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In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations and compare different embedding strategies. We demonstrate the capabilities of the proposed approach on a multimedia collection originating from the violent online extremism forum Stormfront, which is particularly interesting due to the high semantic level of the discussions it features.

Faces in artistic paintings most often contain the same elements (eyes, nose, mouth...) as faces in the real world, however they are not a photo-realistic transfer of physical visual content. These creative nuances the artists introduce in their work act as interference when facial detection models are used in the artistic domain. In this work we introduce models that can accurately detect, classify and conditionally generate artistically painted eyes in portrait paintings. In addition, we introduce the OmniEyes Dataset that captures the essence of painted eyes with annotated patches from 250 K artistic paintings and their metadata. We evaluate our approach in inpainting, out of context eye generation and classification on portrait paintings from the OmniArt dataset. We conduct a user case study to further study the quality of our generated samples, asses their aesthetic aspects and provide quantitative and qualitative results for our model’s performance.

When browsing large video collections, human-in-the-loop systems are essential. The system should understand the semantic information need of the user and interactively help formulate queries to satisfy that information need based on data-driven methods. Full synergy between the interacting user and the system can only be obtained when the system learns from the user interactions while providing immediate response. Doing so with dynamically changing information needs for large scale multimodal collections is a challenging task. To push the boundary of current methods, we propose to apply the state of the art in interactive multimodal learning to the complex multimodal information needs posed by the Video Browser Showdown (VBS). To that end we adapt the Exquisitor system, a highly scalable interactive learning system. Exquisitor combines semantic features extracted from visual content and text to suggest relevant media items to the user, based on user relevance feedback on previously suggested items. In this paper, we briefly describe the Exquisitor system, and its first incarnation as a VBS entrant.