelong Active Recognition
To address this demand, in this paper, we propose a unified framework towards Few-sample Lifelong Active Recognition (FLAR), which aims at performing active recognition on progressively arising novel categories that only have few training samples.
Towards Better Driver Safety: Empowering Personal Navigation Technologies with Road Safety Awareness
Recent research has found that navigation systems usually assume that all roads are equally safe, directing drivers to dangerous routes, which led to catastrophic consequences.
Human-Computer Interaction
TA-Student VQA: Multi-Agents Training by Self-Questioning
The second challenge is that even the state-of-the-art dataset is of large scale, questions targeted at a single image are off in format and lack diversity in content.
Visual Query Answering by Entity-Attribute Graph Matching and Reasoning
Based on GEA and Q, we provide techniques to find matches of Q in GEA, as the answer of Qnl in Img.
