--- title: "Who cites LLaVA-Video: Video Instruction — 116 citing papers" description: "116 papers of the Elamur Research library cite «LLaVA-Video: Video Instruction Tuning With Synthetic Data», found in their bibliographies…" canonical: "https://research.elamur.ai/free/en/papers/zhang-2024-llava-video-video-instruction-tuning/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhang-2024-llava-video-video-instruction-tuning/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [LLaVA-Video: Video Instruction Tuning With…](https://md.research.elamur.ai/free/en/papers/zhang-2024-llava-video-video-instruction-tuning/) / cited by # Who cites «LLaVA-Video: Video Instruction Tuning With Synthetic Data» 116 papers of our library cite it (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · [← back to «LLaVA-Video: Video Instruction Tuning With Synthetic Data»](https://md.research.elamur.ai/free/en/papers/zhang-2024-llava-video-video-instruction-tuning/) 1. [**Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling arXiv:2412.05271**Zhe Chen, Weiyun Wang, Yue Cao, et al.*2024 · 287 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/) 2. **[Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://md.research.elamur.ai/free/en/papers/yang-2024-thinking-space-multimodal-large-language/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Jihan Yang, Shusheng Yang, Anjali W. Gupta, et al.*2024 · 182 citations* 3. **[MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale](https://md.research.elamur.ai/free/en/papers/guo-2025-mammoth-vl-eliciting-multimodal-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiawei Guo, Tianyu Zheng, Yizhi Li, et al.*2025 · 40 citations* 4. **[Video Compression Commander: Plug-and-Play Inference Acceleration for Video Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2025-video-compression-commander-plug-play/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xuyang Liu, Yiyu Wang, Junpeng Ma, et al.*2025 · 16 citations* 5. **[Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA](https://md.research.elamur.ai/free/en/papers/park-2026-too-many-frames-not-all/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Jongwoo Park, Kanchana Ranasinghe, Kumara Kahatapitiya, et al.*2026 · 13 citations* 6. **[VLM2-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual Cues](https://md.research.elamur.ai/free/en/papers/zhang-2025-vlm2-bench-closer-look-how/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianshu Zhang, Dongyu Yao, Renjie Pi, et al.*2025 · 12 citations* 7. **[Audio-centric Video Understanding Benchmark without Text Shortcut](https://md.research.elamur.ai/free/en/papers/yang-2025-audio-centric-video-understanding-benchmark/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yudong Yang, Jimin Zhuang, Guangzhi Sun, et al.*2025 · 10 citations* 8. **[HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding](https://md.research.elamur.ai/free/en/papers/zhang-2026-hermes-kv-cache-as-hierarchical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haowei Zhang, Shudong Yang, Jinlan Fu, et al.*2026 · 9 citations* 9. **[LongVideoAgent: Multi-Agent Reasoning with Long Videos](https://md.research.elamur.ai/free/en/papers/liu-2026-longvideoagent-multi-agent-reasoning-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Runtao Liu, Ziyi Liu, Jiaqi Tang, et al.*2026 · 3 citations* 10. **[CoV: Chain-of-View Prompting for Spatial Reasoning](https://md.research.elamur.ai/free/en/papers/zhao-2026-cov-chain-view-prompting-spatial/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haoyu Zhao, Akide Liu, Zeyu Zhang, et al.*2026 · 2 citations* 11. **[Sparrow: Text-Anchored Window Attention with Visual-Semantic Glimpsing for Speculative Decoding in Video LLMs](https://md.research.elamur.ai/free/en/papers/zhang-2026-sparrow-text-anchored-window-attention/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Libo Zhang, Zhaoning Zhang, Hongwanyang, et al.*2026 · 2 citations* 12. **[Agentic Very Long Video Understanding](https://md.research.elamur.ai/free/en/papers/rege-2026-agentic-very-long-video-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aniket Rege, Arka Sadhu, Yuliang Li, et al.*2026 · 1 citation* 13. **[EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence](https://md.research.elamur.ai/free/en/papers/she-2026-echovlm-dynamic-mixture-experts-vision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chaoyin She, Ruifang Lu, Lida Chen, et al.*2026 · 1 citation* 14. **[A Survey of Large Models in Sports](https://md.research.elamur.ai/free/en/papers/xu-2026-survey-large-models-sports/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yichen Xu, Jianzhe Ma, Chuhan Wang, et al.*2026* 15. **[Black-Box Membership Inference Attacks for Video Training Data in Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-black-box-membership-inference-attacks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinrui Wang, Zhenfeng Gao, Wendan Wang, et al.*2026* 16. **[Can Video LLMs See Through Illusions? Video-Illusion QA Benchmark Dataset](https://md.research.elamur.ai/free/en/papers/ohira-2026-can-video-llms-see-through/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Souto Ohira, Tosho Hirasawa, Mamoru Komachi*2026* 17. **[MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating Multimodal LLMs in Multi-Turn Dialogues](https://md.research.elamur.ai/free/en/papers/pan-2026-mt-video-bench-holistic-video/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yaning Pan, Qianqian Xie, Guohui Zhang, et al.*2026* 18. **[MVP: Enhancing Video Large Language Models via Self-supervised Masked Video Prediction](https://md.research.elamur.ai/free/en/papers/sun-2026-mvp-enhancing-video-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaokun Sun, Zezhong Wu, Zewen Ding, et al.*2026* 19. **[Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering](https://md.research.elamur.ai/free/en/papers/wang-2026-putting-captions-test-evaluating-video/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zizhen Wang, Bo Feng, Zhengfeng Lai, et al.*2026* 20. **[Spatiotemporal Sycophancy: Negation-Based Gaslighting in Video Large Language Models](https://md.research.elamur.ai/free/en/papers/tang-2026-spatiotemporal-sycophancy-negation-based-gaslighting/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ziyao Tang, Pengkun Jiao, Bin Zhu, et al.*2026* 96 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/zhang-2024-llava-video-video-instruction-tuning/) — LLaVA-Video: Video Instruction Tuning With Synthetic Data