Search Results for author:
Found 9 papers, 5 papers with code
Audio-Visual Instance Segmentation
In this paper, we propose a new multi-modal task, namely audio-visual instance segmentation (AVIS), in which the goal is to identify, segment, and track individual sounding object instances in audible videos, simultaneously.
CM-PIE: Cross-modal perception for interactive-enhanced audio-visual video parsing
Audio-visual video parsing is the task of categorizing a video at the segment level with weak labels, and predicting them as audible or visible events.
Improved Instruction Ordering in Recipe-Grounded Conversation
In this paper, we study the task of instructional dialogue and focus on the cooking domain.
Instruction Tuning with Lexicons for Zero-Shot Style Classification
Style is used to convey authors' intentions and attitudes.
Morié Attack (MA): A New Potential Risk of Screen Photos
Images, captured by a camera, play a critical role in training Deep Neural Networks (DNNs).
Moiré Attack (MA): A New Potential Risk of Screen Photos
Images, captured by a camera, play a critical role in training Deep Neural Networks (DNNs).
SOTR: Segmenting Objects with Transformers
Most recent transformer-based models show impressive performance on vision tasks, even better than Convolution Neural Networks (CNN).
LeafMask: Towards Greater Accuracy on Leaf Segmentation
In this work, we present the LeafMask neural network, a new end-to-end model to delineate each leaf region and count the number of leaves, with two main components: 1) the mask assembly module merging position-sensitive bases of each predicted box after non-maximum suppression (NMS) and corresponding coefficients to generate original masks; 2) the mask refining module elaborating leaf boundaries from the mask assembly module by the point selection strategy and predictor.
Ranked #1 on
Instance Segmentation
on Leaf Segmentation Challenge
