Search Results for author:
Found 5 papers, 4 papers with code
MeaCap: Memory-Augmented Zero-shot Image Captioning
The framework of MeaCap achieves the state-of-the-art performance on a series of zero-shot IC settings.
SnapCap: Efficient Snapshot Compressive Video Captioning
To address these problems, in this paper, we propose a novel VC pipeline to generate captions directly from the compressed measurement, which can be captured by a snapshot compressive sensing camera and we dub our model SnapCap.
PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification
We find that by formulating the multi-label classification as a CT problem, we can exploit the interactions between the image and label efficiently by minimizing the bidirectional CT cost.
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishing
Zero-shot capability has been considered as a new revolution of deep learning, letting machines work on tasks without curated training data.
Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning
Inspired by recent successes in integrating semantic topics into this task, this paper develops a plug-and-play hierarchical-topic-guided image paragraph generation framework, which couples a visual extractor with a deep topic model to guide the learning of a language model.
