Anoop Cherian

Anoop Cherian
Anoop's Quick Links

  • Biography

    Anoop was a postdoctoral researcher in the LEAR group at Inria from 2012-2015 where his research was on the estimation and tracking of human poses in videos. From 2015-2017, he was a Research Fellow at the Australian National University, where he worked on the problem of recognizing human activities in video sequences. Anoop is the recipient of the Best Student Paper award at the Intl. Conference on Image Processing in 2012. Currently, his research focus is on modeling the semantics of video data.

  • Recent News & Events

    •  NEWS    MERL Papers and Workshops at CVPR 2025
      Date: June 11, 2025 - June 15, 2025
      Where: Nashville, TN, USA
      MERL Contacts: Matthew Brand; Moitreya Chatterjee; Anoop Cherian; François Germain; Michael J. Jones; Toshiaki Koike-Akino; Jing Liu; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Kuan-Chuan Peng; Naoko Sawada; Pu (Perry) Wang; Ye Wang
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Signal Processing, Speech & Audio
      Brief
      • MERL researchers are presenting 2 conference papers, co-organizing two workshops, and presenting 7 workshop papers at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2025 conference, which will be held in Nashville, TN, USA from June 11-15, 2025. CVPR is one of the most prestigious and competitive international conferences in the area of computer vision. Details of MERL contributions are provided below:


        Main Conference Papers:

        1. "UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing" by Y.H. Lai, J. Ebbers, Y. F. Wang, F. Germain, M. J. Jones, M. Chatterjee

        This work deals with the task of weakly‑supervised Audio-Visual Video Parsing (AVVP) and proposes a novel, uncertainty-aware algorithm called UWAV towards that end. UWAV works by producing more reliable segment‑level pseudo‑labels while explicitly weighting each label by its prediction uncertainty. This uncertainty‑aware training, combined with a feature‑mixup regularization scheme, promotes inter‑segment consistency in the pseudo-labels. As a result, UWAV achieves state‑of‑the‑art performance on two AVVP datasets across multiple metrics, demonstrating both effectiveness and strong generalizability.

        Paper: https://www.merl.com/publications/TR2025-072

        2. "TailedCore: Few-Shot Sampling for Unsupervised Long-Tail Noisy Anomaly Detection" by Y. G. Jung, J. Park, J. Yoon, K.-C. Peng, W. Kim, A. B. J. Teoh, and O. Camps.

        This work tackles unsupervised anomaly detection in complex scenarios where normal data is noisy and has an unknown, imbalanced class distribution. Existing models face a trade-off between robustness to noise and performance on rare (tail) classes. To address this, the authors propose TailSampler, which estimates class sizes from embedding similarities to isolate tail samples. Using TailSampler, they develop TailedCore, a memory-based model that effectively captures tail class features while remaining noise-robust, outperforming state-of-the-art methods in extensive evaluations.

        paper: https://www.merl.com/publications/TR2025-077


        MERL Co-Organized Workshops:

        1. Multimodal Algorithmic Reasoning (MAR) Workshop, organized by A. Cherian, K.-C. Peng, S. Lohit, H. Zhou, K. Smith, L. Xue, T. K. Marks, and J. Tenenbaum.

        Workshop link: https://marworkshop.github.io/cvpr25/

        2. The 6th Workshop on Fair, Data-Efficient, and Trusted Computer Vision, organized by N. Ratha, S. Karanam, Z. Wu, M. Vatsa, R. Singh, K.-C. Peng, M. Merler, and K. Varshney.

        Workshop link: https://fadetrcv.github.io/2025/


        Workshop Papers:

        1. "FreBIS: Frequency-Based Stratification for Neural Implicit Surface Representations" by N. Sawada, P. Miraldo, S. Lohit, T.K. Marks, and M. Chatterjee (Oral)

        With their ability to model object surfaces in a scene as a continuous function, neural implicit surface reconstruction methods have made remarkable strides recently, especially over classical 3D surface reconstruction methods, such as those that use voxels or point clouds. Towards this end, we propose FreBIS - a neural implicit‑surface framework that avoids overloading a single encoder with every surface detail. It divides a scene into several frequency bands and assigns a dedicated encoder (or group of encoders) to each band, then enforces complementary feature learning through a redundancy‑aware weighting module. Swapping this frequency‑stratified stack into an off‑the‑shelf reconstruction pipeline markedly boosts 3D surface accuracy and view‑consistent rendering on the challenging BlendedMVS dataset.

        paper: https://www.merl.com/publications/TR2025-074

        2. "Multimodal 3D Object Detection on Unseen Domains" by D. Hegde, S. Lohit, K.-C. Peng, M. J. Jones, and V. M. Patel.

