Computer Vision

Researchers

Awards

AWARD Best Paper - Honorable Mention Award at WACV 2021
Date: January 6, 2021
Awarded to: Rushil Anirudh, Suhas Lohit, Pavan Turaga
MERL Contact: Suhas Lohit
Research Areas: Computational Sensing, Computer Vision, Machine Learning
Brief
- A team of researchers from Mitsubishi Electric Research Laboratories (MERL), Lawrence Livermore National Laboratory (LLNL) and Arizona State University (ASU) received the Best Paper Honorable Mention Award at WACV 2021 for their paper "Generative Patch Priors for Practical Compressive Image Recovery".
  
  The paper proposes a novel model of natural images as a composition of small patches which are obtained from a deep generative network. This is unlike prior approaches where the networks attempt to model image-level distributions and are unable to generalize outside training distributions. The key idea in this paper is that learning patch-level statistics is far easier. As the authors demonstrate, this model can then be used to efficiently solve challenging inverse problems in imaging such as compressive image recovery and inpainting even from very few measurements for diverse natural scenes.
AWARD MERL Researchers win Best Paper Award at ICCV 2019 Workshop on Statistical Deep Learning in Computer Vision
Date: October 27, 2019
Awarded to: Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Chen Feng, Xiaoming Liu
MERL Contact: Tim K. Marks
Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
Brief
- MERL researcher Tim Marks, former MERL interns Abhinav Kumar and Wenxuan Mou, and MERL consultants Professor Chen Feng (NYU) and Professor Xiaoming Liu (MSU) received the Best Oral Paper Award at the IEEE/CVF International Conference on Computer Vision (ICCV) 2019 Workshop on Statistical Deep Learning in Computer Vision (SDL-CV) held in Seoul, Korea. Their paper, entitled "UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood Loss," describes a method which, given an image of a face, estimates not only the locations of facial landmarks but also the uncertainty of each landmark location estimate.
AWARD CVPR 2011 Longuet-Higgins Prize
Date: June 25, 2011
Awarded to: Paul A. Viola and Michael J. Jones
Awarded for: "Rapid Object Detection using a Boosted Cascade of Simple Features"
Awarded by: Conference on Computer Vision and Pattern Recognition (CVPR)
MERL Contact: Michael J. Jones
Research Area: Machine Learning
Brief
- Paper from 10 years ago with the largest impact on the field: "Rapid Object Detection using a Boosted Cascade of Simple Features", originally published at Conference on Computer Vision and Pattern Recognition (CVPR 2001).

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News & Events

TALK [MERL Seminar Series 2024] Zhaojian Li presents talk titled A Multi-Arm Robotic System for Robotic Apple Harvesting
Date & Time: Wednesday, October 2, 2024; 1:00 PM
Speaker: Zhaojian Li, Mivchigan State University
MERL Host: Yebin Wang
Research Areas: Artificial Intelligence, Computer Vision, Control, Robotics
Abstract
- Harvesting labor is the single largest cost in apple production in the U.S. Surging cost and growing shortage of labor has forced the apple industry to seek automated harvesting solutions. Despite considerable progress in recent years, the existing robotic harvesting systems still fall short of performance expectations, lacking robustness and proving inefficient or overly complex for practical commercial deployment. In this talk, I will present the development and evaluation of a new dual-arm robotic apple harvesting system. This work is a result of a continuous collaboration between Michigan State University and U.S. Department of Agriculture.
NEWS MERL Papers and Workshops at CVPR 2024
Date: June 17, 2024 - June 21, 2024
Where: Seattle, WA
MERL Contacts: Petros T. Boufounos; Moitreya Chatterjee; Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Jonathan Le Roux; Suhas Lohit; Tim K. Marks; Pedro Miraldo; Jing Liu; Kuan-Chuan Peng; Pu (Perry) Wang; Ye Wang; Matthew Brand
Research Areas: Artificial Intelligence, Computational Sensing, Computer Vision, Machine Learning, Speech & Audio
Brief
- MERL researchers are presenting 5 conference papers, 3 workshop papers, and are co-organizing two workshops at the CVPR 2024 conference, which will be held in Seattle, June 17-21. CVPR is one of the most prestigious and competitive international conferences in computer vision. Details of MERL contributions are provided below.
  
  CVPR Conference Papers:
  
  1. "TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models" by H. Ni, B. Egger, S. Lohit, A. Cherian, Y. Wang, T. Koike-Akino, S. X. Huang, and T. K. Marks
  
  This work enables a pretrained text-to-video (T2V) diffusion model to be additionally conditioned on an input image (first video frame), yielding a text+image to video (TI2V) model. Other than using the pretrained T2V model, our method requires no ("zero") training or fine-tuning. The paper uses a "repeat-and-slide" method and diffusion resampling to synthesize videos from a given starting image and text describing the video content.
  
  Paper: https://www.merl.com/publications/TR2024-059
  Project page: https://merl.com/research/highlights/TI2V-Zero
  
  2. "Long-Tailed Anomaly Detection with Learnable Class Names" by C.-H. Ho, K.-C. Peng, and N. Vasconcelos
  
  This work aims to identify defects across various classes without relying on hard-coded class names. We introduce the concept of long-tailed anomaly detection, addressing challenges like class imbalance and dataset variability. Our proposed method combines reconstruction and semantic modules, learning pseudo-class names and utilizing a variational autoencoder for feature synthesis to improve performance in long-tailed datasets, outperforming existing methods in experiments.
  
  Paper: https://www.merl.com/publications/TR2024-040
  
  3. "Gear-NeRF: Free-Viewpoint Rendering and Tracking with Motion-aware Spatio-Temporal Sampling" by X. Liu, Y-W. Tai, C-T. Tang, P. Miraldo, S. Lohit, and M. Chatterjee
  
  This work presents a new strategy for rendering dynamic scenes from novel viewpoints. Our approach is based on stratifying the scene into regions based on the extent of motion of the region, which is automatically determined. Regions with higher motion are permitted a denser spatio-temporal sampling strategy for more faithful rendering of the scene. Additionally, to the best of our knowledge, ours is the first work to enable tracking of objects in the scene from novel views - based on the preferences of a user, provided by a click.
  
  Paper: https://www.merl.com/publications/TR2024-042
  
  4. "SIRA: Scalable Inter-frame Relation and Association for Radar Perception" by R. Yataka, P. Wang, P. T. Boufounos, and R. Takahashi
  
  Overcoming the limitations on radar feature extraction such as low spatial resolution, multipath reflection, and motion blurs, this paper proposes SIRA (Scalable Inter-frame Relation and Association) for scalable radar perception with two designs: 1) extended temporal relation, generalizing the existing temporal relation layer from two frames to multiple inter-frames with temporally regrouped window attention for scalability; and 2) motion consistency track with a pseudo-tracklet generated from observational data for better object association.
  
  Paper: https://www.merl.com/publications/TR2024-041
  
  5. "RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-Visual Navigation" by Z. Yang, J. Liu, P. Chen, A. Cherian, T. K. Marks, J. L. Roux, and C. Gan
  
  We leverage Large Language Models (LLM) for zero-shot semantic audio visual navigation. Specifically, by employing multi-modal models to process sensory data, we instruct an LLM-based planner to actively explore the environment by adaptively evaluating and dismissing inaccurate perceptual descriptions.
  
  Paper: https://www.merl.com/publications/TR2024-043
  
  CVPR Workshop Papers:
  
  1. "CoLa-SDF: Controllable Latent StyleSDF for Disentangled 3D Face Generation" by R. Dey, B. Egger, V. Boddeti, Y. Wang, and T. K. Marks
  
  This paper proposes a new method for generating 3D faces and rendering them to images by combining the controllability of nonlinear 3DMMs with the high fidelity of implicit 3D GANs. Inspired by StyleSDF, our model uses a similar architecture but enforces the latent space to match the interpretable and physical parameters of the nonlinear 3D morphable model MOST-GAN.
  
  Paper: https://www.merl.com/publications/TR2024-045
  
  2. “Tracklet-based Explainable Video Anomaly Localization” by A. Singh, M. J. Jones, and E. Learned-Miller
  
  This paper describes a new method for localizing anomalous activity in video of a scene given sample videos of normal activity from the same scene. The method is based on detecting and tracking objects in the scene and estimating high-level attributes of the objects such as their location, size, short-term trajectory and object class. These high-level attributes can then be used to detect unusual activity as well as to provide a human-understandable explanation for what is unusual about the activity.
  
  Paper: https://www.merl.com/publications/TR2024-057
  
  MERL co-organized workshops:
  
  1. "Multimodal Algorithmic Reasoning Workshop" by A. Cherian, K-C. Peng, S. Lohit, M. Chatterjee, H. Zhou, K. Smith, T. K. Marks, J. Mathissen, and J. Tenenbaum
  
  Workshop link: https://marworkshop.github.io/cvpr24/index.html
  
  2. "The 5th Workshop on Fair, Data-Efficient, and Trusted Computer Vision" by K-C. Peng, et al.
  
