Instruction-based image editing improves the controllability and flexibility of image
manipulation via natural commands without elaborate descriptions or regional masks …

Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in
processing vision-language tasks. One of the crux of MLLMs lies in vision tokenization …

Image color harmonization algorithm aims to automatically match the color distribution of
foreground and background images captured in different conditions. Previous deep learning …

As advanced image manipulation techniques emerge, detecting the manipulation becomes
increasingly important. Despite the success of recent learning-based approaches for image …

We review research on generating visual data from text from the angle of" cross-modal
generation." This point of view allows us to draw parallels between various methods geared …

For multimodal LLMs, the synergy of visual comprehension (textual output) and generation
(visual output) presents an ongoing challenge. This is due to a conflicting objective: for …

For decades, co-relating different data domains to attain the maximum potential of machines
has driven research, especially in neural networks. Similarly, text and visual data (images …

Editing an image automatically via a linguistic request can significantly save laborious
manual work and is friendly to photography novice. In this paper, we focus on the task of …

Visual grounding (VG) is essential to promote the human-computer interaction in object
detection tasks. Most of the current VG methods mainly focus on grounding the target objects …

Iterative language-based image manipulation aims to edit images step by step according to
user's linguistic instructions. The existing methods mostly focus on aligning the attributes …