        LiDAR-based object detection models often suffer performance drops when deployed in unseen environments due to biases in data properties like point density and object size. Unlike domain adaptation methods that rely on access to target data, this work tackles the more realistic setting of domain generalization without test-time samples. We propose CLIX3D, a multimodal framework that uses both LiDAR and image data along with supervised contrastive learning to align same-class features across domains and improve robustness. CLIX3D achieves state-of-the-art performance across various domain shifts in 3D object detection.

        paper: https://www.merl.com/publications/TR2025-078

        3. "Improving Open-World Object Localization by Discovering Background" by A. Singh, M. J. Jones, K.-C. Peng, M. Chatterjee, A. Cherian, and E. Learned-Miller.

        This work tackles open-world object localization, aiming to detect both seen and unseen object classes using limited labeled training data. While prior methods focus on object characterization, this approach introduces background information to improve objectness learning. The proposed framework identifies low-information, non-discriminative image regions as background and trains the model to avoid generating object proposals there. Experiments on standard benchmarks show that this method significantly outperforms previous state-of-the-art approaches.

        paper: https://www.merl.com/publications/TR2025-058

        4. "PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector" by K. Li, T. Zhang, K.-C. Peng, and G. Wang.

        This work addresses challenges in 3D object detection for autonomous driving by improving the fusion of LiDAR and camera data, which is often hindered by domain gaps and limited labeled data. Leveraging advances in foundation models and prompt engineering, the authors propose PF3Det, a multi-modal detector that uses foundation model encoders and soft prompts to enhance feature fusion. PF3Det achieves strong performance even with limited training data. It sets new state-of-the-art results on the nuScenes dataset, improving NDS by 1.19% and mAP by 2.42%.

        paper: https://www.merl.com/publications/TR2025-076

        5. "Noise Consistency Regularization for Improved Subject-Driven Image Synthesis" by Y. Ni., S. Wen, P. Konius, A. Cherian

        Fine-tuning Stable Diffusion enables subject-driven image synthesis by adapting the model to generate images containing specific subjects. However, existing fine-tuning methods suffer from two key issues: underfitting, where the model fails to reliably capture subject identity, and overfitting, where it memorizes the subject image and reduces background diversity. To address these challenges, two auxiliary consistency losses are porposed for diffusion fine-tuning. First, a prior consistency regularization loss ensures that the predicted diffusion noise for prior (non- subject) images remains consistent with that of the pretrained model, improving fidelity. Second, a subject consistency regularization loss enhances the fine-tuned model’s robustness to multiplicative noise modulated latent code, helping to preserve subject identity while improving diversity. Our experimental results demonstrate the effectiveness of our approach in terms of image diversity, outperforming DreamBooth in terms of CLIP scores, background variation, and overall visual quality.

        paper: https://www.merl.com/publications/TR2025-073

        6. "LatentLLM: Attention-Aware Joint Tensor Compression" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        We propose a new framework to convert a large foundation model such as large language models (LLMs)/large multi- modal models (LMMs) into a reduced-dimension latent structure. Our method uses a global attention-aware joint tensor decomposition to significantly improve the model efficiency. We show the benefit on several benchmark including multi-modal reasoning tasks.

        paper: https://www.merl.com/publications/TR2025-075

        7. "TuneComp: Joint Fine-Tuning and Compression for Large Foundation Models" by T. Koike-Akino, X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand

        To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine- tuning the model. However, sequential fine-tuning and compression sacrifices performance, while creating a larger than necessary model as an intermediate step. In this work, we aim to reduce this gap, by directly constructing a smaller model while guided by the downstream task. We propose to jointly fine-tune and compress the model by gradually distilling it to a pruned low-rank structure. Experiments demonstrate that joint fine-tuning and compression significantly outperforms other sequential compression methods.

