43 citations · 64 across the 6 of their papers we have counts for
7 papers
DLoop: Looped Speculative Decoding
Geonmo Gu, Byeongho Heo, HeeJae Jun +4
Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens that the target model subsequ…
Verification-Aware Training for Speculative Decoding
Geonmo Gu, Byeongho Heo, HeeJae Jun +4
Speculative decoding accelerates large language model inference by using a draft model to generate candidate tokens, which are verified by the target model in a single forward pass…
MuCo: Multi-turn Contrastive Learning for Multimodal Embedding Model
Geonmo Gu, Byeongho Heo, Jaemyung Yu +7
Universal Multimodal embedding models built on Multimodal Large Language Models (MLLMs) have traditionally employed contrastive learning, which aligns representations of query-targ…
CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion
Geonmo Gu, Sanghyuk Chun, Wonjae Kim +3
This paper proposes a novel diffusion-based model, CompoDiff, for solving zero-shot Composed Image Retrieval (ZS-CIR) with latent diffusion. This paper also introduces a new synthe…
An Effective Pipeline for a Real-world Clothes Retrieval System
Yang-Ho Ji, HeeJae Jun, Insik Kim +7
In this paper, we propose an effective pipeline for clothes retrieval system which has sturdiness on large-scale real-world fashion data. Our proposed method consists of three comp…
A Benchmark on Tricks for Large-scale Image Retrieval
Byungsoo Ko, Minchul Shin, Geonmo Gu +3
Many studies have been performed on metric learning, which has become a key ingredient in top-performing methods of instance-level image retrieval. Meanwhile, less attention has be…