mlsanityData to 14 Oct 2026

Deep Learning for Video Classification and Captioning

arXiv 1609.06782 ↗First posted 22 Sep 2016cs.CV cs.MMcited by 28

Not on the map. Why?

Abstract (arXiv)

Accelerated by the tremendous increase in Internet bandwidth and storage space, video data has been generated, published and spread explosively, becoming an indispensable part of today's big data. In this paper, we focus on reviewing two lines of research aiming to stimulate the comprehension of videos with deep learning: video classification and video captioning. While video classification concentrates on automatically labeling video clips based on their semantic contents like human actions or complex events, video captioning attempts to generate a complete and natural sentence, enriching the single label as in video classification, to capture the most informative dynamics in videos. In addition, we also provide a review of popular benchmarks and competitions, which are critical for evaluating the technical progress of this vibrant field.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Sep 2016.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 8; 2023: 7; 2024: 7; 2025: 4.

28 papers.

Sort:

By area:

  1. 2022InternVideo: General Video Foundation Models via Generative and Discriminative Learningcited by 447
  2. 2023Query-Dependent Video Representation for Moment Retrieval and Highlight Detectioncited by 166
  3. 2024ST-LLM: Large Language Models Are Effective Temporal Learnerscited by 126
  4. 2022Cap4Video: What Can Auxiliary Captions Do for Text-Video Retrieval?cited by 90
  5. 2022Disentangled Representation Learning for Text-Video Retrievalcited by 77
  6. 2023UATVR: Uncertainty-Adaptive Text-Video Retrievalcited by 55
  7. 2024Lost in Time: A New Temporal Benchmark for VideoLLMscited by 42
  8. 2022Zero-Shot Video Captioning with Evolving Pseudo-Tokenscited by 25
  9. 2024PPLLaVA: Varied Video Sequence Understanding With Prompt Guidancecited by 20
  10. 2022Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representationscited by 19
  11. 2023From Association to Generation: Text-only Captioning by Unsupervised Cross-modal Mappingcited by 11
  12. 2023Boundary-Denoising for Video Activity Localizationcited by 10
16 more
  1. 2024DynFocus: Dynamic Cooperative Network Empowers LLMs with Video Understandingcited by 10
  2. 2023VLM-Eval: A General Evaluation on Video Large Language Modelscited by 7
  3. 2024Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approachcited by 5
  4. 2021Intelligent Monitoring of Stress Induced by Water Deficiency in Plants using Deep Learningcited by 3
  5. 2024VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrievalcited by 3
  6. 2025DynImg: Key Frames with Visual Prompts are Good Representation for Multi-Modal Video Understandingcited by 3
  7. 2024A Survey of Recent Advances and Challenges in Deep Audio-Visual Correlation Learningcited by 2
  8. 2022A Deep Neural Framework for Image Caption Generation Using GRU-Based Attention Mechanismcited by 1
  9. 2022Fighting FIRe with FIRE: Assessing the Validity of Text-to-Video Retrieval Benchmarkscited by 1
  10. 2025Temporal-Oriented Recipe for Transferring Large Vision-Language Model to Video Understandingcited by 1
  11. 2021Enhancing Transformer for Video Understanding Using Gated Multi-Level Attention and Temporal Adversarial Trainingcited by 0
  12. 2022Recent Advances and Challenges in Deep Audio-Visual Correlation Learningcited by 0
  13. 2023SnakeVoxFormer: Transformer-based Single ImageVoxel Reconstruction with Run Length Encodingcited by 0
  14. 2023A Comparative Study of Pre-trained CNNs and GRU-Based Attention for Image Caption Generationcited by 0
  15. 2025Images in Motion?: A First Look into Video Leakage in Collaborative Deep Learningcited by 0
  16. 2025Oracle-RLAIF: An Improved Fine-Tuning Framework for Multi-modal Video Models using Reinforcement Learning from Ranking Feedbackcited by 0