arXiv:2606. 28094v1 Announce Type: cross Abstract: Real-world object removal is challenging due to two key difficulties: the target object's non-local effects, such as shadows and reflections, which are difficult to model, and the fact that user-provided masks are often inaccurate or incomplete.
By Qinming Zhou, Chenxi Sun, Deyang Kong, Junhao He, Xiangheng Tang, Peike Yu, Haotian Wu, Leilei Cao, Linfeng Zhang
arXiv:2608. 20107v1 Announce Type: new Abstract: Recent advances in generative video models have significantly improved visual realism in video object removal, yet evaluation protocols still focus on masked region fidelity, treating removal as local inpainting.
By Yigit Ekin, Enes Sanli, Aykut Erdem, Erkut Erdem, Aysegul Dundar
arXiv:2509.22650v3 Announce Type: replace
Abstract: Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models,...
By Anna Kukleva, Enis Simsar, Alessio Tonioni, Muhammad Ferjad Naeem, Federico Tombari, Jan Eric Lenssen, Bernt Schiele
This paper presents a zero‑shot framework for removing objects from real images using a frozen Stable Diffusion model, avoiding any task‑specific training. The pipeline combines SAM‑based mask construction, BLIP caption conditioning, DDIM inversion, background‑weighted masked null‑text optimization, decoder self‑attention masking, hard outside‑mask latent anchoring, and localized renoise‑denoise refinement. Experiments show effective removal of objects and context‑consistent replacement, with background‑weighted NTI especially helpful for complex backgrounds and repeated refinement reducing residual artifacts.
By Arman Taghizadeh (Institute of Cognitive Science, Osnabr\"uck University, Osnabr\"uck, Germany), Ulf Krumnack (Institute of Cognitive Science, Osnabr\"uck University, Osnabr\"uck, Germany), Kai-Uwe K\"uhnberger (Institute of Cognitive Science, Osnabr\"uck University, Osnabr\"uck, Germany)
Removing an object from a real image requires more than synthesizing plausible content within a mask: the method must suppress residual object features, preserve the unedited scene, and generate repla...
DiDA introduces a lightweight video object segmentation framework that leverages Distillation Learning of Deformable Attention. The method uses deformable attention to adapt key and value positions across frames, enabling object representations that are responsive to spatial and temporal changes. Experiments on DAVIS and YouTube‑VOS benchmarks show state‑of‑the‑art performance and efficient memory usage.
By Quang-Trung Truong, Duc Thanh Nguyen, Binh-Son Hua, Sai-Kit Yeung