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RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents.

T. Kagaya, T. Yuan, Y. Lou, J. Karlekar, S. Pranata, A. Kinose, K. Oguri, F. Wick, and Y. You. CoRR, (2024)

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Enhancement of blind watermark retrieval in drift-compensated MPEG video.S. Pranata, V. Wahadaniah, Y. Guan, H. Chua, and H. Hosseini. ISCAS (3), page 818-821. IEEE, (2003)Deep Face Recognition Model Compression via Knowledge Transfer and Distillation.J. Karlekar, J. Feng, Z. Wong, and S. Pranata. CoRR, (2019)Local Binary Pattern features for pedestrian detection at night/dark environment.Y. Cao, S. Pranata, and H. Nishimura. ICIP, page 2053-2056. IEEE, (2011)A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion.L. Xiong, J. Karlekar, J. Zhao, J. Feng, S. Pranata, and S. Shen. CoRR, (2017)Person re-identification with fusion of hand-crafted and deep pose-based body region features.J. Johnson, S. Yasugi, Y. Sugino, S. Pranata, and S. Shen. CoRR, (2018)Improved Bit Rate Control for Real-Time MPEG Watermarking.S. Pranata, V. Wahadaniah, Y. Guan, and H. Chua. EURASIP J. Adv. Signal Process., 2004 (14): 2132-2141 (2004)Anomaly Detection with Adversarial Dual Autoencoders.H. Vu, D. Ueta, K. Hashimoto, K. Maeno, S. Pranata, and S. Shen. CoRR, (2019)Equivariance and Invariance Inductive Bias for Learning from Insufficient Data.T. Wang, Q. Sun, S. Pranata, J. Karlekar, and H. Zhang. ECCV (11), volume 13671 of Lecture Notes in Computer Science, page 241-258. Springer, (2022)Stagged multi-scale LBP for pedestrian detection.Y. Cao, S. Pranata, M. Yasugi, Z. Niu, and H. Nishimura. ICIP, page 449-452. IEEE, (2012)Driver Pose Estimation Using Recurrent Lightweight Network and Virtual Data Augmented Transfer Learning.Y. Liu, P. Lasang, S. Pranata, S. Shen, and W. Zhang. IEEE Trans. Intell. Transp. Syst., 20 (10): 3818-3831 (2019)

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RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents.

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RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents.