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Understanding the visual relationship between two objects involves identifying the subject, the object, and a predicate relating them. We leverage the strong correlations between the predicate and the hsubj; obji pair (both semantically and spatially) to predict predicates conditioned on the subjects and the objects. Modeling the three entities jointly more accurately reflects their relationships...
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites. We first fine-tune GoogleNet by jointly modeling clothing images and their corresponding descriptions in a visual-semantic embedding space. Then, for each attribute (word), we generate its spatiallyaware representation by combining its semantic word vector representation...
Spatial relationships between objects provide important information for text-based image retrieval. As users are more likely to describe a scene from a real world perspective, using 3D spatial relationships rather than 2D relationships that assume a particular viewing direction, one of the main challenges is to infer the 3D structure that bridges images with users text descriptions. However, direct...
On-the-fly video retrieval using web images and fast Fisher Vector products (VRFP) is a real-time video retrieval framework based on short text input queries, which obtains weakly labeled training images from the web after the query is known. The retrieved web images representing the query and each database video are treated as unordered collections of images, and each collection is represented using...
We propose clause lets, sets of concurrent actions and their temporal relationships, and explore their application to video event analysis. We train clause lets in two stages. We initially train first level clause let detectors that find a limited set of actions in particular qualitative temporal configurations based on Allen's interval relations. In the second stage, we apply the first level detectors...
Efficient content-based access to large multimedia collections requires annotations that are human-meaningful, and user tagging of media is one means to obtain such semantic metadata. Tags can also act as user feedback essential for quality of multimedia experience assessment; however, tags can lack user context and become ambiguous between different users. Further, user tagging is a deliberate and...
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