19 citations · 62 across the 16 of their papers we have counts for
17 papers
Optimizing Occupancy Sensor Placement in Smart Environments
Hao Lu, Richard J. Radke
Understanding the locations of occupants in a commercial built environment is critical for realizing energy savings by delivering lighting, heating, and cooling only where it is ne…
Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusion
Meng Zheng, Benjamin Planche, Zhongpai Gao +3
Conventional 3D medical image segmentation methods typically require learning heavy 3D networks (e.g., 3D-UNet), as well as large amounts of in-domain data with accurate pixel/voxe…
Multimodality in Group Communication Research
Robin Lange, Brooke Foucault Welles, Gyanendra Sharma +3
Team interactions are often multisensory, requiring members to pick up on verbal, visual, spatial and body language cues. Multimodal research, research that captures multiple modes…
How Voice and Helpfulness Shape Perceptions in Human-Agent Teams
Samuel Westby, Richard J. Radke, Christoph Riedl +1
Voice assistants are increasingly prevalent, from personal devices to team environments. This study explores how voice type and contribution quality influence human-agent team perf…
Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation
Ashraful Islam, Ben Lundell, Harpreet Sawhney +3
We present a self-supervised learning (SSL) method suitable for semi-global tasks such as object detection and semantic segmentation. We enforce local consistency between self-lear…
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data
Ashraful Islam, Chun-Fu Chen, Rameswar Panda +3
Most existing works in few-shot learning rely on meta-learning the network on a large base dataset which is typically from the same domain as the target dataset. We tackle the prob…
A Broad Study on the Transferability of Visual Representations with Contrastive Learning
Ashraful Islam, Chun-Fu Chen, Rameswar Panda +3
Tremendous progress has been made in visual representation learning, notably with the recent success of self-supervised contrastive learning methods. Supervised contrastive learnin…
A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization
Ashraful Islam, Chengjiang Long, Richard Radke
Weakly supervised temporal action localization is a challenging vision task due to the absence of ground-truth temporal locations of actions in the training videos. With only video…
Towards Visually Explaining Similarity Models
Meng Zheng, Srikrishna Karanam, Terrence Chen +2
We consider the problem of visually explaining similarity models, i.e., explaining why a model predicts two images to be similar in addition to producing a scalar score. While much…
Weakly Supervised Temporal Action Localization Using Deep Metric Learning
Ashraful Islam, Richard J. Radke
Temporal action localization is an important step towards video understanding. Most current action localization methods depend on untrimmed videos with full temporal annotations of…
Multi-person Spatial Interaction in a Large Immersive Display Using Smartphones as Touchpads
Gyanendra Sharma, Richard J Radke
In this paper, we present a multi-user interaction interface for a large immersive space that supports simultaneous screen interactions by combining (1) user input via personal sma…
Towards Visually Explaining Variational Autoencoders
Wenqian Liu, Runze Li, Meng Zheng +5
Recent advances in Convolutional Neural Network (CNN) model interpretability have led to impressive progress in visualizing and understanding model predictions. In particular, grad…
Visual Similarity Attention
Meng Zheng, Srikrishna Karanam, Terrence Chen +2
While there has been substantial progress in learning suitable distance metrics, these techniques in general lack transparency and decision reasoning, i.e., explaining why the inpu…
Re-Identification with Consistent Attentive Siamese Networks
Meng Zheng, Srikrishna Karanam, Ziyan Wu +1
We propose a new deep architecture for person re-identification (re-id). While re-id has seen much recent progress, spatial localization and view-invariant representation learning…
Measuring the Temporal Behavior of Real-World Person Re-Identification
Meng Zheng, Srikrishna Karanam, Richard J. Radke
Designing real-world person re-identification (re-id) systems requires attention to operational aspects not typically considered in academic research. Typically, the probe image or…
Rank Persistence: Assessing the Temporal Performance of Real-World Person Re-Identification
Srikrishna Karanam, Eric Lam, Richard J. Radke
Designing useful person re-identification systems for real-world applications requires attention to operational aspects not typically considered in academic research. Here, we focu…
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets
Srikrishna Karanam, Mengran Gou, Ziyan Wu +3
Person re-identification (re-id) is a critical problem in video analytics applications such as security and surveillance. The public release of several datasets and code for vision…