2 citations · 4 across the 18 of their papers we have counts for
19 papers
Extremely Sparse Supervision Incentivizes Reasoning Ability
Zhishuai Liu, Xingzi Xu, Mehmet Saygin Seyfioglu +2
Large language models demonstrate increasingly strong reasoning capabilities through effective post-training. Yet, prevailing post-training methods optimize over massive numbers of…
Trie Automata for Constrained Decoding over Large Finite Sets
Xingzi Xu, Karim Bouyarmane
Large language models increasingly need to generate structured outputs that conform to predefined schemas, with one common constraint being selection from a finite set of valid str…
Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent
Wenliang Zhong, Rob Barton, Lucas Goncalves +7
Unifying image clustering across different clustering scenarios remains challenging due to fundamental gaps among tasks. We introduce a Guideline-Driven Image Clustering Agent, the…
When Rubrics Fail: Error Enumeration as Reward in Reference-Free RL Post-Training for Virtual Try-On
Wisdom Ikezogwo, Mehmet Saygin Seyfioglu, Ranjay Krishna +1
Reinforcement learning with verifiable rewards (RLVR) and Rubrics as Rewards (RaR) have driven strong gains in domains with clear correctness signals and even in subjective domains…
Style-Instructed Mask-Free Virtual Try On
Mengqi Zhang, Qi Li, Mehmet Saygin Seyfioglu +1
Virtual Try-On is a promising research area with broad applications in e-commerce and everyday life, enabling users to visualize garments on themselves or others before purchase. M…
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…
Structured Object Language Modeling (SoLM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising
Amir Tavanaei, Kee Kiat Koo, Hayreddin Ceker +4
In this paper, we study the problem of generating structured objects that conform to a complex schema, with intricate dependencies between the different components (facets) of the…
Enhancing Multimodal Large Language Models with Multi-instance Visual Prompt Generator for Visual Representation Enrichment
Wenliang Zhong, Wenyi Wu, Qi Li +6
Multimodal Large Language Models (MLLMs) have achieved SOTA performance in various visual language tasks by fusing the visual representations with LLMs leveraging some visual adapt…
Confidence-Aware Sub-Structure Beam Search (CABS): Mitigating Hallucination in Structured Data Generation with Large Language Models
Chengwei Wei, Kee Kiat Koo, Amir Tavanaei +1
Large Language Models (LLMs) have facilitated structured data generation, with applications in domains like tabular data, document databases, product catalogs, etc. However, concer…
Diffuse to Choose: Enriching Image Conditioned Inpainting in Latent Diffusion Models for Virtual Try-All
Mehmet Saygin Seyfioglu, Karim Bouyarmane, Suren Kumar +2
As online shopping is growing, the ability for buyers to virtually visualize products in their settings-a phenomenon we define as "Virtual Try-All"-has become crucial. Recent diffu…
Catalog Phrase Grounding (CPG): Grounding of Product Textual Attributes in Product Images for e-commerce Vision-Language Applications
Wenyi Wu, Karim Bouyarmane, Ismail Tutar
We present Catalog Phrase Grounding (CPG), a model that can associate product textual data (title, brands) into corresponding regions of product images (isolated product region, br…
DreamPaint: Few-Shot Inpainting of E-Commerce Items for Virtual Try-On without 3D Modeling
Mehmet Saygin Seyfioglu, Karim Bouyarmane, Suren Kumar +2
We introduce DreamPaint, a framework to intelligently inpaint any e-commerce product on any user-provided context image. The context image can be, for example, the user's own image…