Recent large-scale vision-language models (VLMs) have demonstrated remarkable
capabilities in understanding and generating textual descriptions for visual content …

Zero-Shot Composed Image Retrieval (ZS-CIR) has garnered increasing interest in recent
years, which aims to retrieve a target image based on a query composed of a reference …

Multimodal large language models (MLLMs) have shown promising advancements in
general visual and language understanding. However, the representation of multimodal …

Large language models (LLMs) have achieved superior performance in powering text-
based AI agents, endowing them with decision-making and reasoning abilities akin to …

Image retrieval, ie, finding desired images given a reference image, inherently
encompasses rich, multi-faceted search intents that are difficult to capture solely using …

Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities for
capturing and reasoning over multimodal inputs. However, these models are prone to …

In Composed Video Retrieval, a video and a textual description which modifies the video
content are provided as inputs to the model. The aim is to retrieve the relevant video with the …

Visual Text-to-Speech (VTTS) aims to take the spatial environmental image as the prompt to
synthesize the reverberation speech for the spoken content. Previous research focused on …

Zero-Shot Composed Image Retrieval (ZS-CIR) supports diverse tasks with a broad range of
visual content manipulation intentions that can be related to domain, scene, object, and …

In the real world, where information is abundant and diverse across different modalities,
understanding and utilizing various data types to improve retrieval systems is a key focus of …