Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: Research Program


Overall, Linkmedia follows two main directions of research that are (i) extracting and representing information from the documents in collections, from the relationships between the documents and from what user build from these documents, and (ii) facilitating the access to documents and to the information that has been elaborated from their processing.

Research Direction 1: Extracting and Representing Information

Linkmedia follows several research tracks for extracting knowledge from the collections and representing that knowledge to facilitate users acquiring gradual, long term, constructive insights. Automatically processing documents makes it crucial to consider the accountability of the algorithms, as well as understanding when and why algorithms make errors, and possibly invent techniques that compensate or reduce the impact of errors. It also includes dealing with malicious adversaries carefully manipulating the data in order to compromise the whole knowledge extraction effort. In other words, Linkmedia also investigates various aspects related to the security of the algorithms analyzing multimedia material for knowledge extraction and representation.

Knowledge is not solely extracted by algorithms, but also by humans as they gradually get insight. This human knowledge can be materialized in computer-friendly formats, allowing algorithms to use this knowledge. For example, humans can create or update ontologies and knowledge bases that are in relation with a particular collection, they can manually label specific data samples to facilitate their disambiguation, they can manually correct errors, etc. In turn, knowledge provided by humans may help algorithms to then better process the data collections, which provides higher quality knowledge to humans, which in turn can provide some better feedback to the system, and so on. This virtuous cycle where algorithms and humans cooperate in order to make the most of multimedia collections requires specific support and techniques, as detailed below.

Machine Learning for Multimedia Material.

Many approaches are used to extract relevant information from multimedia material, ranging from very low-level to higher-level descriptions (classes, captions, ...). That diversity of information is produced by algorithms that have varying degrees of supervision. Lately, fully supervised approaches based on deep learning proved to outperform most older techniques. This is particularly true for the latest developments of Recurrent Neural Networkds (RNN, such as LSTMs) or convolutional neural network (CNNs) for images that reach excellent performance  [62]. Linkmedia contributes to advancing the state of the art in computing representations for multimedia material by investigating the topics listed below. Some of them go beyond the very processing of multimedia material as they also question the fundamentals of machine learning procedures when applied to multimedia.

Adversarial Machine Learning.

Systems based on ML take more and more decisions on our behalf, and maliciously influencing these decisions by crafting adversarial multimedia material is a potential source of dangers: a small amount of carefully crafted noise imperceptibly added to images corrupts classification and/or recognition. This can naturally impact the insight users get on the multimedia collection they work with, leading to taking erroneous decisions e.g.

This adversarial phenomenon is not particular to deep learning, and can be observed even when using other ML approaches  [52]. Furthermore, it has been demonstrated that adversarial samples generalize very well across classifiers, architectures, training sets. The reasons explaining why such tiny content modifications succeed in producing severe errors are still not well understood.

We are left with little choice: we must gain a better understanding of the weaknesses of ML processes, and in particular of deep learning. We must understand why attacks are possible as well as discover mechanisms protecting ML against adversarial attacks (with a special emphasis on convolutional neural networks). Some initial contributions have started exploring such research directions, mainly focusing on images and computer vision problems. Very little has been done for understanding adversarial ML from a multimedia perspective  [56].

Linkmedia is in a unique position to throw at this problem new perspectives, by experimenting with other modalities, used in isolation one another, as well as experimenting with true multimodal inputs. This is very challenging, and far more complicated and interesting than just observing adversarial ML from a computer vision perspective. No one clearly knows what is at stake with adversarial audio samples, adversarial video sequences, adversarial ASR, adversarial NLP, adversarial OCR, all this being often part of a sophisticated multimedia processing pipeline.

Our ambition is to lead the way for initiating investigations where the full diversity of modalities we are used to work with in multimedia are considered from a perspective of adversarial attacks and defenses, both at learning and test time. In addition to what is described above, and in order to trust the multimedia material we analyze and/or the algorithms that are at play, Linkmedia investigates the following topics:

Multimedia Knowledge Extraction.

Information obtained from collections via computer ran processes is not the only thing that needs to be represented. Humans are in the loop, and they gradually improve their level of understanding of the content and nature of the multimedia collection. Discovering knowledge and getting insight is involving multiple people across a long period of time, and what each understands, concludes and discovers must be recorded and made available to others. Collaboratively inspecting collections is crucial. Ontologies are an often preferred mechanism for modeling what is inside a collection, but this is probably limitative and narrow.

