Look at What I'm Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos

• URL: http://arxiv.org/abs/2110.10596v1
• Date: Wed, 20 Oct 2021 14:45:13 GMT
• Title: Look at What I'm Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos
• Authors: Reuben Tan, Bryan A. Plummer, Kate Saenko, Hailin Jin, Bryan Russell
• Abstract summary: We introduce the task of spatially localizing narrated interactions in videos. Key to our approach is the ability to learn to spatially localize interactions with self-supervision on a large corpus of videos with accompanying transcribed narrations. We propose a multilayer cross-modal attention network that enables effective optimization of a contrastive loss during training.
• Score: 78.34818195786846
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce the task of spatially localizing narrated interactions in videos. Key to our approach is the ability to learn to spatially localize interactions with self-supervision on a large corpus of videos with accompanying transcribed narrations. To achieve this goal, we propose a multilayer cross-modal attention network that enables effective optimization of a contrastive loss during training. We introduce a divided strategy that alternates between computing inter- and intra-modal attention across the visual and natural language modalities, which allows effective training via directly contrasting the two modalities' representations. We demonstrate the effectiveness of our approach by self-training on the HowTo100M instructional video dataset and evaluating on a newly collected dataset of localized described interactions in the YouCook2 dataset. We show that our approach outperforms alternative baselines, including shallow co-attention and full cross-modal attention. We also apply our approach to grounding phrases in images with weak supervision on Flickr30K and show that stacking multiple attention layers is effective and, when combined with a word-to-region loss, achieves state of the art on recall-at-one and pointing hand accuracies.

Related papers

• CM-PIE: Cross-modal perception for interactive-enhanced audio-visual video parsing [23.85763377992709]
  We propose a novel interactive-enhanced cross-modal perception method(CM-PIE), which can learn fine-grained features by applying a segment-based attention module. We show that our model offers improved parsing performance on the Look, Listen, and Parse dataset.
  arXiv  Detail & Related papers  (2023-10-11T14:15:25Z)
• Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding [112.3913646778859]
  We propose a simple yet effective video-language modeling framework, S-ViLM. It includes two novel designs, inter-clip spatial grounding and intra-clip temporal grouping, to promote learning region-object alignment and temporal-aware features. S-ViLM surpasses the state-of-the-art methods substantially on four representative downstream tasks.
  arXiv  Detail & Related papers  (2023-03-28T22:45:07Z)
• Learning Actor-centered Representations for Action Localization in Streaming Videos using Predictive Learning [18.757368441841123]
  Event perception tasks such as recognizing and localizing actions in streaming videos are essential for tackling visual understanding tasks. We tackle the problem of learning textitactor-centered representations through the notion of continual hierarchical predictive learning. Inspired by cognitive theories of event perception, we propose a novel, self-supervised framework.
  arXiv  Detail & Related papers  (2021-04-29T06:06:58Z)
• MaAST: Map Attention with Semantic Transformersfor Efficient Visual Navigation [4.127128889779478]
  This work focuses on performing better or comparable to the existing learning-based solutions for visual navigation for autonomous agents. We propose a method to encode vital scene semantics into a semantically informed, top-down egocentric map representation. We conduct experiments on 3-D reconstructed indoor PointGoal visual navigation and demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2021-03-21T12:01:23Z)
• Contrastive Transformation for Self-supervised Correspondence Learning [120.62547360463923]
  We study the self-supervised learning of visual correspondence using unlabeled videos in the wild. Our method simultaneously considers intra- and inter-video representation associations for reliable correspondence estimation. Our framework outperforms the recent self-supervised correspondence methods on a range of visual tasks.
  arXiv  Detail & Related papers  (2020-12-09T14:05:06Z)
• Look, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning [17.6311804187027]
  An underlying correlation between audio and visual events can be utilized as free supervised information to train a neural network. We propose a novel self-supervised framework with co-attention mechanism to learn generic cross-modal representations from unlabelled videos. Experiments show that our model achieves state-of-the-art performance on the pretext task while having fewer parameters compared with existing methods.
  arXiv  Detail & Related papers  (2020-08-13T10:08:12Z)
• Learning Modality Interaction for Temporal Sentence Localization and Event Captioning in Videos [76.21297023629589]
  We propose a novel method for learning pairwise modality interactions in order to better exploit complementary information for each pair of modalities in videos. Our method turns out to achieve state-of-the-art performances on four standard benchmark datasets.
  arXiv  Detail & Related papers  (2020-07-28T12:40:59Z)
• Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos [73.4504252917816]
  The task of temporally grounding textual queries in videos is to localize one video segment that semantically corresponds to the given query. Most of the existing approaches rely on segment-sentence pairs (temporal annotations) for training, which are usually unavailable in real-world scenarios. We present an effective weakly-supervised model, named as Multi-Level Attentional Reconstruction Network (MARN), which only relies on video-sentence pairs during the training stage.
  arXiv  Detail & Related papers  (2020-03-16T07:01:01Z)
• See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks [184.4379622593225]
  We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task. We emphasize the importance of inherent correlation among video frames and incorporate a global co-attention mechanism. We propose a unified and end-to-end trainable framework where different co-attention variants can be derived for mining the rich context within videos.
  arXiv  Detail & Related papers  (2020-01-19T11:10:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.