Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings

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. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. 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, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature

IMuR 2022 Introduction to the 2nd Workshop on Interactive Multimedia Retrieval

The retrieval of multimedia content remains a difficult problem where a high accuracy or specificity can often only be achieved interactively, with a user working closely and iteratively with a retrieval system. While there exist several venues for the exchange of insights in the area of information retrieval in general and multimedia retrieval specifically, there is little discussion on such interactive retrieval approaches. The Workshop on Interactive Multimedia Retrieval offers such a venue. Held for the 2nd time in 2022, it attracted a diverse set of contributions, six of which were accepted for presentation. The following provides a brief overview of the workshop itself as well as the contributions of 2022

Non-metric similarity search of tandem mass spectra including posttranslational modifications

AbstractIn biological applications, the tandem mass spectrometry is a widely used method for determining protein and peptide sequences from an “in vitro” sample. The sequences are not determined directly, but they must be interpreted from the mass spectra, which is the output of the mass spectrometer. This work is focused on a similarity-search approach to mass spectra interpretation, where the parameterized Hausdorff distance (dHP) is used as the similarity. In order to provide an efficient similarity search under dHP, the metric access methods and the TriGen algorithm (controlling the metricity of dHP) are employed. Moreover, the search model based on the dHP supports posttranslational modifications (PTMs) in the query mass spectra, what is typically a problem when an indexing approach is used. Our approach can be utilized as a coarse filter by any other database approach for mass spectra interpretation

Protein Sequences Identification using NM-tree

ABSTRACT We have generalized a method for tandem mass spectra interpretation, based on the parameterized Hausdorff distance dHP . Instead of just peptides (short pieces of proteins), in this paper we describe the interpretation of whole protein sequences. For this purpose, we employ the recently introduced NM-tree to index the database of hypothetical mass spectra for exact or fast approximate search. The NM-tree combines the M-tree with the TriGen algorithm in a way that allows to dynamically control the retrieval precision at query time. A scheme for protein sequences identification using the NM-tree is proposed

Interactive video retrieval in the age of effective joint embedding deep models: lessons from the 11th VBS

This paper presents findings of the eleventh Video Browser Showdown competition, where sixteen teams competed in known-item and ad-hoc search tasks. Many of the teams utilized state-of-the-art video retrieval approaches that demonstrated high effectiveness in challenging search scenarios. In this paper, a broad survey of all utilized approaches is presented in connection with an analysis of the performance of participating teams. Specifically, both high-level performance indicators are presented with overall statistics as well as in-depth analysis of the performance of selected tools implementing result set logging. The analysis reveals evidence that the CLIP model represents a versatile tool for cross-modal video retrieval when combined with interactive search capabilities. Furthermore, the analysis investigates the effect of different users and text query properties on the performance in search tasks. Last but not least, lessons learned from search task preparation are presented, and a new direction for ad-hoc search based tasks at Video Browser Showdown is introduced

Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown

The Video Browser Showdown addresses difficult video search challenges through an annual interactive evaluation campaign attracting research teams focusing on interactive video retrieval. The campaign aims to provide insights into the performance of participating interactive video retrieval systems, tested by selected search tasks on large video collections. For the first time in its ten year history, the Video Browser Showdown 2021 was organized in a fully remote setting and hosted a record number of sixteen scoring systems. In this paper, we describe the competition setting, tasks and results and give an overview of state-of-the-art methods used by the competing systems. By looking at query result logs provided by ten systems, we analyze differences in retrieval model performances and browsing times before a correct submission. Through advances in data gathering methodology and tools, we provide a comprehensive analysis of ad-hoc video search tasks, discuss results, task design and methodological challenges. We highlight that almost all top performing systems utilize some sort of joint embedding for text-image retrieval and enable specification of temporal context in queries for known-item search. Whereas a combination of these techniques drive the currently top performing systems, we identify several future challenges for interactive video search engines and the Video Browser Showdown competition itself