16 citations · 33 across the 9 of their papers we have counts for
10 papers
K9-Bench: Evaluating Multimodal LLMs on Canine-Centric Videos
Khush Attarde, Yusuf Ali, Megha Thukral +3
MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text. A crucial, yet underexplored, application of these models lies i…
Encoder-Free Human Motion Understanding via Structured Motion Descriptions
Yao Zhang, Zhuchenyang Liu, Thomas Ploetz +1
The world knowledge and reasoning capabilities of text-based large language models (LLMs) are advancing rapidly, yet current approaches to human motion understanding, including mot…
Fine-grained Motion Retrieval via Joint-Angle Motion Images and Token-Patch Late Interaction
Yao Zhang, Zhuchenyang Liu, Yanlan He +2
Text-motion retrieval aims to learn a semantically aligned latent space between natural language descriptions and 3D human motion skeleton sequences, enabling bidirectional search…
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition
Harish Haresamudram, Irfan Essa, Thomas Ploetz
Human activity recognition (HAR) in wearable computing is typically based on direct processing of sensor data. Sensor readings are translated into representations, either derived t…
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition
Shuai Shao, Yu Guan, Xin Guan +2
Human Activity Recognition (HAR) is one of the core research areas in mobile and wearable computing. With the application of deep learning (DL) techniques such as CNN, recognizing…
PLAN-B: Predicting Likely Alternative Next Best Sequences for Action Prediction
Dan Scarafoni, Irfan Essa, Thomas Ploetz
Action prediction focuses on anticipating actions before they happen. Recent works leverage probabilistic approaches to describe future uncertainties and sample future actions. How…
Contrastive Predictive Coding for Human Activity Recognition
Harish Haresamudram, Irfan Essa, Thomas Ploetz
Feature extraction is crucial for human activity recognition (HAR) using body-worn movement sensors. Recently, learned representations have been used successfully, offering promisi…
Transfer Learning for Activity Recognition in Mobile Health
Yuchao Ma, Andrew T. Campbell, Diane J. Cook +6
While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation. Aimin…
IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition
Hyeokhyen Kwon, Catherine Tong, Harish Haresamudram +4
The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR). Labele…
Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach
BingZhang Hu, Yu Guan, Yan Gao +3
Gait as a biometric trait has attracted much attention in many security and privacy applications such as identity recognition and authentication, during the last few decades. Becau…