Olsen Hall Room 305, University of Massachusetts Lowell

One University Avenue, Lowell, MA 01854 USA

Email: bsun AT cs DOT uml DOT edu






I am currently a PhD student at the Computer Vision and Machine Learning Group of University of Massachusetts Lowell and my advisor is Prof. Kate Saenko. I got my MS from University of Massachusetts Lowell and BEng from Shandong University, both in Computer Science.


Research Interests

Computer Vision, Machine Learning, Domain Adaptation, Object Recognition, Deep Learning, Optimization



Conference Papers

[1] B. Sun, K. Saenko. From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains. British Machine Vision Conference (BMVC), 2014 [Paper] [Extended Abstract] [Poster] [Slides]

[2] B. Sun, X. Sun, Y. Wu, Y. Yin, and G. Yang. A New Algorithm for Generating Unique-Solution Sudoku. ICNC, 2008.


Journal Papers

[1] A. Chakrabarti, Y. Xiong, B. Sun, T. Darrell, D. Scharstein, T. Zickler, and K. Saenko. Modeling Radiometric Uncertainty for Vision with Tone-mapped Color Images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2014 [Paper] [Project page]


Workshop Publications

[1] B. Sun, K. Saenko. From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains. TASK-CV: Transferring and Adapting Source Knowledge in Computer Vision at ECCV 2014 [Poster]

[2] B. Schroeder, B. Sun, K. Saenko, and K. Ali. Deconstructing the Deformable Parts Model: Do More with Less. 9th Annual Workshop for Women in Machine Learning, 2014 [Paper] [Poster]

[3] B. Schroeder, B. Sun, K. Saenko, and K. Ali. Deconstructing the Deformable Parts Model: Do More with Less. Third International Workshop on Parts and Attributes at ECCV, 2014 [Paper] [Poster]


Papers Under Review

[1] B. Sun, K. Saenko. Decorrelated Subspace Adaptation for Object Recognition. In Review

[2] X. Peng, B. Sun, K. Ali, and K. Saenko. Exploring Invariances in Deep Convolutional Neural Networks Using Synthetic Images. ICLR, 2015



[1] From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains. Amazon Fall Graduate Research Symposium 2014. Seattle, 2014. [Slides]



Decorrelated Subspace Adaptation for Object Recognition

Description: Macintosh HD:Users:baochens:github:cvpr15_sw:images:concept.pngUnlike human learning, machine learning often fails to generalize to new input distributions, causing reduced accuracy. Domain adaptation approaches attempt to compensate for shifts between the training (source) and test (target) data. Existing unsupervised methods project domains into a lower-dimensional space and attempt to line up the principal dimensions, effectively learning a mapping from source to target points or vice versa. However, they fail to fully take into account the difference in the covariance structures of the two distributions, resulting in misaligned distributions even after adaptation. We present a unified view of existing adaptation using subspace mapping methods and develop a generalized approach that also incorporates feature covariance structure via decorrelation in the unified subspace. We present detailed evaluation of our approach on benchmark datasets and show improved results over published approaches. [Paper in Review]


Exploring Invariances In Deep Convolutional Neural Networks Using Synthetic Images

Description: Macintosh HD:Users:baochens:github:ICLR2015-VCNN:images:VCNN2.pngDeep convolutional neural networks learn extremely powerful image representations, yet most of that power is hidden in the millions of deep-layer parameters. What exactly do these parameters represent? Recent work has started to analyze CNN representations, finding that, e.g., they are invariant to some 2D transformations, but are confused by particular types of image noise. In this project, we delve deeper and ask: how invariant are CNNs to object-class variations caused by 3D shape, pose, and photorealism? These invariance properties are difficult to analyze using traditional data, so we propose an approach that renders synthetic data from freely available 3D CAD models. Using our approach we can easily generate an infinite amount of training images for almost any object. We explore the invariance of CNNs to various intra-class variations by simulating different rendering conditions, with surprising findings. Based on these results, we propose an optimal synthetic data generation strategy for training object detectors from CAD models. We show that our Virtual CNN approach significantly outperforms previous methods for learning object detectors from synthetic data on the benchmark PASCAL VOC2007 dataset. [Paper Submitted to ICLR 2015] [arXiv link]

