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Found 11 papers, 1 papers with code
Bounding-box Channels for Visual Relationship Detection
In this paper, we propose the bounding-box channels, a novel architecture capable of relating the semantic, spatial, and image features strongly.
Leveraging Many-To-Many Relationships for Defending Against Visual-Language Adversarial Attacks
To this end, this paper studies defense strategies against adversarial attacks on VL models for ITR for the first time.
Improving segmentation of calcified and non-calcified plaques on CCTA-CPR scans via masking of the artery wall
In this paper, we propose a novel methodology for segmenting calcified and non-calcified plaques in CCTA-CPR scans of coronary arteries.
Video Ads Content Structuring by Combining Scene Confidence Prediction and Tagging
Video ads segmentation and tagging is a challenging task due to two main reasons: (1) the video scene structure is complex and (2) it includes multiple modalities (e. g., visual, audio, text.).
Efficient training for future video generation based on hierarchical disentangled representation of latent variables
In this paper, we propose a novel method for generating future prediction videos with less memory usage than the conventional methods.
Coronary Wall Segmentation in CCTA Scans via a Hybrid Net with Contours Regularization
In this paper, we propose a novel boundary detection method for coronary arteries that focuses on the continuity and connectivity of the boundaries.
Improved Optical Flow for Gesture-based Human-robot Interaction
Gesture interaction is a natural way of communicating with a robot as an alternative to speech.
Long-Term Human Video Generation of Multiple Futures Using Poses
First, from an input human video, we generate sequences of future human poses (i. e., the image coordinates of their body-joints) via adversarial learning.
Conditional Video Generation Using Action-Appearance Captions
The results demonstrate that CFT-GAN is able to successfully generate videos containing the action and appearances indicated in the captions.
Visual Question Generation for Class Acquisition of Unknown Objects
In this paper, we propose a method for generating questions about unknown objects in an image, as means to get information about classes that have not been learned.
Summarization of User-Generated Sports Video by Using Deep Action Recognition Features
The labels are provided by annotators possessing different experience with respect to Kendo to demonstrate how the proposed method adapts to different needs.
