Comparing Learning Paradigms for Egocentric Video Summarization

• URL: http://arxiv.org/abs/2506.21785v1
• Date: Thu, 26 Jun 2025 21:46:48 GMT
• Title: Comparing Learning Paradigms for Egocentric Video Summarization
• Authors: Daniel Wen,
• Abstract summary: This study investigates computer vision paradigms by assessing their ability to understand and interpret egocentric video data.<n>We examine Shotluck Holmes (state-of-the-art supervised learning), TAC-SUM (state-of-the-art unsupervised learning), and GPT-4o (a prompt fine-tuned pre-trained model), evaluating their effectiveness in video summarization.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we investigate various computer vision paradigms - supervised learning, unsupervised learning, and prompt fine-tuning - by assessing their ability to understand and interpret egocentric video data. Specifically, we examine Shotluck Holmes (state-of-the-art supervised learning), TAC-SUM (state-of-the-art unsupervised learning), and GPT-4o (a prompt fine-tuned pre-trained model), evaluating their effectiveness in video summarization. Our results demonstrate that current state-of-the-art models perform less effectively on first-person videos compared to third-person videos, highlighting the need for further advancements in the egocentric video domain. Notably, a prompt fine-tuned general-purpose GPT-4o model outperforms these specialized models, emphasizing the limitations of existing approaches in adapting to the unique challenges of first-person perspectives. Although our evaluation is conducted on a small subset of egocentric videos from the Ego-Exo4D dataset due to resource constraints, the primary objective of this research is to provide a comprehensive proof-of-concept analysis aimed at advancing the application of computer vision techniques to first-person videos. By exploring novel methodologies and evaluating their potential, we aim to contribute to the ongoing development of models capable of effectively processing and interpreting egocentric perspectives.

Related papers

• From Sight to Insight: Unleashing Eye-Tracking in Weakly Supervised Video Salient Object Detection [60.11169426478452]
  This paper aims to introduce fixation information to assist the detection of salient objects under weak supervision.<n>We propose a Position and Semantic Embedding (PSE) module to provide location and semantic guidance during the feature learning process.<n>An Intra-Inter Mixed Contrastive (MCII) model improves thetemporal modeling capabilities under weak supervision.
  arXiv  Detail & Related papers  (2025-06-30T05:01:40Z)
• EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving [61.99289768925256]
  EvaLearn is a benchmark designed to evaluate large language models (LLMs) on their learning capability and efficiency in challenging tasks.<n>We benchmark nine frontier models and observe varied performance profiles.<n>We observe that current LLMs with stronger static abilities do not show a clear advantage in learning capability across all tasks.
  arXiv  Detail & Related papers  (2025-06-03T09:18:33Z)
• A Large-Scale Analysis on Contextual Self-Supervised Video Representation Learning [22.870129496984546]
  We establish a unified benchmark that enables fair comparisons across different methods.<n>We investigate five critical aspects of self-supervised learning in videos: (1) dataset size, (2) model complexity, (3) data distribution, (4) data noise, and (5) feature representations.<n>We propose a novel approach that significantly reduces training data requirements while surpassing state-of-the-art methods that rely on 10% more pretraining data.
  arXiv  Detail & Related papers  (2025-04-08T15:47:58Z)
• Exo2Ego: Exocentric Knowledge Guided MLLM for Egocentric Video Understanding [69.96199605596138]
  Current MLLMs primarily focus on third-person (exocentric) vision, overlooking the unique aspects of first-person (egocentric) videos.<n>We propose learning the mapping between exocentric and egocentric domains to enhance egocentric video understanding.<n>We introduce Ego-ExoClip, a pre-training dataset comprising 1.1M synchronized ego-exo clip-text pairs.
  arXiv  Detail & Related papers  (2025-03-12T08:10:33Z)
• VideoWorld: Exploring Knowledge Learning from Unlabeled Videos [119.35107657321902]
  This work explores whether a deep generative model can learn complex knowledge solely from visual input.<n>We develop VideoWorld, an auto-regressive video generation model trained on unlabeled video data, and test its knowledge acquisition abilities in video-based Go and robotic control tasks.
  arXiv  Detail & Related papers  (2025-01-16T18:59:10Z)
• EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understanding [90.9111678470214]
  We propose EgoDistill, a distillation-based approach that learns to reconstruct heavy egocentric video clip features. Our method leads to significant improvements in efficiency, requiring 200x fewer GFLOPs than equivalent video models. We demonstrate its effectiveness on the Ego4D and EPICKitchens datasets, where our method outperforms state-of-the-art efficient video understanding methods.
  arXiv  Detail & Related papers  (2023-01-05T18:39:23Z)
• Unsupervised Video Summarization via Multi-source Features [4.387757291346397]
  Video summarization aims at generating a compact yet representative visual summary that conveys the essence of the original video. We propose the incorporation of multiple feature sources with chunk and stride fusion to provide more information about the visual content. For a comprehensive evaluation on the two benchmarks TVSum and SumMe, we compare our method with four state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-05-26T13:12:46Z)
• Self-supervised Co-training for Video Representation Learning [103.69904379356413]
  We investigate the benefit of adding semantic-class positives to instance-based Info Noise Contrastive Estimation training. We propose a novel self-supervised co-training scheme to improve the popular infoNCE loss. We evaluate the quality of the learnt representation on two different downstream tasks: action recognition and video retrieval.
  arXiv  Detail & Related papers  (2020-10-19T17:59:01Z)
• Unsupervised Gaze Prediction in Egocentric Videos by Energy-based Surprise Modeling [6.294759639481189]
  Egocentric perception has grown rapidly with the advent of immersive computing devices. Human gaze prediction is an important problem in analyzing egocentric videos. We quantitatively analyze the generalization capabilities of supervised, deep learning models on the egocentric gaze prediction task.
  arXiv  Detail & Related papers  (2020-01-30T21:52:38Z)

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.