Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices. - Sadeep Jayasumana; Richard I. Hartley; Mathieu Salzmann; Hongdong Li; Mehrtash Tafazzoli Harandi (CVPR, 2013)

Combining Multiple Manifold-Valued Descriptors for Improved Object Recognition. - Sadeep Jayasumana; Richard I. Hartley; Mathieu Salzmann; Hongdong Li; Mehrtash Tafazzoli Harandi (DICTA, 2013)

A Framework for Shape Analysis via Hilbert Space Embedding. - Sadeep Jayasumana; Mathieu Salzmann; Hongdong Li; Mehrtash Tafazzoli Harandi (ICCV, 2013)

Improved Server Architecture for Highly Efficient Message Mediation. - Hiranya Jayathilaka; Pradeep R. Fernando; Paul Fremantle; Kasun Indrasiri; Dushan Abeyruwan; Supun Kamburugamuve; Sadeep Jayasumana; Sanjiva Weerawarana; Srinath Perera (iiWAS, 2013)

Optimizing over Radial Kernels on Compact Manifolds. - Sadeep Jayasumana; Richard I. Hartley; Mathieu Salzmann; Hongdong Li; Mehrtash Tafazzoli Harandi (CVPR, 2014)

Expanding the Family of Grassmannian Kernels: An Embedding Perspective. - Mehrtash Tafazzoli Harandi; Mathieu Salzmann; Sadeep Jayasumana; Richard I. Hartley; Hongdong Li (ECCV (7), 2014)

Prototypical Priors: From Improving Classification to Zero-Shot Learning. - Saumya Jetley; Bernardino Romera-Paredes; Sadeep Jayasumana; Philip H. S. Torr (BMVC, 2015)

Conditional Random Fields as Recurrent Neural Networks. - Shuai Zheng 001; Sadeep Jayasumana; Bernardino Romera-Paredes; Vibhav Vineet; Zhizhong Su; Dalong Du; Chang Huang; Philip H. S. Torr (ICCV, 2015)

Higher Order Potentials in End-to-End Trainable Conditional Random Fields. - Anurag Arnab; Sadeep Jayasumana; Shuai Zheng 001; Philip H. S. Torr (CoRR, 2015)

Kernel Methods on Riemannian Manifolds with Gaussian RBF Kernels. - Sadeep Jayasumana; Richard I. Hartley; Mathieu Salzmann; Hongdong Li; Mehrtash Tafazzoli Harandi (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Higher Order Conditional Random Fields in Deep Neural Networks. - Anurag Arnab; Sadeep Jayasumana; Shuai Zheng 001; Philip H. S. Torr (ECCV (2), 2016)

Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction. - Anurag Arnab; Shuai Zheng 001; Sadeep Jayasumana; Bernardino Romera-Paredes; Måns Larsson; Alexander Kirillov; Bogdan Savchynskyy; Carsten Rother; Fredrik Kahl; Philip H. S. Torr (IEEE Signal Process. Mag., 2018)

Bipartite Conditional Random Fields for Panoptic Segmentation. - Sadeep Jayasumana; Kanchana Ranasinghe; Mayuka Jayawardhana; Sahan Liyanaarachchi; Harsha Ranasinghe (CoRR, 2019)

Bipartite Conditional Random Fields for Panoptic Segmentation. - Sadeep Jayasumana; Kanchana Ranasinghe; Sahan Liyanaarachchi; Mayuka Jayawardhana; Harsha Ranasinghe; Sina Samangooei (BMVC, 2020)

Kernelized Classification in Deep Networks. - Sadeep Jayasumana; Srikumar Ramalingam; Sanjiv Kumar (CoRR, 2020)

Long-tail learning via logit adjustment. - Aditya Krishna Menon; Sadeep Jayasumana; Ankit Singh Rawat; Himanshu Jain; Andreas Veit; Sanjiv Kumar (ICLR, 2021)

Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces. - Ankit Singh Rawat; Aditya Krishna Menon; Wittawat Jitkrittum; Sadeep Jayasumana; Felix X. Yu; Sashank J. Reddi; Sanjiv Kumar (ICML, 2021)

Balancing Constraints and Submodularity in Data Subset Selection. - Srikumar Ramalingam; Daniel Glasner; Kaushal Patel; Raviteja Vemulapalli; Sadeep Jayasumana; Sanjiv Kumar (CoRR, 2021)

In defense of dual-encoders for neural ranking. - Aditya Krishna Menon; Sadeep Jayasumana; Ankit Singh Rawat; Seungyeon Kim 001; Sashank J. Reddi; Sanjiv Kumar (ICML, 2022)

When does mixup promote local linearity in learned representations? - Arslan Chaudhry; Aditya Krishna Menon; Andreas Veit; Sadeep Jayasumana; Srikumar Ramalingam; Sanjiv Kumar (CoRR, 2022)

EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval. - Seungyeon Kim 001; Ankit Singh Rawat; Manzil Zaheer; Sadeep Jayasumana; Veeranjaneyulu Sadhanala; Wittawat Jitkrittum; Aditya Krishna Menon; Rob Fergus; Sanjiv Kumar (CoRR, 2023)

SPEGTI: Structured Prediction for Efficient Generative Text-to-Image Models. - Sadeep Jayasumana; Daniel Glasner; Srikumar Ramalingam; Andreas Veit; Ayan Chakrabarti; Sanjiv Kumar (CoRR, 2023)

MarkovGen: Structured Prediction for Efficient Text-to-Image Generation. - Sadeep Jayasumana; Daniel Glasner; Srikumar Ramalingam; Andreas Veit; Ayan Chakrabarti; Sanjiv Kumar (CVPR, 2024)

Rethinking FID: Towards a Better Evaluation Metric for Image Generation. - Sadeep Jayasumana; Srikumar Ramalingam; Andreas Veit; Daniel Glasner; Ayan Chakrabarti; Sanjiv Kumar (CVPR, 2024)

USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval. - Seungyeon Kim 001; Ankit Singh Rawat; Manzil Zaheer; Wittawat Jitkrittum; Veeranjaneyulu Sadhanala; Sadeep Jayasumana; Aditya Krishna Menon; Rob Fergus; Sanjiv Kumar (ICML, 2024)

Efficient Document Ranking with Learnable Late Interactions. - Ziwei Ji; Himanshu Jain; Andreas Veit; Sashank J. Reddi; Sadeep Jayasumana; Ankit Singh Rawat; Aditya Krishna Menon; Felix X. Yu; Sanjiv Kumar (CoRR, 2024)

LatentCRF: Continuous CRF for Efficient Latent Diffusion. - Kanchana Ranasinghe; Sadeep Jayasumana; Andreas Veit; Ayan Chakrabarti; Daniel Glasner; Michael S. Ryoo; Srikumar Ramalingam; Sanjiv Kumar (CoRR, 2024)