Alexander Martin

In this work, we tackle the problem of text-to-video re- trieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text-video re- trieval, our approach, Video-ColBERT, introduces a simple and efficient mechanism for fine-grained similarity assess- ment between queries and videos. Video-ColBERT is built upon three main components: a fine-grained spatial and temporal token-wise interaction, query and visual expan- sions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong in- dividual, yet compatible representations for encoding video content. These representations lead to increases in perfor- mance on common text-to-video retrieval benchmarks com- pared to other bi-encoder methods.

Efficiently retrieving and synthesizing information from large-scale multimodal collections has become a critical challenge. However, existing video retrieval datasets suffer from scope limitations, primarily focusing on matching descriptive but vague queries with small collections of professionally edited, English-centric videos. To address this gap, we introduce MultiVENT 2.0, a large-scale, multilingual event-centric video retrieval benchmark featuring a collection of more than 218,000 news videos and 3,906 queries targeting specific world events. These queries specifically target information found in the visual content, audio, embedded text, and text metadata of the videos, requiring systems leverage all these sources to succeed at the task. Preliminary results show that state-of-the-art vision-language models struggle significantly with this task, and while alternative approaches show promise, they are still insufficient to adequately address this problem. These findings underscore the need for more robust multimodal retrieval systems, as effective video retrieval is a crucial step towards multimodal content understanding and generation.

Organizing perceived change into events is a key element of human cognition, and so to understand data as humans do, AI systems must model events of human interest. While natural language enables straightforward ways to represent complex events, visual data does not facilitate analogous methods and, consequently, introduces unique challenges in event understanding. To tackle complex event modeling in multimodal settings, we introduce a multimodal formulation for arbitrarily complex events and cast the extraction of these events as a three-stage span retrieval task. We propose a corresponding benchmark for this task, MultiVENT Grounded, that consists of 14.5 hours of densely annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities. We propose a collection of LLM-driven approaches to the task of multimodal event analysis, and evaluate both on MultiVENT Grounded. Results illustrate the challenges that abstract event understanding in noisy content poses while also demonstrating promise in event-centric video-language systems.

We introduce event-keyed summarization (EKS), a novel task that marries traditional summarization and document-level event extraction, with the goal of generating a contextualized summary for a specific event, given a document and an extracted event structure. We introduce a dataset for this task, MUCSUM, consisting of summaries of all events in the classic MUC-4 dataset, along with a set of baselines that comprises both pretrained LM standards in the summarization literature, as well as larger frontier models. We show that ablations that reduce EKS to traditional summarization or structure-to-text yield inferior summaries of target events and that MUCSUM is a robust benchmark for this task. Lastly, we conduct a human evaluation of both reference and model summaries, and provide some detailed analysis of the results.