1 citations · 2 across the 7 of their papers we have counts for
8 papers
EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents
Dongwook Choi, Taeyoon Kwon, Bogyung Jeong +6
MLLM-powered embodied agents deployed in real-world environments encounter physical hazards. However, existing approaches lack explicit mechanisms for identifying hazards and reaso…
MATE: Meet At The Embedding -- Connecting Images with Long Texts
Young Kyun Jang, Junmo Kang, Yong Jae Lee +1
While advancements in Vision Language Models (VLMs) have significantly improved the alignment of visual and textual data, these models primarily focus on aligning images with short…
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…
Spherical Linear Interpolation and Text-Anchoring for Zero-shot Composed Image Retrieval
Young Kyun Jang, Dat Huynh, Ashish Shah +2
Composed Image Retrieval (CIR) is a complex task that retrieves images using a query, which is configured with an image and a caption that describes desired modifications to that i…
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding
Bo He, Hengduo Li, Young Kyun Jang +5
With the success of large language models (LLMs), integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However,…
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…
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…