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Infusing Environmental Captions for Long-Form Video Language Grounding

Hyogun Lee, Soyeon Hong, Mujeen Sung, Jinwoo Choi

TL;DR

This work tackles long-form video-language grounding (LFVLG), where ground-truth moments are sparsely distributed within long videos. It introduces EI-VLG, a method that infuses environment cues from a multi-modal large language model-generated caption stream into a video-language grounding model to drastically reduce search space. The approach comprises an Environment Encoder with a frozen caption generator and a learnable text encoder trained via a marginal log-likelihood objective, an Environment Infuser that fuses environment cues with video features through cross-attention, and a flexible Video-Language Grounding model. Evaluated on EgoNLQ, EI-VLG achieves state-of-the-art performance on most metrics, demonstrating the practical value of incorporating rich, externally generated environmental descriptions for robust long-form video understanding.

Abstract

In this work, we tackle the problem of long-form video-language grounding (VLG). Given a long-form video and a natural language query, a model should temporally localize the precise moment that answers the query. Humans can easily solve VLG tasks, even with arbitrarily long videos, by discarding irrelevant moments using extensive and robust knowledge gained from experience. Unlike humans, existing VLG methods are prone to fall into superficial cues learned from small-scale datasets, even when they are within irrelevant frames. To overcome this challenge, we propose EI-VLG, a VLG method that leverages richer textual information provided by a Multi-modal Large Language Model (MLLM) as a proxy for human experiences, helping to effectively exclude irrelevant frames. We validate the effectiveness of the proposed method via extensive experiments on a challenging EgoNLQ benchmark.

Infusing Environmental Captions for Long-Form Video Language Grounding

TL;DR

This work tackles long-form video-language grounding (LFVLG), where ground-truth moments are sparsely distributed within long videos. It introduces EI-VLG, a method that infuses environment cues from a multi-modal large language model-generated caption stream into a video-language grounding model to drastically reduce search space. The approach comprises an Environment Encoder with a frozen caption generator and a learnable text encoder trained via a marginal log-likelihood objective, an Environment Infuser that fuses environment cues with video features through cross-attention, and a flexible Video-Language Grounding model. Evaluated on EgoNLQ, EI-VLG achieves state-of-the-art performance on most metrics, demonstrating the practical value of incorporating rich, externally generated environmental descriptions for robust long-form video understanding.

Abstract

In this work, we tackle the problem of long-form video-language grounding (VLG). Given a long-form video and a natural language query, a model should temporally localize the precise moment that answers the query. Humans can easily solve VLG tasks, even with arbitrarily long videos, by discarding irrelevant moments using extensive and robust knowledge gained from experience. Unlike humans, existing VLG methods are prone to fall into superficial cues learned from small-scale datasets, even when they are within irrelevant frames. To overcome this challenge, we propose EI-VLG, a VLG method that leverages richer textual information provided by a Multi-modal Large Language Model (MLLM) as a proxy for human experiences, helping to effectively exclude irrelevant frames. We validate the effectiveness of the proposed method via extensive experiments on a challenging EgoNLQ benchmark.
Paper Structure (24 sections, 7 equations, 3 figures, 4 tables)

This paper contains 24 sections, 7 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. EI-VLG
  3. Environment Encoder
  4. Caption generator.
  5. Text encoder.
  6. Text encoder learning.
  7. Video-Language Grounding Model
  8. Environment Infuser
  9. Experiments
  10. Main Results
  11. Ablation Study
  12. Environment Infusion
  13. Quality of Environment Cues
  14. Environment Encoder
  15. Conclusion
  16. ...and 9 more sections

Figures (3)

  • Figure 1: How do humans and machines solve the long-form video-language grounding problem? The example illustrates how humans can easily localize the red chopping board using extensive and robust knowledge gained from experience. In contrast, VLG models trained on small-scale datasets might incorrectly discard the ground truth moment because the chopping board does not have a wooden texture.
  • Figure 2: Overview of EI-VLG. EI-VLG consists of three components: (a) environment encoder (Section \ref{['sec:ee']}), (b) environment infuser (Section \ref{['sec:ei']}), and (c) video-language model (Section \ref{['sec:vlg']}). (d) We fine-tune the environment encoder to encourage the encoded environment feature vectors to be suitable for attention with query embedding. (e) During inference, EI-VLG effectively reduces the search space by infusing the environment knowledge.
  • Figure 3: We should use a large caption generator. We need fine-grained descriptions to reduce the search space within a long sequence of indistinguishable in-context views. Not only where I am, but also the direction I see, objects, and their relative locations.