  Workshop link: https://fadetrcv.github.io/2024/
  
  3. "SuperLoRA: Parameter-Efficient Unified Adaptation for Large Vision Models" by X. Chen, J. Liu, Y. Wang, P. Wang, M. Brand, G. Wang, and T. Koike-Akino
  
  This paper proposes a generalized framework called SuperLoRA that unifies and extends different variants of low-rank adaptation (LoRA). Introducing new options with grouping, folding, shuffling, projection, and tensor decomposition, SuperLoRA offers high flexibility and demonstrates superior performance up to 10-fold gain in parameter efficiency for transfer learning tasks.
  
  Paper: https://www.merl.com/publications/TR2024-062

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Research Highlights

Internships

CA0107: Internship - Perception-Aware Control and Planning

MERL is seeking a highly motivated and qualified intern to collaborate with the Control for Autonomy team in the development of visual perception-aware control. The overall objective is to optimize control policy where the perception uncertainty is affected by the chosen policy. Application areas include mobile robotics, drones, autonomous vehicles, and spacecraft. The ideal candidate is expected to be working towards a PhD with a strong emphasis on stochastic optimal control/planning or visual odometry and to have interest and background in as many as possible among: output-feedback optimal control, visual SLAM, POMDP, information fields, motion planning, and machine learning. The expected start date is in the late Spring/Early Summer 2025, for a duration of 3-6 months.
Required Specific Experience
- Current/Past enrollment in a PhD program in Mechanical, Aerospace, Electrical Engineering, or a related field
- 2+ years of research in at least some of: optimal control, motion planning, computer vision, navigation, uncertainty quantification, stochastic planning/control
- Strong programming skills in Python and/or C++
CV0050: Internship - Anomaly Localization for Industrial Inspection

MERL is looking for a self-motivated intern to work on anomaly localization in industrial inspection setting using computer vision. The relevant topics in the scope include (but not limited to): cross-view image anomaly localization, how to train one model for multiple views and defect types, how to incorporate large foundation models in image anomaly localization, etc. The candidates with experiences of image anomaly localization in industrial inspection settings (e.g., MVTec-AD or VisA datasets) and usage of large foundation models are strongly preferred. The ideal candidate would be a PhD student with a strong background in computer vision and machine learning, and the candidate is expected to have published at least one paper in a top-tier computer vision, machine learning, or artificial intelligence venues, such as CVPR, ECCV, ICCV, ICML, ICLR, NeurIPS, or AAAI. Proficiency in Python programming and familiarity in at least one deep learning framework are necessary. The ideal candidate is required to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The duration of the internship is ideally to be at least 3 months with a flexible start date.
Required Specific Experience
- Experience with Python, PyTorch, and large foundation models (e.g. CLIP, ALIGN, etc.).
CV0075: Internship - Multimodal Embodied AI

MERL is looking for a self-motivated intern to work on problems at the intersection of multimodal large language models and embodied AI in dynamic indoor environments. The ideal candidate would be a PhD student with a strong background in machine learning and computer vision, as demonstrated by top-tier publications. The candidate must have prior experience in designing synthetic scenes (e.g., 3D games) using popular graphics software, embodied AI, large language models, reinforcement learning, and the use of simulators such as Habitat/SoundSpaces. Hands on experience in using animated 3D human shape models (e.g., SMPL and variants) is desired. The intern is expected to collaborate with researchers in computer vision at MERL to develop algorithms and prepare manuscripts for scientific publications.
Required Specific Experience
- Experience in designing 3D interactive scenes
- Experience with vision based embodied AI using simulators (implementation on real robotic hardware would be a plus).
- Experience training large language models on multimodal data
- Experience with training reinforcement learning algorithms
- Strong foundations in machine learning and programming
- Strong track record of publications in top-tier computer vision and machine learning venues (such as CVPR, NeurIPS, etc.).

