2 citations · 4 across the 18 of their papers we have counts for
8 papers · 1 filter
Enabling Fine-Grained Operating Points for Black-Box LLMs
Ege Beyazit, KL Navaneet, Prashant Mathur +3
Black-box Large Language Models (LLMs) provide practical and accessible alternatives to other machine learning methods, as they require minimal labeled data and machine learning ex…
DiT-VTON: Diffusion Transformer Framework for Unified Multi-Category Virtual Try-On and Virtual Try-All with Integrated Image Editing
Qi Li, Shuwen Qiu, Julien Han +4
The rapid growth of e-commerce has intensified the demand for Virtual Try-On (VTO) technologies, enabling customers to realistically visualize products overlaid on their own images…
Learning to Reason Efficiently with Discounted Reinforcement Learning
Alex Ayoub, Kavosh Asadi, Dale Schuurmans +2
Large reasoning models (LRMs) often consume excessive tokens, inflating computational cost and latency. More broadly, in goal reaching sequential decision problems we often want to…
InstructVTON: Optimal Auto-Masking and Natural-Language-Guided Interactive Style Control for Inpainting-Based Virtual Try-On
Julien Han, Shuwen Qiu, Qi Li +4
We present InstructVTON, an instruction-following interactive virtual try-on system that allows fine-grained and complex styling control of the resulting generation, guided by natu…
Efficient Encoder-Free Pose Conditioning and Pose Control for Virtual Try-On
Qi Li, Shuwen Qiu, Julien Han +4
As online shopping continues to grow, the demand for Virtual Try-On (VTON) technology has surged, allowing customers to visualize products on themselves by overlaying product image…
DEFT-VTON: Efficient Virtual Try-On with Consistent Generalised H-Transform
Xingzi Xu, Qi Li, Shuwen Qiu +2
Diffusion models enable high-quality virtual try-on (VTO) with their established image synthesis abilities. Despite the extensive end-to-end training of large pre-trained models in…
C2-DPO: Constrained Controlled Direct Preference Optimization
Kavosh Asadi, Julien Han, Idan Pipano +5
Direct preference optimization (\texttt{DPO}) has emerged as a promising approach for solving the alignment problem in AI. In this paper, we make two counter-intuitive observations…
Adjoint sharding for very long context training of state space models
Xingzi Xu, Amir Tavanaei, Kavosh Asadi +1
Despite very fast progress, efficiently training large language models (LLMs) in very long contexts remains challenging. Existing methods fall back to training LLMs with short cont…