73 research outputs found
End-to-End Cross-Modality Retrieval with CCA Projections and Pairwise Ranking Loss
Cross-modality retrieval encompasses retrieval tasks where the fetched items
are of a different type than the search query, e.g., retrieving pictures
relevant to a given text query. The state-of-the-art approach to cross-modality
retrieval relies on learning a joint embedding space of the two modalities,
where items from either modality are retrieved using nearest-neighbor search.
In this work, we introduce a neural network layer based on Canonical
Correlation Analysis (CCA) that learns better embedding spaces by analytically
computing projections that maximize correlation. In contrast to previous
approaches, the CCA Layer (CCAL) allows us to combine existing objectives for
embedding space learning, such as pairwise ranking losses, with the optimal
projections of CCA. We show the effectiveness of our approach for
cross-modality retrieval on three different scenarios (text-to-image,
audio-sheet-music and zero-shot retrieval), surpassing both Deep CCA and a
multi-view network using freely learned projections optimized by a pairwise
ranking loss, especially when little training data is available (the code for
all three methods is released at: https://github.com/CPJKU/cca_layer).Comment: Preliminary version of a paper published in the International Journal
of Multimedia Information Retrieva
ForestHash: Semantic Hashing With Shallow Random Forests and Tiny Convolutional Networks
Hash codes are efficient data representations for coping with the ever
growing amounts of data. In this paper, we introduce a random forest semantic
hashing scheme that embeds tiny convolutional neural networks (CNN) into
shallow random forests, with near-optimal information-theoretic code
aggregation among trees. We start with a simple hashing scheme, where random
trees in a forest act as hashing functions by setting `1' for the visited tree
leaf, and `0' for the rest. We show that traditional random forests fail to
generate hashes that preserve the underlying similarity between the trees,
rendering the random forests approach to hashing challenging. To address this,
we propose to first randomly group arriving classes at each tree split node
into two groups, obtaining a significantly simplified two-class classification
problem, which can be handled using a light-weight CNN weak learner. Such
random class grouping scheme enables code uniqueness by enforcing each class to
share its code with different classes in different trees. A non-conventional
low-rank loss is further adopted for the CNN weak learners to encourage code
consistency by minimizing intra-class variations and maximizing inter-class
distance for the two random class groups. Finally, we introduce an
information-theoretic approach for aggregating codes of individual trees into a
single hash code, producing a near-optimal unique hash for each class. The
proposed approach significantly outperforms state-of-the-art hashing methods
for image retrieval tasks on large-scale public datasets, while performing at
the level of other state-of-the-art image classification techniques while
utilizing a more compact and efficient scalable representation. This work
proposes a principled and robust procedure to train and deploy in parallel an
ensemble of light-weight CNNs, instead of simply going deeper.Comment: Accepted to ECCV 201
Learning Aligned Cross-Modal Representations from Weakly Aligned Data
People can recognize scenes across many different modalities beyond natural
images. In this paper, we investigate how to learn cross-modal scene
representations that transfer across modalities. To study this problem, we
introduce a new cross-modal scene dataset. While convolutional neural networks
can categorize cross-modal scenes well, they also learn an intermediate
representation not aligned across modalities, which is undesirable for
cross-modal transfer applications. We present methods to regularize cross-modal
convolutional neural networks so that they have a shared representation that is
agnostic of the modality. Our experiments suggest that our scene representation
can help transfer representations across modalities for retrieval. Moreover,
our visualizations suggest that units emerge in the shared representation that
tend to activate on consistent concepts independently of the modality.Comment: Conference paper at CVPR 201
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
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