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Recent Publications

Chang, H., Boularias, A., Jain, S., "Insert-One: One-Shot Robust Visual-Force Servoing for Novel Object Insertion with 6-DoF Tracking", 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024), October 2024.
BibTeX TR2024-137 PDF
- @inproceedings{Chang2024oct,
- author = {Chang, Haonan and Boularias, Abdeslam and Jain, Siddarth}},
- title = {Insert-One: One-Shot Robust Visual-Force Servoing for Novel Object Insertion with 6-DoF Tracking},
- booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-137}
- }
Shimane, Y., Ho, K., Weiss, A., "Autonomous Horizon-Based Optical Navigation on Near-Planar Cislunar Libration Point Orbits", 4th Space Imaging Workshop, October 2024.
BibTeX TR2024-139 PDF
- @inproceedings{Shimane2024oct,
- author = {Shimane, Yuri and Ho, Koki and Weiss, Avishai}},
- title = {Autonomous Horizon-Based Optical Navigation on Near-Planar Cislunar Libration Point Orbits},
- booktitle = {4th Space Imaging Workshop},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-139}
- }
Ota, K., Jha, D.K., Jain, S., Yerazunis, W.S., Corcodel, R., Shukla, Y., Bronars, A., Romeres, D., "Autonomous Robotic Assembly: From Part Singulation to Precise Assembly", IEEE/RSJ International Conference on Intelligent Robots and Systems., October 2024.
BibTeX TR2024-133 PDF
- @inproceedings{Ota2024oct,
- author = {Ota, Kei and Jha, Devesh K. and Jain, Siddarth and Yerazunis, William S. and Corcodel, Radu and Shukla, Yash and Bronars, Antonia and Romeres, Diego}},
- title = {Autonomous Robotic Assembly: From Part Singulation to Precise Assembly},
- booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems.},
- year = 2024,
- month = oct,
- url = {https://www.merl.com/publications/TR2024-133}
- }
Hegde, D., Lohit, S., Peng, K.-C., Jones, M.J., Patel, V.M., "Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection", European Conference on Computer Vision (ECCV), Leonardis, A. and Ricci, E. and Roth, S. and Russakovsky, O. and Sattler, T. and Varol, G., Eds., DOI: 10.1007/978-3-031-73347-5_27, September 2024, pp. 475-491.
BibTeX TR2024-130 PDF Video Presentation
- @inproceedings{Hegde2024sep,
- author = {{Hegde, Deepti and Lohit, Suhas and Peng, Kuan-Chuan and Jones, Michael J. and Patel, Vishal M.}},
- title = {Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection},
- booktitle = {European Conference on Computer Vision (ECCV)},
- year = 2024,
- editor = {Leonardis, A. and Ricci, E. and Roth, S. and Russakovsky, O. and Sattler, T. and Varol, G.},
- pages = {475--491},
- month = sep,
- publisher = {Springer},
- doi = {10.1007/978-3-031-73347-5_27},
- issn = {0302-9743},
- isbn = {978-3-031-73346-8},
- url = {https://www.merl.com/publications/TR2024-130}
- }
Pais, G., Piedade, V., Chatterjee, M., Greiff, M., Miraldo, P., "A Probability-guided Sampler for Neural Implicit Surface Rendering", European Conference on Computer Vision (ECCV), September 2024.
BibTeX TR2024-129 PDF
- @inproceedings{Pais2024sep,
- author = {Pais, Goncalo and Piedade, Valter and Chatterjee, Moitreya and Greiff, Marcus and Miraldo, Pedro}},
- title = {A Probability-guided Sampler for Neural Implicit Surface Rendering},
- booktitle = {European Conference on Computer Vision (ECCV)},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-129}
- }
Cherian, A., Jain, S., Marks, T.K., "Few-shot Transparent Instance Segmentation for Bin Picking", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2024.
BibTeX TR2024-127 PDF
- @inproceedings{Cherian2024sep,
- author = {Cherian, Anoop and Jain, Siddarth and Marks, Tim K.}},
- title = {Few-shot Transparent Instance Segmentation for Bin Picking},
- booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-127}
- }
Yin, J., Luo, A., Du, Y., Cherian, A., Marks, T.K., Le Roux, J., Gan, C., "Disentangled Acoustic Fields For Multimodal Physical Scene Understanding", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2024.
BibTeX TR2024-125 PDF
- @inproceedings{Yin2024sep,
- author = {Yin, Jie and Luo, Andrew and Du, Yilun and Cherian, Anoop and Marks, Tim K. and Le Roux, Jonathan and Gan, Chuang}},
- title = {Disentangled Acoustic Fields For Multimodal Physical Scene Understanding},
- booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
- year = 2024,
- month = sep,
- url = {https://www.merl.com/publications/TR2024-125}
- }
Roque, P., Miraldo, P., Dimarogonas, D., "Multi-Agent Formation Control using Epipolar Constraints", IEEE Robotics and Automation Letters, DOI: 10.1109/LRA.2024.3444690, Vol. 9, No. 12, pp. 11002-11009, September 2024.
BibTeX TR2024-147 PDF
- @article{Roque2024sep,
- author = {Roque, Pedro and Miraldo, Pedro and Dimarogonas, Dimos}},
- title = {Multi-Agent Formation Control using Epipolar Constraints},
- journal = {IEEE Robotics and Automation Letters},
- year = 2024,
- volume = 9,
- number = 12,
- pages = {11002--11009},
- month = sep,
- doi = {10.1109/LRA.2024.3444690},
- issn = {2377-3766},
- url = {https://www.merl.com/publications/TR2024-147}
- }