16,016 research outputs found
Multimodal Sparse Coding for Event Detection
Unsupervised feature learning methods have proven effective for
classification tasks based on a single modality. We present multimodal sparse
coding for learning feature representations shared across multiple modalities.
The shared representations are applied to multimedia event detection (MED) and
evaluated in comparison to unimodal counterparts, as well as other feature
learning methods such as GMM supervectors and sparse RBM. We report the
cross-validated classification accuracy and mean average precision of the MED
system trained on features learned from our unimodal and multimodal settings
for a subset of the TRECVID MED 2014 dataset.Comment: Multimodal Machine Learning Workshop at NIPS 201
Graph Distillation for Action Detection with Privileged Modalities
We propose a technique that tackles action detection in multimodal videos
under a realistic and challenging condition in which only limited training data
and partially observed modalities are available. Common methods in transfer
learning do not take advantage of the extra modalities potentially available in
the source domain. On the other hand, previous work on multimodal learning only
focuses on a single domain or task and does not handle the modality discrepancy
between training and testing. In this work, we propose a method termed graph
distillation that incorporates rich privileged information from a large-scale
multimodal dataset in the source domain, and improves the learning in the
target domain where training data and modalities are scarce. We evaluate our
approach on action classification and detection tasks in multimodal videos, and
show that our model outperforms the state-of-the-art by a large margin on the
NTU RGB+D and PKU-MMD benchmarks. The code is released at
http://alan.vision/eccv18_graph/.Comment: ECCV 201
Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition
Aerial scene recognition is a fundamental task in remote sensing and has
recently received increased interest. While the visual information from
overhead images with powerful models and efficient algorithms yields
considerable performance on scene recognition, it still suffers from the
variation of ground objects, lighting conditions etc. Inspired by the
multi-channel perception theory in cognition science, in this paper, for
improving the performance on the aerial scene recognition, we explore a novel
audiovisual aerial scene recognition task using both images and sounds as
input. Based on an observation that some specific sound events are more likely
to be heard at a given geographic location, we propose to exploit the knowledge
from the sound events to improve the performance on the aerial scene
recognition. For this purpose, we have constructed a new dataset named AuDio
Visual Aerial sceNe reCognition datasEt (ADVANCE). With the help of this
dataset, we evaluate three proposed approaches for transferring the sound event
knowledge to the aerial scene recognition task in a multimodal learning
framework, and show the benefit of exploiting the audio information for the
aerial scene recognition. The source code is publicly available for
reproducibility purposes.Comment: ECCV 202
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches
- …