mlsanityData to 7 Oct 2026

HCQA @ Ego4D EgoSchema Challenge 2024

arXiv 2406.15771 ↗First posted 22 Jun 2024cs.CV cs.AIcited by 9

Not on the map. Why?

Abstract (arXiv)

In this report, we present our champion solution for Ego4D EgoSchema Challenge in CVPR 2024. To deeply integrate the powerful egocentric captioning model and question reasoning model, we propose a novel Hierarchical Comprehension scheme for egocentric video Question Answering, named HCQA. It consists of three stages: Fine-grained Caption Generation, Context-driven Summarization, and Inference-guided Answering. Given a long-form video, HCQA captures local detailed visual information and global summarised visual information via Fine-grained Caption Generation and Context-driven Summarization, respectively. Then in Inference-guided Answering, HCQA utilizes this hierarchical information to reason and answer given question. On the EgoSchema blind test set, HCQA achieves 75% accuracy in answering over 5,000 human curated multiple-choice questions. Our code will be released at https://github.com/Hyu-Zhang/HCQA.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 22 Jun 2024.

Builds on

17 of 26 references.

References · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,175
  4. 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,530
  5. 2023mPLUG-Owl: Modularization Empowers Large Language Models with Multimodalitycited by 1,038
  6. 2023EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understandingcited by 666
  7. 2024VideoAgent: Long-form Video Understanding with Large Language Model as Agentcited by 323
  8. 2022Egocentric Video-Language Pretrainingcited by 231
  9. 2022Learning Video Representations from Large Language Modelscited by 204
  10. 2022Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizerscited by 204
  11. 2023A Simple LLM Framework for Long-Range Video Question-Answeringcited by 176
  12. 2024Video ReCap: Recursive Captioning of Hour-Long Videoscited by 88
5 more
  1. 2022EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognitioncited by 48
  2. 2023Zero-Shot Video Question Answering with Procedural Programscited by 37
  3. 2023A Simple Recipe for Contrastively Pre-training Video-First Encoders Beyond 16 Framescited by 34
  4. 2024Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thoughtcited by 34
  5. 2023UniSA: Unified Generative Framework for Sentiment Analysiscited by 17

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 8; 2026: 1.

9 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering
  2. 2025Advancing Egocentric Video Question Answering with Multimodal Large Language Models
  3. 2025EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answering
  4. 2025HCNQA: Enhancing 3D VQA with Hierarchical Concentration Narrowing Supervision
  5. 2026The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering
  6. 2026The Right Inference Strategy Is All You Need: Nearly Training-Free Domain-Wise Inference for EgoCross Challenge
  7. 2024Grounded Multi-Hop VideoQA in Long-Form Egocentric Videos
  8. 2026Graph it first! Enabling Reasoning on Long-form Egocentric Videos through Scene Graphs