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While Rotary Position Embedding (RoPE) and its variants are widely adopted for their long-context capabilities, the extension of the 1D RoPE to video, with its complex spatio-temporal structure, remains an open challenge.
ActivityNet: A large-scale video benchmark for human activity understanding
Fabian Caba Heilbron, Victor Escorcia, B. G. and Niebles, J. C · 2015
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The kinetics human action video dataset
Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Green, T., Back, T., Natsev, P., et al · 2017
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Is space-time attention all you need for video understanding?, 2021
Bertasius, G., Wang, H., and Torresani, L · 2021
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Less is more: Clipbert for video-and-language learning via sparse sampling, 2021
Lei, J., Li, L., Zhou, L., Gan, Z., Berg, T. L., Bansal, M., and Liu, J · 2021
Earlier work this paper cites.
Learning transferable visual models from natural language supervision, 2021
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
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Videoclip: Contrastive pre-training for zero-shot video-text understanding, 2021
Xu, H., Ghosh, G., Huang, P.-Y., Okhonko, D., Aghajanyan, A., Metze, F., Zettlemoyer, L., and Feichtenhofer, C · 2021
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Internvideo: General video foundation models via generative and discriminative learning, 2022
Wang, Y., Li, K., Li, Y., He, Y., Huang, B., Zhao, Z., Zhang, H., Xu, J., Liu, Y., Wang, Z., Xing, S., Chen, G., Pan, J., Yu, J., Wang, Y., Wang, L., and Qiao, Y · 2022
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Advancing high-resolution video-language representation with large-scale video transcriptions
Xue, H., Hang, T., Zeng, Y., Sun, Y., Liu, B., Yang, H., Fu, J., and Guo, B · 2022
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Sharegpt4v: Improving large multi-modal models with better captions
Chen, L., Li, J., Dong, X., Zhang, P., He, C., Wang, J., Zhao, F., and Lin, D · 2023
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Vtimellm: Empower llm to grasp video moments, 2023
Huang, B., Wang, X., Chen, H., Song, Z., and Zhu, W · 2023
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Jin, P., Takanobu, R., Zhang, C., Cao, X., and Yuan, L · 2023
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Needle in a haystack - pressure testing llms
Kamradt, G · 2023
Earlier work this paper cites.
Efficient memory management for large language model serving with pagedattention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J. E., Zhang, H., and Stoica, I · 2023
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VideoChat: Chat-centric video understanding
Li, K., He, Y., Wang, Y., Li, Y., Wang, W., Luo, P., Wang, Y., Wang, L., and Qiao, Y · 2023
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Valley: Video assistant with large language model enhanced ability, 2023
Luo, R., Zhao, Z., Yang, M., Dong, J., Li, D., Lu, P., Wang, T., Hu, L., Qiu, M., and Wei, Z · 2023
Earlier work this paper cites.
Yarn: Efficient context window extension of large language models
Peng, B., Quesnelle, J., Fan, H., and Shippole, E · 2023
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Alpha-clip: A clip model focusing on wherever you want, 2023
Sun, Z., Fang, Y., Wu, T., Zhang, P., Zang, Y., Kong, S., Xiong, Y., Lin, D., and Wang, J · 2023
Cited alongside, same era.
Chatvideo: A tracklet-centric multimodal and versatile video understanding system, 2023
Wang, J., Chen, D., Luo, C., Dai, X., Yuan, L., Wu, Z., and Jiang, Y.-G · 2023
Cited alongside, same era.
Efficient streaming language models with attention sinks
Xiao, G., Tian, Y., Chen, B., Han, S., and Lewis, M · 2023
Cited alongside, same era.
Agrawal, P., Antoniak, S., Hanna, E. B., Bout, B., Chaplot, D., Chudnovsky, J., Costa, D., Monicault, B. D., Garg, S., Gervet, T., Ghosh, S., Héliou, A., Jacob, P., Jiang, A. Q., Khandelwal, K., Lacroix, T., Lample, G., Casas, D. L., Lavril, T., Scao, T. L., Lo, A., Marshall, W., Martin, L., Mensch, A., Muddireddy, P., Nemychnikova, V., Pellat, M., Platen, P. V., Raghuraman, N., Rozière, B., Sablayrolles, A., Saulnier, L., Sauvestre, R., Shang, W., Soletskyi, R., Stewart, L., Stock, P., Studnia, J., Subramanian, S., Vaze, S., Wang, T., and Yang, S · 2024
Cited alongside, same era.
