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Found 11 papers, 1 papers with code
How to Make a BLT Sandwich? Learning to Reason towards Understanding Web Instructional Videos
Furthermore, we study multiple modalities including description and transcripts for the purpose of boosting video understanding.
GAN-EM: GAN based EM learning framework
To overcome such limitation, we propose a GAN based EM learning framework that can maximize the likelihood of images and estimate the latent variables with only the constraint of L-Lipschitz continuity.
Relational Reasoning using Prior Knowledge for Visual Captioning
Extensive experiments on the MS-COCO image captioning benchmark and the MSVD video captioning benchmark validate the superiority of our method on leveraging prior commonsense knowledge to enhance relational reasoning for visual captioning.
Weakly Supervised Localization Using Background Images
Weakly Supervised Object Localization (WSOL) methodsusually rely on fully convolutional networks in order to ob-tain class activation maps(CAMs) of targeted labels.
Improve CAM with Auto-adapted Segmentation and Co-supervised Augmentation
In this paper, we propose a confidence segmentation (ConfSeg) module that builds confidence score for each pixel in CAM without introducing additional hyper-parameters.
MemCap: Memorizing Style Knowledge for Image Captioning
Generating stylized captions for images is a challenging task since it requires not only describing the content of the image accurately but also expressing the desired linguistic style appropriately.
Ranked #2 on
Image Captioning
on FlickrStyle10K
Video Question Answering on Screencast Tutorials
This paper presents a new video question answering task on screencast tutorials.
Boosting Entity-aware Image Captioning with Multi-modal Knowledge Graph
Entity-aware image captioning aims to describe named entities and events related to the image by utilizing the background knowledge in the associated article.
Multi-modal Dependency Tree for Video Captioning
To this end, we propose a novel video captioning method that generates a sentence by first constructing a multi-modal dependency tree and then traversing the constructed tree, where the syntactic structure and semantic relationship in the sentence are represented by the tree topology.
Text2Layer: Layered Image Generation using Latent Diffusion Model
To achieve layered image generation, we train an autoencoder that is able to reconstruct layered images and train diffusion models on the latent representation.
DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
We have witnessed significant progress in deep learning-based 3D vision, ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS).
