TCLR: Temporal contrastive learning for video representation von Dave, Ishan, Gupta, Rohit, Rizve, Mamshad Nayeem, Shah, Mubarak

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Towards Realistic Semi-supervised Learning von Rizve, Mamshad Nayeem, Kardan, Navid, Shah, Mubarak

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TCLR: Temporal Contrastive Learning for Video Representation von Ishan Dave, Gupta, Rohit, Mamshad Nayeem Rizve, Shah, Mubarak

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An Automatic Bleeding Frame and Region Detection Scheme for Wireless Capsule Endoscopy Videos Based on Interplane Intensity Variation Profile in Normalized RGB Color Space von Kundu, Amit Kumar, Rizve, Mamshad Nayeem, Fattah, Shaikh Anowarul

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OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning von Rizve, Mamshad Nayeem, Kardan, Navid, Khan, Salman, Shahbaz Khan, Fahad, Shah, Mubarak

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FinePseudo: Improving Pseudo-labelling Through Temporal-Alignablity for Semi-supervised Fine-Grained Action Recognition von Dave, Ishan Rajendrakumar, Rizve, Mamshad Nayeem, Shah, Mubarak

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X-Former: Unifying Contrastive and Reconstruction Learning for MLLMs von Sirnam, Swetha, Yang, Jinyu, Neiman, Tal, Rizve, Mamshad Nayeem, Tran, Son, Yao, Benjamin, Chilimbi, Trishul, Shah, Mubarak

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FinePseudo: Improving Pseudo-Labelling through Temporal-Alignablity for Semi-Supervised Fine-Grained Action Recognition von Dave, Ishan Rajendrakumar, Rizve, Mamshad Nayeem, Shah, Mubarak

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GAReT: Cross-view Video Geolocalization with Adapters and Auto-Regressive Transformers von Pillai, Manu S, Rizve, Mamshad Nayeem, Shah, Mubarak

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Towards Realistic Semi-Supervised Learning von Rizve, Mamshad Nayeem, Kardan, Navid, Shah, Mubarak

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CDFSL-V: Cross-Domain Few-Shot Learning for Videos von Samarasinghe, Sarinda, Rizve, Mamshad Nayeem, Kardan, Navid, Shah, Mubarak

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TimeBalance: Temporally-Invariant and Temporally-Distinctive Video Representations for Semi-Supervised Action Recognition von Dave, Ishan Rajendrakumar, Rizve, Mamshad Nayeem, Chen, Chen, Shah, Mubarak

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Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video von Venkataramanan, Shashanka, Rizve, Mamshad Nayeem, Carreira, João, Asano, Yuki M, Avrithis, Yannis

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X-Former: Unifying Contrastive and Reconstruction Learning for MLLMs von Swetha, Sirnam, Yang, Jinyu, Neiman, Tal, Rizve, Mamshad Nayeem, Tran, Son, Yao, Benjamin, Chilimbi, Trishul, Shah, Mubarak

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Open Vocabulary Multi-Label Video Classification von Gupta, Rohit, Rizve, Mamshad Nayeem, Unnikrishnan, Jayakrishnan, Tawari, Ashish, Tran, Son, Shah, Mubarak, Yao, Benjamin, Chilimbi, Trishul

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Preserving Modality Structure Improves Multi-Modal Learning von Sirnam, Swetha, Rizve, Mamshad Nayeem, Shvetsova, Nina, Kuehne, Hilde, Shah, Mubarak

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VidLA: Video-Language Alignment at Scale von Rizve, Mamshad Nayeem, Fei, Fan, Unnikrishnan, Jayakrishnan, Tran, Son, Yao, Benjamin Z, Zeng, Belinda, Shah, Mubarak, Chilimbi, Trishul

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Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning von Rizve, Mamshad Nayeem, Khan, Salman, Khan, Fahad Shahbaz, Shah, Mubarak

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In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning von Rizve, Mamshad Nayeem, Duarte, Kevin, Rawat, Yogesh S, Shah, Mubarak

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OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning von Rizve, Mamshad Nayeem, Kardan, Navid, Khan, Salman, Khan, Fahad Shahbaz, Shah, Mubarak

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