        paper: https://www.merl.com/publications/TR2025-079
    •  
    •  TALK    [MERL Seminar Series 2025] Petar Veličković presents talk titled Amplifying Human Performance in Combinatorial Competitive Programming
      Date & Time: Wednesday, February 26, 2025; 11:00 AM
      Speaker: Petar Veličković, Google DeepMind
      MERL Host: Anoop Cherian
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
      Abstract
      • Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady progress has been made, the highest percentiles still remain out of reach for these methods on standard competition platforms such as Codeforces. In this talk, I will describe and dive into our recent work, where we focussed on combinatorial competitive programming. In combinatorial challenges, the target is to find as-good-as-possible solutions to otherwise computationally intractable problems, over specific given inputs. We hypothesise that this scenario offers a unique testbed for human-AI synergy, as human programmers can write a backbone of a heuristic solution, after which AI can be used to optimise the scoring function used by the heuristic. We deploy our approach on previous iterations of Hash Code, a global team programming competition inspired by NP-hard software engineering problems at Google, and we leverage FunSearch to evolve our scoring functions. Our evolved solutions significantly improve the attained scores from their baseline, successfully breaking into the top percentile on all previous Hash Code online qualification rounds, and outperforming the top human teams on several. To the best of our knowledge, this is the first known AI-assisted top-tier result in competitive programming.
    •  

    See All News & Events for Anoop

  • Research Highlights

  • MERL Publications

    •  Ni, Y., Wen, S., Koniusz, P., Cherian, A., "Noise Consistency Regularization for Improved Subject-Driven Image Synthesis", IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR), June 2025.
      BibTeX TR2025-073 PDF
      • @inproceedings{Ni2025jun,
      • author = {Ni, Yao and Wen, Song and Koniusz, Piotr and Cherian, Anoop},
      • title = {{Noise Consistency Regularization for Improved Subject-Driven Image Synthesis}},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR)},
      • year = 2025,
      • month = jun,
      • url = {https://www.merl.com/publications/TR2025-073}
      • }
    •  Singh, A., Jones, M.J., Peng, K.-C., Chatterjee, M., Cherian, A., Learned-Miller, E., "Improving Open-World Object Localization by Discovering Background", CVPR Workshop on Domain Generalization: Evolution, Breakthroughs and Future Horizon, May 2025.
      BibTeX TR2025-058 PDF
      • @inproceedings{Singh2025may,
      • author = {Singh, Ashish and Jones, Michael J. and Peng, Kuan-Chuan and Chatterjee, Moitreya and Cherian, Anoop and Learned-Miller, Erik},
      • title = {{Improving Open-World Object Localization by Discovering Background}},
      • booktitle = {CVPR Workshop on Domain Generalization: Evolution, Breakthroughs and Future Horizon},
      • year = 2025,
      • month = may,
      • url = {https://www.merl.com/publications/TR2025-058}
      • }
    •  Mumcu, F., Jones, M.J., Yilmaz, Y., Cherian, A., "ComplexVAD: Detecting Interaction Anomalies in Video", IEEE Winter Conference on Applications of Computer Vision (WACV) Workshop, February 2025.
      BibTeX TR2025-016 PDF Data
      • @inproceedings{Mumcu2025feb,
      • author = {Mumcu, Furkan and Jones, Michael J. and Yilmaz, Yasin and Cherian, Anoop},
      • title = {{ComplexVAD: Detecting Interaction Anomalies in Video}},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV) Workshop},
      • year = 2025,
      • month = feb,
      • url = {https://www.merl.com/publications/TR2025-016}
      • }
    •  He, Y., Shin, S., Cherian, A., Trigoni, N., Markham, A., "SoundLoc3D: Invisible 3D Sound Source Localization and Classification Using a Multimodal RGB-D Acoustic Camera", IEEE Winter Conference on Applications of Computer Vision (WACV), December 2024, pp. 5408-5418.
      BibTeX TR2025-003 PDF
      • @inproceedings{He2024dec2,
      • author = {He, Yuhang and Shin, Sangyun and Cherian, Anoop and Trigoni, Niki and Markham, Andrew},
      • title = {{SoundLoc3D: Invisible 3D Sound Source Localization and Classification Using a Multimodal RGB-D Acoustic Camera}},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2024,
      • pages = {5408--5418},
      • month = dec,
      • publisher = {CVF},
      • url = {https://www.merl.com/publications/TR2025-003}
      • }
    •  Zhang, J., Zhang, F., Rodriguez, C., Ben-Shabat, I., Cherian, A., Gould, S., "Temporally Grounding Instructional Diagrams in Unconstrained Videos", IEEE Winter Conference on Applications of Computer Vision (WACV), December 2024, pp. 8090-8100.
      BibTeX TR2025-002 PDF
      • @inproceedings{Zhang2024dec,
      • author = {Zhang, Jiahao and Zhang, Frederic and Rodriguez, Cristian and Ben-Shabat, Itzik and Cherian, Anoop and Gould, Stephen},
      • title = {{Temporally Grounding Instructional Diagrams in Unconstrained Videos}},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2024,
      • pages = {8090--8100},
      • month = dec,
      • publisher = {CVF},
      • url = {https://www.merl.com/publications/TR2025-002}
      • }
    See All MERL Publications for Anoop