Video-chatgpt: Towards detailed video understanding via large vision and language models
Maaz, M., Rasheed, H., Khan, S., and Khan, F. S · 2024
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Video-chatgpt: Towards detailed video understanding via large vision and language models
Maaz, M., Rasheed, H., Khan, S., and Khan, F. S · 2024
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Base of rope bounds context length
Men, X., Xu, M., Wang, B., Zhang, Q., Lin, H., Han, X., and Chen, W · 2024
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RoFormer: Enhanced transformer with rotary position embedding
Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., and Liu, Y · 2024
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Moss: An open conversational large language model
Sun, T., Zhang, X., He, Z., Li, P., Cheng, Q., Liu, X., Yan, H., Shao, Y., Tang, Q., Zhang, S., Zhao, X., Chen, K., Zheng, Y., Zhou, Z., Li, R., Zhan, J., Zhou, Y., Li, L., Yang, X., Wu, L., Yin, Z., Huang, X., Jiang, Y.-G., and Qiu, X · 2024
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Round and round we go! what makes rotary positional encodings useful?
Barbero, F., Vitvitskyi, A., Perivolaropoulos, C., Pascanu, R., and Veličković, P · 2024
Cited alongside, same era.
Internlm2 technical report, 2024
Cai, Z., Cao, M., Chen, H., Chen, K., Chen, K., Chen, X., Chen, X., Chen, Z., Chen, Z., Chu, P., Dong, X., Duan, H., Fan, Q., Fei, Z., Gao, Y., Ge, J., Gu, C., Gu, Y., Gui, T., Guo, A., Guo, Q., He, C., Hu, Y., Huang, T., Jiang, T., Jiao, P., Jin, Z., Lei, Z., Li, J., Li, J., Li, L., Li, S., Li, W., Li, Y., Liu, H., Liu, J., Hong, J., Liu, K., Liu, K., Liu, X., Lv, C., Lv, H., Lv, K., Ma, L., Ma, R., Ma, Z., Ning, W., Ouyang, L., Qiu, J., Qu, Y., Shang, F., Shao, Y., Song, D., Song, Z., Sui, Z., Sun, P., Sun, Y., Tang, H., Wang, B., Wang, G., Wang, J., Wang, J., Wang, R., Wang, Y., Wang, Z., Wei, X., Weng, Q., Wu, F., Xiong, Y., Xu, C., Xu, R., Yan, H., Yan, Y., Yang, X., Ye, H., Ying, H., Yu, J., Yu, J., Zang, Y., Zhang, C., Zhang, L., Zhang, P., Zhang, P., Zhang, R., Zhang, S., Zhang, S., Zhang, W., Zhang, W., Zhang, X., Zhang, X., Zhao, H., Zhao, Q., Zhao, X., Zhou, F., Zhou, Z., Zhuo, J., Zou, Y., Qiu, X., Qiao, Y., and Lin, D · 2024
Cited alongside, same era.
Auroracap: Efficient, performant video detailed captioning and a new benchmark, 2024
Chai, W., Song, E., Du, Y., Meng, C., Madhavan, V., Bar-Tal, O., Hwang, J.-N., Xie, S., and Manning, C. D · 2024
Cited alongside, same era.
Open-llava-next: An open-source implementation of llava-next series for facilitating the large multi-modal model community
Chen, L. and Xing, L · 2024
Cited alongside, same era.
Dong, X., Zhang, P., Zang, Y., Cao, Y., Wang, B., Ouyang, L., Wei, X., Zhang, S., Duan, H., Cao, M., Zhang, W., Li, Y., Yan, H., Gao, Y., Zhang, X., Li, W., Li, J., Chen, K., He, C., Zhang, X., Qiao, Y., Lin, D., and Wang, J · 2024
Cited alongside, same era.
Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
Cited alongside, same era.
Video-MME: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis
Fu, C., Dai, Y., Luo, Y., Li, L., Ren, S., Zhang, R., Wang, Z., Zhou, C., Shen, Y., Zhang, M., et al · 2024
Cited alongside, same era.
