7 papers
Rethinking the Good Enough Embedding for Easy Few-Shot Learning
Michael Karnes, Alper Yilmaz
The field of deep visual recognition is undergoing a paradigm shift toward universal representations. The Platonic Representation Hypothesis suggests that diverse architectures tra…
MotivNet: Evolving Meta-Sapiens into an Emotionally Intelligent Foundation Model
Rahul Medicharla, Alper Yilmaz
In this paper, we introduce MotivNet, a generalizable facial emotion recognition model for robust real-world application. Current state-of-the-art FER models tend to have weak gene…
CascadeFormer: A Family of Two-stage Cascading Transformers for Skeleton-based Human Action Recognition
Yusen Peng, Alper Yilmaz
Skeleton-based human action recognition leverages sequences of human joint coordinates to identify actions performed in videos. Owing to the intrinsic spatiotemporal structure of s…
THYME: Temporal Hierarchical-Cyclic Interactivity Modeling for Video Scene Graphs in Aerial Footage
Trong-Thuan Nguyen, Pha Nguyen, Jackson Cothren +3
The rapid proliferation of video in applications such as autonomous driving, surveillance, and sports analytics necessitates robust methods for dynamic scene understanding. Despite…
HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation
Trong-Thuan Nguyen, Pha Nguyen, Jackson Cothren +2
Multimodal LLMs have advanced vision-language tasks but still struggle with understanding video scenes. To bridge this gap, Video Scene Graph Generation (VidSGG) has emerged to cap…
CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos
Trong-Thuan Nguyen, Pha Nguyen, Xin Li +3
Video scene graph generation (VidSGG) has emerged as a transformative approach to capturing and interpreting the intricate relationships among objects and their temporal dynamics i…