  • Other Publications

    •  Anoop Cherian and Stephen Gould, "Second-order Temporal Pooling for Action Recognition", International Journal of Computer Vision (IJCV), 2018.
      BibTeX
      • @Article{cherian2018ijcv,
      • author = {Cherian, Anoop and Gould, Stephen},
      • title = {Second-order Temporal Pooling for Action Recognition},
      • journal = {International Journal of Computer Vision (IJCV)},
      • year = 2018,
      • publisher = {Springer}
      • }
    •  Rodrigo Santa Cruz, Basura Fernando, Anoop Cherian and Stephen Gould, "Visual Permutation Learning", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018.
      BibTeX
      • @Article{cherian2018permutation,
      • author = {Santa Cruz, Rodrigo and Fernando, Basura and Cherian, Anoop and Gould, Stephen},
      • title = {Visual Permutation Learning},
      • journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
      • year = 2018,
      • publisher = {IEEE}
      • }
    •  Jue Wang, Anoop Cherian, Fatih Porikli and Stephen Gould, "Video Representation Learning Using Discriminative Pooling", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
      BibTeX
      • @Inproceedings{cherian_representation_cvpr18,
      • author = {Wang, Jue and Cherian, Anoop and Porikli, Fatih and Gould, Stephen},
      • title = {Video Representation Learning Using Discriminative Pooling},
      • booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2018
      • }
    •  Suryansh Kumar, Anoop Cherian, Yuchao Dai and Hongdong Li, "Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
      BibTeX
      • @Inproceedings{cherian_rigid_cvpr18,
      • author = {Kumar, Suryansh and Cherian, Anoop and Dai, Yuchao and Li, Hongdong},
      • title = {Scalable Dense Non-rigid Structure-from-Motion: A Grassmannian Perspective},
      • booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2018
      • }
    •  Anoop Cherian, Suvrit Sra, Stephen Gould and Richard Hartley, "Non-Linear Temporal Subspace Representations for Activity Recognition", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
      BibTeX
      • @Inproceedings{cherian_temporal_cvpr18,
      • author = {Cherian, Anoop and Sra, Suvrit and Gould, Stephen and Hartley, Richard},
      • title = {Non-Linear Temporal Subspace Representations for Activity Recognition},
      • booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2018
      • }
    •  Anoop Cherian, Basura Fernando, Mehrtash Harandi and Stephen Gould, "Generalized Rank Pooling for Activity Recognition", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
      BibTeX
      • @Inproceedings{cherian2017generalized,
      • author = {Cherian, Anoop and Fernando, Basura and Harandi, Mehrtash and Gould, Stephen},
      • title = {Generalized Rank Pooling for Activity Recognition},
      • booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2017
      • }
    •  Anoop Cherian, Panagiotis Stanitsas, Mehrtash Harandi, Vassilios Morellas and Nikolaos Papanikolopoulos, "Learning Discriminative Alpha-Beta Divergences for Positive Definite Matrices", International Conference on Computer Vision (ICCV), 2017.
      BibTeX
      • @Inproceedings{cherian_rigid_iccv17,
      • author = {Cherian, Anoop and Stanitsas, Panagiotis and Harandi, Mehrtash and Morellas, Vassilios and Papanikolopoulos, Nikolaos},
      • title = {Learning Discriminative Alpha-Beta Divergences for Positive Definite Matrices},
      • booktitle = {International Conference on Computer Vision (ICCV)},
      • year = 2017
      • }
    •  Rodrigo Santa Cruz, Basura Fernando, Anoop Cherian and Stephen Gould, "DeepPermNet: Visual Permutation Learning", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
      BibTeX
      • @Inproceedings{cruz2017deeppermnet,
      • author = {Cruz, Rodrigo Santa and Fernando, Basura and Cherian, Anoop and Gould, Stephen},
      • title = {{DeepPermNet: Visual Permutation Learning}},
      • booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2017
      • }
    •  Anoop Cherian, Vassilios Morellas and Nikolaos Papanikolopoulos, "Bayesian Non-Parametric clustering for positive definite matrices", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016.
      BibTeX
      • @Article{cherian2016bayesian,
      • author = {Cherian, Anoop and Morellas, Vassilios and Papanikolopoulos, Nikolaos},
      • title = {Bayesian Non-Parametric clustering for positive definite matrices},
      • journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
      • year = 2016,
      • publisher = {IEEE}
      • }
    •  Piotr Koniusz and Anoop Cherian, "Sparse coding for third-order super-symmetric tensor descriptors with application to texture recognition", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
      BibTeX
      • @Inproceedings{koniusz2016sparse,
      • author = {Koniusz, Piotr and Cherian, Anoop},
      • title = {Sparse coding for third-order super-symmetric tensor descriptors with application to texture recognition},
      • booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2016
      • }
    •  Piotr Koniusz, Anoop Cherian and Fatih Porikli, "Tensor representations via kernel linearization for action recognition from 3D skeletons", European Conference on Computer Vision (ECCV), 2016.
      BibTeX
      • @Inproceedings{koniusz2016tensor,
      • author = {Koniusz, Piotr and Cherian, Anoop and Porikli, Fatih},
      • title = {Tensor representations via kernel linearization for action recognition from {3D} skeletons},
      • booktitle = {European Conference on Computer Vision (ECCV)},
      • year = 2016,
      • organization = {Springer}
      • }
    •  Anoop Cherian, Julien Mairal, Karteek Alahari and Cordelia Schmid, "Mixing body-part sequences for human pose estimation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
      BibTeX
      • @Inproceedings{cherian2014mixing,
      • author = {Cherian, Anoop and Mairal, Julien and Alahari, Karteek and Schmid, Cordelia},
      • title = {Mixing body-part sequences for human pose estimation},
      • booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2014
      • }
    •  Anoop Cherian, "Nearest neighbors using compact sparse codes", International Conference on Machine Learning (ICML), 2014.
      BibTeX
      • @Inproceedings{cherian2014nearest,
      • author = {Cherian, Anoop},
      • title = {Nearest neighbors using compact sparse codes},
      • booktitle = {International Conference on Machine Learning (ICML)},
      • year = 2014
      • }
    •  Anoop Cherian and Suvrit Sra, "Riemannian sparse coding for positive definite matrices", European Conference on Computer Vision (ECCV), 2014.
      BibTeX
      • @Inproceedings{cherian2014riemannian,
      • author = {Cherian, Anoop and Sra, Suvrit},
      • title = {Riemannian sparse coding for positive definite matrices},
      • booktitle = {European Conference on Computer Vision (ECCV)},
      • year = 2014,
      • organization = {Springer}
      • }
    •  Anoop Cherian, Suvrit Sra, Arindam Banerjee and Nikolaos Papanikolopoulos, "Jensen-Bregman logdet divergence with application to efficient similarity search for covariance matrices", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2013.
      BibTeX
      • @Article{cherian2013jensen,
      • author = {Cherian, Anoop and Sra, Suvrit and Banerjee, Arindam and Papanikolopoulos, Nikolaos},
      • title = {{Jensen-Bregman} logdet divergence with application to efficient similarity search for covariance matrices},
      • journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
      • year = 2013,
      • publisher = {IEEE}
      • }
    •  Anoop Cherian, Vassilios Morellas, Nikolaos Papanikolopoulos and Saad J Bedros, "Dirichlet process mixture models on symmetric positive definite matrices for appearance clustering in video surveillance applications", Computer Vision and Pattern Recognition (CVPR), 2011.
      BibTeX
      • @Inproceedings{cherian2011dirichlet,
      • author = {Cherian, Anoop and Morellas, Vassilios and Papanikolopoulos, Nikolaos and Bedros, Saad J},
      • title = {Dirichlet process mixture models on symmetric positive definite matrices for appearance clustering in video surveillance applications},
      • booktitle = {Computer Vision and Pattern Recognition (CVPR)},
      • year = 2011
      • }
    •  Anoop Cherian, Suvrit Sra, Arindam Banerjee and Nikolaos Papanikolopoulos, "Efficient similarity search for covariance matrices via the Jensen-Bregman LogDet divergence", International Conference on Computer Vision (ICCV), 2011.
      BibTeX
      • @Inproceedings{cherian2011efficient,
      • author = {Cherian, Anoop and Sra, Suvrit and Banerjee, Arindam and Papanikolopoulos, Nikolaos},
      • title = {Efficient similarity search for covariance matrices via the Jensen-Bregman LogDet divergence},
      • booktitle = {International Conference on Computer Vision (ICCV)},
      • year = 2011
      • }
    •  Suvrit Sra and Anoop Cherian, "Generalized dictionary learning for symmetric positive definite matrices with application to nearest neighbor retrieval", Machine Learning and Knowledge Discovery in Databases (ECML), 2011.
      BibTeX
      • @Article{sra2011generalized,
      • author = {Sra, Suvrit and Cherian, Anoop},
      • title = {Generalized dictionary learning for symmetric positive definite matrices with application to nearest neighbor retrieval},
      • journal = {Machine Learning and Knowledge Discovery in Databases (ECML)},
      • year = 2011
      • }
    •  Anoop Cherian, Vassilios Morellas and Nikolaos Papanikolopoulos, "Accurate 3D ground plane estimation from a single image", International Conference on Robotics and Automation, 2009.
      BibTeX
      • @Inproceedings{cherian2009accurate,
      • author = {Cherian, Anoop and Morellas, Vassilios and Papanikolopoulos, Nikolaos},
      • title = {Accurate 3D ground plane estimation from a single image},
      • booktitle = {International Conference on Robotics and Automation},
      • year = 2009
      • }

