3,761 research outputs found
Jointly Modeling Embedding and Translation to Bridge Video and Language
Automatically describing video content with natural language is a fundamental
challenge of multimedia. Recurrent Neural Networks (RNN), which models sequence
dynamics, has attracted increasing attention on visual interpretation. However,
most existing approaches generate a word locally with given previous words and
the visual content, while the relationship between sentence semantics and
visual content is not holistically exploited. As a result, the generated
sentences may be contextually correct but the semantics (e.g., subjects, verbs
or objects) are not true.
This paper presents a novel unified framework, named Long Short-Term Memory
with visual-semantic Embedding (LSTM-E), which can simultaneously explore the
learning of LSTM and visual-semantic embedding. The former aims to locally
maximize the probability of generating the next word given previous words and
visual content, while the latter is to create a visual-semantic embedding space
for enforcing the relationship between the semantics of the entire sentence and
visual content. Our proposed LSTM-E consists of three components: a 2-D and/or
3-D deep convolutional neural networks for learning powerful video
representation, a deep RNN for generating sentences, and a joint embedding
model for exploring the relationships between visual content and sentence
semantics. The experiments on YouTube2Text dataset show that our proposed
LSTM-E achieves to-date the best reported performance in generating natural
sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. We also
demonstrate that LSTM-E is superior in predicting Subject-Verb-Object (SVO)
triplets to several state-of-the-art techniques
Skeleton-aided Articulated Motion Generation
This work make the first attempt to generate articulated human motion
sequence from a single image. On the one hand, we utilize paired inputs
including human skeleton information as motion embedding and a single human
image as appearance reference, to generate novel motion frames, based on the
conditional GAN infrastructure. On the other hand, a triplet loss is employed
to pursue appearance-smoothness between consecutive frames. As the proposed
framework is capable of jointly exploiting the image appearance space and
articulated/kinematic motion space, it generates realistic articulated motion
sequence, in contrast to most previous video generation methods which yield
blurred motion effects. We test our model on two human action datasets
including KTH and Human3.6M, and the proposed framework generates very
promising results on both datasets.Comment: ACM MM 201
Scene Graph Generation by Iterative Message Passing
Understanding a visual scene goes beyond recognizing individual objects in
isolation. Relationships between objects also constitute rich semantic
information about the scene. In this work, we explicitly model the objects and
their relationships using scene graphs, a visually-grounded graphical structure
of an image. We propose a novel end-to-end model that generates such structured
scene representation from an input image. The model solves the scene graph
inference problem using standard RNNs and learns to iteratively improves its
predictions via message passing. Our joint inference model can take advantage
of contextual cues to make better predictions on objects and their
relationships. The experiments show that our model significantly outperforms
previous methods for generating scene graphs using Visual Genome dataset and
inferring support relations with NYU Depth v2 dataset.Comment: CVPR 201
Attend and Interact: Higher-Order Object Interactions for Video Understanding
Human actions often involve complex interactions across several inter-related
objects in the scene. However, existing approaches to fine-grained video
understanding or visual relationship detection often rely on single object
representation or pairwise object relationships. Furthermore, learning
interactions across multiple objects in hundreds of frames for video is
computationally infeasible and performance may suffer since a large
combinatorial space has to be modeled. In this paper, we propose to efficiently
learn higher-order interactions between arbitrary subgroups of objects for
fine-grained video understanding. We demonstrate that modeling object
interactions significantly improves accuracy for both action recognition and
video captioning, while saving more than 3-times the computation over
traditional pairwise relationships. The proposed method is validated on two
large-scale datasets: Kinetics and ActivityNet Captions. Our SINet and
SINet-Caption achieve state-of-the-art performances on both datasets even
though the videos are sampled at a maximum of 1 FPS. To the best of our
knowledge, this is the first work modeling object interactions on open domain
large-scale video datasets, and we additionally model higher-order object
interactions which improves the performance with low computational costs.Comment: CVPR 201
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