Search Results for author:
Found 8 papers, 5 papers with code
Top-Down Framework for Weakly-supervised Grounded Image Captioning
Weakly-supervised grounded image captioning (WSGIC) aims to generate the caption and ground (localize) predicted object words in the input image without using bounding box supervision.
POAR: Towards Open Vocabulary Pedestrian Attribute Recognition
Our key idea is to formulate the POAR problem as an image-text search problem.
VLT: Vision-Language Transformer and Query Generation for Referring Segmentation
We propose a Vision-Language Transformer (VLT) framework for referring segmentation to facilitate deep interactions among multi-modal information and enhance the holistic understanding to vision-language features.
Ranked #3 on
Referring Video Object Segmentation
on MeViS
Referring Expression Segmentation
Referring Video Object Segmentation
Learning Transferable Human-Object Interaction Detector With Natural Language Supervision
More specifically, we propose a new HOI visual encoder to detect the interacting humans and objects, and map them to a joint feature space to perform interaction recognition.
Vision-Language Transformer and Query Generation for Referring Segmentation
We introduce transformer and multi-head attention to build a network with an encoder-decoder attention mechanism architecture that "queries" the given image with the language expression.
Generalized Referring Expression Comprehension
Generalized Referring Expression Segmentation
+1
Discovering Human Interactions With Large-Vocabulary Objects via Query and Multi-Scale Detection
In this work, we study the problem of human-object interaction (HOI) detection with large vocabulary object categories.
Discovering Human Interactions With Novel Objects via Zero-Shot Learning
To recognize objects from unseen categories, we devise a zero-shot classification module upon the classifier of seen categories.
Joint Representative Selection and Feature Learning: A Semi-Supervised Approach
Given labeled source data and big unlabeled target data, we aim to find representatives in the target data, which can not only represent and associate data points belonging to each labeled category, but also discover novel categories in the target data, if any.