Linkmedia is concerned with making use of existing strategies in relation with ontologies and knowledge bases. In addition, Linkmedia uses mechanisms allowing to materialize the knowledge gradually acquired by humans and that might be subsequently used either by other humans or by computers in order to better and more precisely analyze collections. This line of work is instantiated at the core of the iCODA project Linkmedia coordinates. We are therefore concerned with:

Research Direction 2: Accessing Information

Linkmedia centers its activities on enabling humans to make good use of vast multimedia collections. This material takes all its cultural and economic value, all its artistic wonder when it can be accessed, watched, searched, browsed, visualized, summarized, classified, shared, ... This allows users to fully enjoy the incalculable richness of the collections. It also makes it possible for companies to create business rooted in this multimedia material.

Accessing the multimedia data that is inside a collection is complicated by the various type of data, their volume, their length, etc. But it is even more complicated to access the information that is not materialized in documents, such as the relationships between parts of different documents that however share some similarity. Linkmedia in its first four years of existence established itself as one of the leading teams in the field of multimedia analytics, contributing to the establishment of a dedicated community (refer to the various special sessions we organized with MMM, the iCODA and the LIMAH projects, as well as  [66], [67], [63]).

Overall, facilitating the access to the multimedia material, to the relevant information and the corresponding knowledge asks for algorithms that efficiently search collections in order to identify the elements of collections or of the acquired knowledge that are matching a query, or that efficiently allow navigating the collections or the acquired knowledge. Navigation is likely facilitated if techniques are able to handle information and knowledge according to hierarchical perspectives, that is, allow to reveal data according to various levels of details. Aggregating or summarizing multimedia elements is not trivial.

Figure 1. Exploration-search axis with example tasks

Three topics are therefore in relation with this second research direction. Linkmedia tackles the issues in relation to searching, to navigating and to summarizing multimedia information. Information needs when discovering the content of a multimedia collection can be conveniently mapped to the exploration-search axis, as first proposed by Zahálka and Worring in  [88], and illustrated by Figure 1 where expert users typically work near the right end because their tasks involve precise queries probing search engines. In contrast, lay-users start near the exploration end of the axis. Overall, users may alternate searches and explorations by going back and forth along the axis. The underlying model and system must therefore be highly dynamic, support interactions with the users and propose means for easy refinements. Linkmedia contributes to advancing the state of the art in searching operations, in navigating operations (also referred to as browsing), and in summarizing operations.


Search engines must run similarity searches very efficiently. High-dimensional indexing techniques therefore play a central role. Yet, recent contributions in ML suggest to revisit indexing in order to adapt to the specific properties of modern features describing contents.


Navigating a multimedia collection is very central to its understanding. It differs from searching as navigation is not driven by any specific query. Rather, it is mostly driven by the relationships that various documents have one another. Relationships are supported by the links between documents and/or parts of documents. Links rely on semantic similarity, depicting the fact that two documents share information on the same topic. But other aspects than semantics are also at stake, e.g., time with the dates of creation of the documents or geography with mentions or appearance in documents of some geographical landmarks or with geo-tagged data.

In multimedia collections, links can be either implicit or explicit, the latter being much easier to use for navigation. An example of an implicit link can be the name of someone existing in several different news articles; we, as humans, create a mental link between them. In some cases, the computer misses such configurations, leaving such links implicit. Implicit links are subject to human interpretation, hence they are sometimes hard to identify for any automatic analysis process. Implicit links not being materialized, they can therefore hardly be used for navigation or faceted search. Explicit links can typically be seen as hyperlinks, established either by content providers or, more aligned with Linkmedia , automatically determined from content analysis. Entity linking (linking content to an entity referenced in a knowledge base) is a good example of the creation of explicit links. Semantic similarity links, as investigated in the LIMAH project and as considered in the search and hyperlinking task at MediaEval and TRECVid, are also prototypical links that can be made explicit for navigation. Pursuing work, we investigate two main issues:


Multimedia collections contain far too much information to allow any easy comprehension. It is mandatory to have facilities to aggregate and summarize a large body on information into a compact, concise and meaningful representation facilitating getting insight. Current technology suggests that multimedia content aggregation and story-telling are two complementary ways to provide users with such higher-level views. Yet, very few studies already investigated these issues. Recently, video or image captioning  [87], [82] have been seen as a way to summarize visual content, opening the door to state-of-the-art multi-document text summarization  [61] with text as a pivot modality. Automatic story-telling has been addressed for highly specific types of content, namely TV series  [54] and news  [73], [80], but still need a leap forward to be mostly automated, e.g., using constraint-based approaches for summarization  [51], [80].

Furthermore, not only the original multimedia material has to be summarized, but the knowledge acquired from its analysis is also to summarize. It is important to be able to produce high-level views of the relationships between documents, emphasizing some structural distinguishing qualities. Graphs establishing such relationships need to be constructed at various level of granularity, providing some support for summarizing structural traits.

Summarizing multimedia information poses several scientific challenges that are: