Do Egocentric Video-Language Models Truly Understand Hand-Object Interactions?

• URL: http://arxiv.org/abs/2405.17719v3
• Date: Thu, 20 Feb 2025 04:28:19 GMT
• Title: Do Egocentric Video-Language Models Truly Understand Hand-Object Interactions?
• Authors: Boshen Xu, Ziheng Wang, Yang Du, Zhinan Song, Sipeng Zheng, Qin Jin,
• Abstract summary: Egocentric video-language pretraining is a crucial step in advancing the understanding of hand-object interactions in first-person scenarios. Despite successes on existing testbeds, we find that current EgoVLMs can be easily misled by simple modifications. This raises the question: Do EgoVLMs truly understand hand-object interactions?
• Score: 48.702973928321946
• License:
• Abstract: Egocentric video-language pretraining is a crucial step in advancing the understanding of hand-object interactions in first-person scenarios. Despite successes on existing testbeds, we find that current EgoVLMs can be easily misled by simple modifications, such as changing the verbs or nouns in interaction descriptions, with models struggling to distinguish between these changes. This raises the question: Do EgoVLMs truly understand hand-object interactions? To address this question, we introduce a benchmark called EgoHOIBench, revealing the performance limitation of current egocentric models when confronted with such challenges. We attribute this performance gap to insufficient fine-grained supervision and the greater difficulty EgoVLMs experience in recognizing verbs compared to nouns. To tackle these issues, we propose a novel asymmetric contrastive objective named EgoNCE++. For the video-to-text objective, we enhance text supervision by generating negative captions using large language models or leveraging pretrained vocabulary for HOI-related word substitutions. For the text-to-video objective, we focus on preserving an object-centric feature space that clusters video representations based on shared nouns. Extensive experiments demonstrate that EgoNCE++ significantly enhances EgoHOI understanding, leading to improved performance across various EgoVLMs in tasks such as multi-instance retrieval, action recognition, and temporal understanding. Our code is available at https://github.com/xuboshen/EgoNCEpp.

Related papers

• HENASY: Learning to Assemble Scene-Entities for Egocentric Video-Language Model [9.762722976833581]
  Current models rely extensively on instance-level alignment between video and language modalities. We take an inspiration from human perception and explore a compositional approach for ego video representation.
  arXiv  Detail & Related papers  (2024-06-01T05:41:12Z)
• Retrieval-Augmented Egocentric Video Captioning [53.2951243928289]
  EgoInstructor is a retrieval-augmented multimodal captioning model that automatically retrieves semantically relevant third-person instructional videos. We train the cross-view retrieval module with a novel EgoExoNCE loss that pulls egocentric and exocentric video features closer by aligning them to shared text features that describe similar actions.
  arXiv  Detail & Related papers  (2024-01-01T15:31:06Z)
• Helping Hands: An Object-Aware Ego-Centric Video Recognition Model [60.350851196619296]
  We introduce an object-aware decoder for improving the performance of ego-centric representations on ego-centric videos. We show that the model can act as a drop-in replacement for an ego-awareness video model to improve performance through visual-text grounding.
  arXiv  Detail & Related papers  (2023-08-15T17:58:11Z)
• Verbs in Action: Improving verb understanding in video-language models [128.87443209118726]
  State-of-the-art video-language models based on CLIP have been shown to have limited verb understanding. We improve verb understanding for CLIP-based video-language models by proposing a new Verb-Focused Contrastive framework.
  arXiv  Detail & Related papers  (2023-04-13T17:57:01Z)
• Egocentric Video-Language Pretraining [74.04740069230692]
  Video-Language Pretraining aims to learn transferable representation to advance a wide range of video-text downstream tasks. We exploit the recently released Ego4D dataset to pioneer Egocentric training along three directions. We demonstrate strong performance on five egocentric downstream tasks across three datasets.
  arXiv  Detail & Related papers  (2022-06-03T16:28:58Z)
• Learning to Recognize Actions on Objects in Egocentric Video with Attention Dictionaries [51.48859591280838]
  We present EgoACO, a deep neural architecture for video action recognition. It learns to pool action-context-object descriptors from frame level features. Cap uses self-attention with a dictionary of learnable weights to pool from the most relevant feature regions.
  arXiv  Detail & Related papers  (2021-02-16T10:26:04Z)

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.