137 citations · 233 across the 26 of their papers we have counts for
10 papers · 1 filter
Avatar4D: Synthesizing Domain-Specific 4D Humans for Real-World Pose Estimation
Jerrin Bright, Zhibo Wang, Dmytro Klepachevskyi +4
We present Avatar4D, a real-world transferable pipeline for generating customizable synthetic human motion datasets tailored to domain-specific applications. Unlike prior works, wh…
Geometrically Principled Randomized Optimization for Efficient LLM Training
Sahar Rajabi, Nayeema Nonta, Sirisha Rambhatla
Low-rank gradient optimization for large language models is currently divided into two categories: structured methods that rigorously identify subspaces, and randomized approaches…
Human vs. LLM-Based Thematic Analysis for Digital Mental Health Research: Proof-of-Concept Comparative Study
Karisa Parkington, Bazen G. Teferra, Marianne Rouleau-Tang +12
Thematic analysis provides valuable insights into participants' experiences through coding and theme development, but its resource-intensive nature limits its use in large healthca…
Gen4D: Synthesizing Humans and Scenes in the Wild
Jerrin Bright, Zhibo Wang, Yuhao Chen +3
Lack of input data for in-the-wild activities often results in low performance across various computer vision tasks. This challenge is particularly pronounced in uncommon human-cen…
SafeTuneBed: A Toolkit for Benchmarking LLM Safety Alignment in Fine-Tuning
Saad Hossain, Samanvay Vajpayee, Sirisha Rambhatla
As large language models (LLMs) become ubiquitous, parameter-efficient fine-tuning methods and safety-first defenses have proliferated rapidly. However, the number of approaches an…
Estimating Quality in Therapeutic Conversations: A Multi-Dimensional Natural Language Processing Framework
Alice Rueda, Argyrios Perivolaris, Niloy Roy +15
Engagement between client and therapist is a critical determinant of therapeutic success. We propose a multi-dimensional natural language processing (NLP) framework that objectivel…
Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers
Alice Rueda, Mohammed S. Hassan, Argyrios Perivolaris +9
Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding, reasoning, and problem-solving across various domains. However, their abil…
LOCATEdit: Graph Laplacian Optimized Cross Attention for Localized Text-Guided Image Editing
Achint Soni, Meet Soni, Sirisha Rambhatla
Text-guided image editing aims to modify specific regions of an image according to natural language instructions while maintaining the general structure and the background fidelity…
LangDA: Building Context-Awareness via Language for Domain Adaptive Semantic Segmentation
Chang Liu, Bavesh Balaji, Saad Hossain +5
Unsupervised domain adaptation for semantic segmentation (DASS) aims to transfer knowledge from a label-rich source domain to a target domain with no labels. Two key approaches in…
SubTrack++ : Gradient Subspace Tracking for Scalable LLM Training
Sahar Rajabi, Nayeema Nonta, Sirisha Rambhatla
Training large language models (LLMs) is highly resource-intensive due to their massive number of parameters and the overhead of optimizer states. While recent work has aimed to re…