activity
20162025
most citedA Multimodal Dialogue System for Conversational Image Editing

7 citations · 15 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2025

Apple Intelligence Foundation Language Models: Tech Report 2025

Ethan Li, Anders Boesen Lindbo Larsen, Chen Zhang +395

We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model opti…

cs.CL2022★ 1 cited

On Task-Adaptive Pretraining for Dialogue Response Selection

Tzu-Hsiang Lin, Ta-Chung Chi, Anna Rumshisky

Recent advancements in dialogue response selection (DRS) are based on the \textit{task-adaptive pre-training (TAP)} approach, by first initializing their model with BERT~\cite{devl…

cs.MM2021

Triple Attention Network architecture for MovieQA

Ankit Shah, Tzu-Hsiang Lin, Shijie Wu

Movie question answering, or MovieQA is a multimedia related task wherein one is provided with a video, the subtitle information, a question and candidate answers for it. The task…

cs.CV2020★ 1 cited

Robust Handwriting Recognition with Limited and Noisy Data

Hai Pham, Amrith Setlur, Saket Dingliwal +7

Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved. Most existing approaches focus on handwriting datasets that…

cs.CL2020★ 7 cited

A Multimodal Dialogue System for Conversational Image Editing

Tzu-Hsiang Lin, Trung Bui, Doo Soon Kim +1

In this paper, we present a multimodal dialogue system for Conversational Image Editing. We formulate our multimodal dialogue system as a Partially Observed Markov Decision Process…

cs.CL2020★ 2 cited

Adjusting Image Attributes of Localized Regions with Low-level Dialogue

Tzu-Hsiang Lin, Alexander Rudnicky, Trung Bui +2

Natural Language Image Editing (NLIE) aims to use natural language instructions to edit images. Since novices are inexperienced with image editing techniques, their instructions ar…

cs.CL2016★ 4 cited

Interactive Spoken Content Retrieval by Deep Reinforcement Learning

Yen-Chen Wu, Tzu-Hsiang Lin, Yang-De Chen +2

User-machine interaction is important for spoken content retrieval. For text content retrieval, the user can easily scan through and select on a list of retrieved item. This is imp…