Probabilistic Derendering of Camera Tone-Mapped Images

Description: Macintosh HD:Users:baochens:Desktop:derendering.pngTo produce images that are suitable for display, tone-mapping is widely used in digital cameras to map linear color measurements into narrow gamuts with limited dynamic range. This introduces non-linear distortion that must be undone, through a radiometric calibration process, before computer vision systems can analyze such photographs radiometrically. This project considers the inherent uncertainty of undoing the effects of tone-mapping. We observe that this uncertainty varies substantially across color space, making some pixels more reliable than others. We introduce a model for this uncertainty and a method for fitting it to a given camera or imaging pipeline. Once fit, the model provides for each pixel in a tone-mapped digital photograph a probability distribution over linear scene colors that could have induced it. We demonstrate how these distributions can be useful for visual inference by incorporating them into estimation algorithms for a representative set of vision tasks. [PAMI 2014] [Project page]


From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains

Description: Macintosh HD:Users:baochens:Dropbox:bmvc2014:images:overview.pngThe most successful 2D object detection methods require a large number of images annotated with object bounding boxes to be collected for training. We present an alternative approach that trains on virtual data rendered from 3D models, avoiding the need for manual labeling. Growing demand for virtual reality applications is quickly bringing about an abundance of available 3D models for a large variety of object categories. While mainstream use of 3D models in vision has focused on predicting the 3D pose of objects, we investigate the use of such freely available 3D models for multicategory 2D object detection. To address the issue of dataset bias that arises from training on virtual data and testing on real images, we propose a simple and fast adaptation approach based on decorrelated features. We also compare two kinds of virtual data, one rendered with real-image textures and one without. Evaluation on a benchmark domain adaptation dataset demonstrates that our method performs comparably to existing methods trained on large-scale real image domains. [Paper] [Extended Abstract] [Poster] [Slides]


Deconstructing the Deformable Parts Model: Do More with Less

Description: Macintosh HD:Users:baochens:Desktop:Screen Shot 2015-01-22 at 10.18.48 AM.pngThe deformable parts model (DPM) is a successful detection model that continues to achieve state-of-the-art results in object detection. While the model's success is attributed to the deformable parts, the proposed system has many other design choices such as the use of multi-resolution HOG features, the sampling of parts from high energy areas, the use of a mixture model, etc. Recently, there have been attempts at analyzing the contribution of these design choices more closely. We delve further into this analysis, performing a study of the effects of restricting the deformation model, the use of single-resolution filters versus multi-resolution filters, and the application of energy 'dropout' while sampling parts. Our results indicate that a better performance can be achieved with simpler cost-free deformation models. [Paper] [Poster]



WEAVE: Web-based Analysis and Visualization Environment

Description: Macintosh HD:Users:baochens:Desktop:Screen Shot 2015-01-22 at 10.24.43 AM.pngWeave is a new web-based visualization platform designed to enable visualization of any available data by anyone for any purpose. Weave is an application development platform supporting multiple levels of users – novice to advanced – as well as the ability to integrate, analyze and visualize data at 'nested' levels of geography, and to disseminate the results in a web page.  [Project page]







Honors and Awards

PM Raj Fund for Excellence in Computer Science  2014

Outstanding Student Scholarship (top 5-10%)  2005 – 2009

Scientific and Technological Innovation Scholarship (top 0.5%)  2008

1st Prize, National Post-Graduate Mathematical Contest in Modeling (top 4.1%)  2008

Dean's Scholarship (top 0.5%)  2007

5th Place, Robocup China Open 2007 (5/40)   2007

1st Prize, China Undergraduate Mathematical Contest in Modeling (top 2.1%)  2007


Research Experience

University of Massachusetts Lowell, Lowell, MA  01/2013 – Present

Graduate Student Researcher with Prof. Kate Saenko

Researching on computer vision and machine learning


University of Massachusetts Lowell, Lowell, MA  09/2010 – 12/2012

Graduate Student Researcher with Prof. Georges Grinstein

Researching on data visualization and data mining


Shandong University, Jinan, China  01/2008 – 2010/06

Undergraduate and Graduate Student Researcher with Prof. Xinshun Xu

Researching on machine learning and computational Intelligence


Teaching Experience

University of Massachusetts Lowell, Lowell, MA  09/2010 – 05/2013

Teaching Assistant for Machine Learning, Organization of Programming Languages, Topics in Bioinformatics, Visual Analytics, Operating Systems, and Computer Architecture.