REVIEW 5 cited by
Inst-Inpaint: Instructing to Remove Objects with Diffusion Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Image inpainting task refers to erasing unwanted pixels from images and filling them in a semantically consistent and realistic way. Traditionally, the pixels that are wished to be erased are defined with binary masks. From the application point of view, a user needs to generate the masks for the objects they would like to remove which can be time-consuming and prone to errors. In this work, we are interested in an image inpainting algorithm that estimates which object to be removed based on natural language input and removes it, simultaneously. For this purpose, first, we construct a dataset named GQA-Inpaint for this task. Second, we present a novel inpainting framework, Inst-Inpaint, that can remove objects from images based on the instructions given as text prompts. We set various GAN and diffusion-based baselines and run experiments on synthetic and real image datasets. We compare methods with different evaluation metrics that measure the quality and accuracy of the models and show significant quantitative and qualitative improvements.
Forward citations
Cited by 5 Pith papers
-
Teaching MLLMs to Say No: Generalized Referring Expression Comprehension via Refusal Calibrated GRPO
A recalibrated GRPO reinforcement learning method lets multimodal LLMs say 'None' for nonexistent referring expressions without sacrificing localization accuracy on objects that do exist.
-
OneReward: Unified Mask-Guided Image Generation via Multi-Task Human Preference Learning
A single VLM-based reward model, conditionable on task and evaluation dimension, drives multi-task RL that improves mask-guided image editing across four tasks without task-specific SFT.
-
Contact-Aware Amodal Completion for Human-Object Interaction via Multi-Regional Inpainting
A contact-aware, multi-regional inpainting method using a pretrained diffusion model completes occluded objects in human-object interactions without additional training.
-
AttentionDrag: Exploiting Latent Correlation Knowledge in Pre-trained Diffusion Models for Image Editing
AttentionDrag is a one-step, training-free drag-editing method that uses diffusion self-attention to move regions, generate masks, and fill gaps.
-
Translation of Text Embedding via Delta Vector to Suppress Strongly Entangled Content in Text-to-Image Diffusion Models
Subtracting the text embedding of an unwanted concept from a target word's embedding, with cross-attention keys and values steered in opposite directions, suppresses strongly entangled content in Stable Diffusion and ...
Discussion (0). Sign in to comment.