  • Software & Data Downloads

  • Videos

  • MERL Issued Patents

    • Title: "A Method and System for Scene-Aware Audio-Video Representation"
      Inventors: Cherian, Anoop; Chatterjee, Moitreya; Le Roux, Jonathan
      Patent No.: 12,056,213
      Issue Date: Aug 6, 2024
    • Title: "Artificial Intelligence System for Classification of Data Based on Contrastive Learning"
      Inventors: Cherian, Anoop; Aeron, Shuchin
      Patent No.: 11,809,988
      Issue Date: Nov 7, 2023
    • Title: "System and Method for Manipulating Two-Dimensional (2D) Images of Three-Dimensional (3D) Objects"
      Inventors: Marks, Tim; Medin, Safa; Cherian, Anoop; Wang, Ye
      Patent No.: 11,663,798
      Issue Date: May 30, 2023
    • Title: "InSeGAN: A Generative Approach to Instance Segmentation in Depth Images"
      Inventors: Cherian, Anoop; Pais, Goncalo; Marks, Tim; Sullivan, Alan
      Patent No.: 11,651,497
      Issue Date: May 16, 2023
    • Title: "Method and System for Scene-Aware Interaction"
      Inventors: Hori, Chiori; Cherian, Anoop; Chen, Siheng; Marks, Tim; Le Roux, Jonathan; Hori, Takaaki; Harsham, Bret A.; Vetro, Anthony; Sullivan, Alan
      Patent No.: 11,635,299
      Issue Date: Apr 25, 2023
    • Title: "Scene-Aware Video Encoder System and Method"
      Inventors: Cherian, Anoop; Hori, Chiori; Le Roux, Jonathan; Marks, Tim; Sullivan, Alan
      Patent No.: 11,582,485
      Issue Date: Feb 14, 2023
    • Title: "Low-latency Captioning System"
      Inventors: Hori, Chiori; Hori, Takaaki; Cherian, Anoop; Marks, Tim; Le Roux, Jonathan
      Patent No.: 11,445,267
      Issue Date: Sep 13, 2022
    • Title: "Anomaly Detector for Detecting Anomaly using Complementary Classifiers"
      Inventors: Cherian, Anoop; Wang, Jue
      Patent No.: 11,423,698
      Issue Date: Aug 23, 2022
    • Title: "System and Method for a Dialogue Response Generation System"
      Inventors: Hori, Chiori; Cherian, Anoop; Marks, Tim; Hori, Takaaki
      Patent No.: 11,264,009
      Issue Date: Mar 1, 2022
    • Title: "Scene-Aware Video Dialog"
      Inventors: Geng, Shijie; Gao, Peng; Cherian, Anoop; Hori, Chiori; Le Roux, Jonathan
      Patent No.: 11,210,523
      Issue Date: Dec 28, 2021
    See All Patents for MERL