TC-LLaVA: Rethinking the transfer from image to video understanding with temporal considerations
Gao, M., Liu, J., Li, M., Xie, J., Liu, Q., Zhao, B., Chen, X., and Xiong, H · 2024
Cited alongside, same era.
Gemma: Open models based on gemini research and technology, 2024
Team, G., Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., Tafti, P., Hussenot, L., Sessa, P. G., Chowdhery, A., Roberts, A., Barua, A., Botev, A., Castro-Ros, A., Slone, A., Héliou, A., Tacchetti, A., Bulanova, A., Paterson, A., Tsai, B., Shahriari, B., Lan, C. L., Choquette-Choo, C. A., Crepy, C., Cer, D., Ippolito, D., Reid, D., Buchatskaya, E., Ni, E., Noland, E., Yan, G., Tucker, G., Muraru, G.-C., Rozhdestvenskiy, G., Michalewski, H., Tenney, I., Grishchenko, I., Austin, J., Keeling, J., Labanowski, J., Lespiau, J.-B., Stanway, J., Brennan, J., Chen, J., Ferret, J., Chiu, J., Mao-Jones, J., Lee, K., Yu, K., Millican, K., Sjoesund, L. L., Lee, L., Dixon, L., Reid, M., Mikuła, M., Wirth, M., Sharman, M., Chinaev, N., Thain, N., Bachem, O., Chang, O., Wahltinez, O., Bailey, P., Michel, P., Yotov, P., Chaabouni, R., Comanescu, R., Jana, R., Anil, R., McIlroy, R., Liu, R., Mullins, R., Smith, S. L., Borgeaud, S., Girgin, S., Douglas, S., Pandya, S., Shakeri, S., De, S., Klimenko, T., Hennigan, T., Feinberg, V., Stokowiec, W., hui Chen, Y., Ahmed, Z., Gong, Z., Warkentin, T., Peran, L., Giang, M., Farabet, C., Vinyals, O., Dean, J., Kavukcuoglu, K., Hassabis, D., Ghahramani, Z., Eck, D., Barral, J., Pereira, F., Collins, E., Joulin, A., Fiedel, N., Senter, E., Andreev, A., and Kenealy, K · 2024
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Xing, L., Huang, Q., Dong, X., Lu, J., Zhang, P., Zang, Y., Cao, Y., He, C., Wang, J., Wu, F., and Lin, D · 2024
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Pllava : Parameter-free llava extension from images to videos for video dense captioning, 2024
Xu, L., Zhao, Y., Zhou, D., Lin, Z., Ng, S. K., and Feng, J · 2024
Later among the works it cites.
Lv-eval: A balanced long-context benchmark with 5 length levels up to 256k
Yuan, T., Ning, X., Zhou, D., Yang, Z., Li, S., Zhuang, M., Tan, Z., Yao, Z., Lin, D., Li, B., et al · 2024
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MLVU: A comprehensive benchmark for multi-task long video understanding
Zhou, J., Shu, Y., Zhao, B., Wu, B., Xiao, S., Yang, X., Xiong, Y., Zhang, B., Huang, T., and Liu, Z · 2024
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Mm-ifengine: Towards multimodal instruction following
Ding, S., Wu, S., Zhao, X., Zang, Y., Duan, H., Dong, X., Zhang, P., Cao, Y., Lin, D., and Wang, J · 2025
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Google image search, 2025
Google · 2025
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Ovo-bench: How far is your video-llms from real-world online video understanding?, 2025
Li, Y., Niu, J., Miao, Z., Ge, C., Zhou, Y., He, Q., Dong, X., Duan, H., Ding, S., Qian, R., Zhang, P., Zang, Y., Cao, Y., He, C., and Wang, J · 2025
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Multimodal needle in a haystack: Benchmarking long-context capability of multimodal large language models
Wang, H., Shi, H., Tan, S., Qin, W., Wang, W., Zhang, T., Nambi, A., Ganu, T., and Wang, H · 2025
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Internlm-xcomposer2. 5-reward: A simple yet effective multi-modal reward model
Zang, Y., Dong, X., Zhang, P., Cao, Y., Liu, Z., Ding, S., Wu, S., Ma, Y., Duan, H., Zhang, W., et al · 2025
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Llava-mini: Efficient image and video large multimodal models with one vision token, 2025
Zhang, S., Fang, Q., Yang, Z., and Feng, Y · 2025
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