2 citations · 2 across the 10 of their papers we have counts for
11 papers
Scaling Up Temporal Domain Generalization via Temporal Experts Averaging
Aoming Liu, Kevin Miller, Venkatesh Saligrama +4
Temporal Domain Generalization (TDG) aims to generalize across temporal distribution shifts, e.g., lexical change over time. Prior work often addresses this by predicting future mo…
OP-LoRA: The Blessing of Dimensionality
Piotr Teterwak, Kate Saenko, Bryan A. Plummer +1
Low-rank adapters (LoRA) enable finetuning of large models with only a small number of parameters. However, they often suffer from an ill-conditioned loss landscape, leading to dif…
Scene Co-pilot: Procedural Text to Video Generation with Human in the Loop
Zhaofang Qian, Abolfazl Sharifi, Tucker Carroll +1
Video generation has achieved impressive quality, but it still suffers from artifacts such as temporal inconsistency and violation of physical laws. Leveraging 3D scenes can fundam…
Distilling Vision-Language Pretraining for Efficient Cross-Modal Retrieval
Young Kyun Jang, Donghyun Kim, Ser-nam Lim
``Learning to hash'' is a practical solution for efficient retrieval, offering fast search speed and low storage cost. It is widely applied in various applications, such as image-t…
Towards Cross-modal Backward-compatible Representation Learning for Vision-Language Models
Young Kyun Jang, Ser-nam Lim
Modern retrieval systems often struggle with upgrading to new and more powerful models due to the incompatibility of embeddings between the old and new models. This necessitates a…
Visual Delta Generator with Large Multi-modal Models for Semi-supervised Composed Image Retrieval
Young Kyun Jang, Donghyun Kim, Zihang Meng +2
Composed Image Retrieval (CIR) is a task that retrieves images similar to a query, based on a provided textual modification. Current techniques rely on supervised learning for CIR…
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning
Dongmin Park, Zhaofang Qian, Guangxing Han +1
Mitigating hallucinations of Large Vision Language Models,(LVLMs) is crucial to enhance their reliability for general-purpose assistants. This paper shows that such hallucinations…
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks
Xuanming Cui, Alejandro Aparcedo, Young Kyun Jang +1
Recent advances in instruction tuning have led to the development of State-of-the-Art Large Multimodal Models (LMMs). Given the novelty of these models, the impact of visual advers…
Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model
Shraman Pramanick, Guangxing Han, Rui Hou +6
The ability of large language models (LLMs) to process visual inputs has given rise to general-purpose vision systems, unifying various vision-language (VL) tasks by instruction tu…
CLAMP: Contrastive LAnguage Model Prompt-tuning
Piotr Teterwak, Ximeng Sun, Bryan A. Plummer +2
Large language models (LLMs) have emerged as powerful general-purpose interfaces for many machine learning problems. Recent work has adapted LLMs to generative visual tasks like im…
A Stable and Efficient Covariate-Balancing Estimator for Causal Survival Effects
Khiem Pham, David A. Hirshberg, Phuong-Mai Huynh-Pham +3
We propose an empirically stable and asymptotically efficient covariate-balancing approach to the problem of estimating survival causal effects in data with conditionally-independe…