This work introduces a model that can recognize objects in images even if no training data is available for the objects. The only necessary knowledge about the unseen categories comes from unsupervised large text corpora. In our zero-shot framework distributional information in language can be seen as spanning a semantic basis for understanding what objects look like. Most previous zero-shot learning models can only differentiate between unseen classes. In contrast, our model can both obtain state of the art performance on classes that have thousands of training images and obtain reasonable performance on unseen classes. This is achieved by first using outlier detection in the semantic space and then two separate recognition models. Furthermore, our model does not require any manually defined semantic features for either words or images.
Some work has been done on multimodal distributional methods @cite_9 @cite_2 . Most recently, @cite_3 worked on perceptually grounding word meaning and showed that joint models are better able to predict the color of concrete objects.
@cite_9 @cite_3 @cite_2
2137735870 1953971037 342285082 2093367888
Our research aims at building computational models of word meaning that are perceptually grounded. Using computer vision techniques, we build visual and multimodal distributional models and compare them to standard textual models. Our results show that, while visual models with state-of-the-art computer vision techniques perform worse than textual models in general tasks (accounting for semantic relatedness), they are as good or better models of the meaning of words with visual correlates such as color terms, even in a nontrivial task that involves nonliteral uses of such words. Moreover, we show that visual and textual information are tapping on different aspects of meaning, and indeed combining them in multimodal models often improves performance. We investigate the hypothesis that word representations ought to incorporate both distributional and relational semantics. To this end, we employ the Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a distributional objective on raw text and a relational objective on WordNet. Preliminary results on knowledge base completion, analogy tests, and parsing show that word representations trained on both objectives can give improvements in some cases. The distributional hypothesis of Harris (1954), according to which the meaning of words is evidenced by the contexts they occur in, has motivated several effective techniques for obtaining vector space semantic representations of words using unannotated text corpora. This paper argues that lexico-semantic content should additionally be invariant across languages and proposes a simple technique based on canonical correlation analysis (CCA) for incorporating multilingual evidence into vectors generated monolingually. We evaluate the resulting word representations on standard lexical semantic evaluation tasks and show that our method produces substantially better semantic representations than monolingual techniques. Detecting complex events in videos is intrinsically a multimodal problem since both audio and visual channels provide important clues. While conventional methods fuse both modalities at a superficial level, in this paper we propose a new representation--called bi-modal words--to explore representative joint audio---visual patterns. We first build a bipartite graph to model relation across the quantized words extracted from the visual and audio modalities. Partitioning over the bipartite graph is then applied to produce the bi-modal words that reveal the joint patterns across modalities. Different pooling strategies are then employed to re-quantize the visual and audio words into the bi-modal words and form bi-modal Bag-of-Words representations. Since it is difficult to predict the suitable number of bi-modal words, we generate bi-modal words at different levels (i.e., codebooks with different sizes), and use multiple kernel learning to combine the resulting multiple representations during event classifier learning. Experimental results on three popular datasets show that the proposed method achieves statistically significant performance gains over methods using individual visual and audio feature alone and existing popular multi-modal fusion methods. We also find that average pooling is particularly suitable for bi-modal representation, and using multiple kernel learning to combine multi-modal representations at various granularities is helpful.