This paper focuses on the task of open-domain live commentary generation. Compared to domain-specific work in this task, this setting proved particularly challenging due to the absence of domain-specific features. Aiming to bridge this gap, we integrate spatial information by proposing an utterance generation model with a novel spatial graph that is flexible to deal with the open-domain characteristics of the commentaries and significantly improves performance. Furthermore, we propose a novel evaluation scheme, more suitable for live commentary generation, that uses LLMs to automatically check whether generated utterances address essential aspects of the video via the answerability of questions extracted directly from the videos using LVLMs. Our results suggest that using a combination of our answerability score and a standard machine translation metric is likely a more reliable way to evaluate the performance in this task.

This paper explores the task of Temporal Video Grounding (TVG) where, given an untrimmed video and a query sentence, the goal is to recognize and determine temporal boundaries of action instances in the video described by natural language queries. Recent works tackled this task by improving query inputs with large pre-trained language models (PLM), at the cost of more expensive training. However, the effects of this integration are unclear, as these works also propose improvements in the visual inputs. Therefore, this paper studies the role of query sentence representation with PLMs in TVG and assesses the applicability of parameter-efficient training with NLP adapters. We couple popular PLMs with a selection of existing approaches and test different adapters to reduce the impact of the additional parameters. Our results on three challenging datasets show that, with the same visual inputs, TVG models greatly benefited from the PLM integration and fine-tuning, stressing the importance of the text query representation in this task. Furthermore, adapters were an effective alternative to full fine-tuning, even though they are not tailored to our task, allowing PLM integration in larger TVG models and delivering results comparable to SOTA models. Finally, our results shed light on which adapters work best in different scenarios.

In this paper, we specifically look at the image-text retrieval problem. Recent multimodal frameworks have shown that structured inputs and fine-tuning lead to consistent performance improvement. However, this paradigm has been challenged recently with newer Transformer-based models that can reach zero-shot state-of-the-art results despite not explicitly using structured data during pre-training. Since such strategies lead to increased computational resources, we seek to better understand their role in image-text retrieval by analyzing visual and text representations extracted with three multimodal frameworks – SGM, UNITER, and CLIP. To perform such analysis, we represent a single image or text as low-dimensional linear subspaces and perform retrieval based on subspace similarity. We chose this representation as subspaces give us the flexibility to model an entity based on feature sets, allowing us to observe how integrating or reducing information changes the representation of each entity. We analyze the performance of the selected models’ features on two standard benchmark datasets. Our results indicate that heavily pre-training models can already lead to features with critical information representing each entity, with zero-shot UNITER features performing consistently better than fine-tuned features. Furthermore, while models can benefit from structured inputs, learning representations for objects and relationships separately, such as in SGM, likely causes a loss of crucial contextual information needed to obtain a compact cluster that can effectively represent a single entity.