PredErase is a training‑free method that removes objects and their photometric effects by guiding a frozen Fill model with predictive latent cues. It expands the user‑provided mask to a contact‑band region, uses I‑JEPA to generate a context‑conditioned target for the hole, and aligns Fill’s completions with this target while keeping surrounding pixels fixed. On benchmarks such as RemovalBench, RORD‑Val, and DEFACTO‑Val, PredErase improves the native FLUX.2 backbone for instance‑only masks, though supervised removers still outperform it on full‑image metrics.
By Waikit Xiu, Qiang Lu, Junbiao Chen, Xiying Li
arXiv:2603. 13994v2 Announce Type: replace-cross Abstract: Vision foundation models trained with self-supervised objectives achieve strong performance across diverse tasks and exhibit emergent object segmentation properties.
By Hossein Adeli, Seoyoung Ahn, Andrew Luo, Mengmi Zhang, Nikolaus Kriegeskorte, Gregory Zelinsky
The paper introduces a CAD‑free 3D shape prior that enhances object recognition by reconstructing each object with 3D Gaussian Splatting (3DGS) from short RGB‑D scans and fusing the resulting shape prototype with frozen DINOv2 image features. Experiments on T‑LESS and HOPE datasets show that geometry alone can match or exceed CAD‑based recognition, and that the combined approach improves performance, especially on shape‑distinctive or partially occluded objects. The study demonstrates that the benefit comes from the geometric information rather than rendered pixels, and that the prior is complementary to frozen vision features.
By Chenxi Tao, Seung-Kyum Choi
Background-Free Objectness Learning (B-FOR) is a dense, class‑agnostic detection framework that learns objectness without treating unlabeled regions as background. It predicts multi‑scale object‑center and scale fields, using spatially structured soft targets to supervise only reliable annotated areas and introduces displacement‑aware scale fields to model object extent. Experiments on PASCAL VOC, MS‑COCO, and Open Images show B‑FOR improves recall by over +10 AR points compared to prior class‑agnostic baselines, with ablation studies confirming the importance of localized supervision and displacement‑aware scaling.
By Dania Batool, Liliana Lo Presti, Marco La Cascia, Filippo Vella
arXiv:2609.28187v1 Announce Type: new
Abstract: Self-supervised vision transformers trained with DINO-style objectives exhibit striking emergent semantic representation quality across visual tasks, y...
By Basavaraj Sunagad, Artur Jesslen, Adam Kortylewski
arXiv:2608.28216v1 Announce Type: new
Abstract: Locating a specific object instance in a cluttered scene using a single reference image and a short description, and reporting when that instance is ab...
By Kishor Datta Gupta, Ahmed Rafi Hasan, Md. Mahfuzur Rahman, Md. Sadman Haque, Mohd Ariful Haque
arXiv:2607. 02404v1 Announce Type: cross Abstract: Image encoders trained with LeJEPA can deliver strong features for downstream tasks, but, like other image-level self-supervised methods, typically require large training datasets.
By Jakob Geusen, Ender Konukoglu
arXiv:2608.30964v1 Announce Type: new
Abstract: Pretrained vision embeddings are increasingly used as general-purpose representations for modelling how people appraise urban scenes, and are validated...
By Kaizhen Tan, Yuantao Deng
arXiv:2606. 03976v1 Announce Type: cross Abstract: Representations of the world, arguably, contain information about features (e.
By Lianghuan Huang, Yihao Li, Saeed Salehi, Yingshan Chang, Ansh Soni, Konrad P. Kording
arXiv:2608. 05774v1 Announce Type: cross Abstract: Joint-embedding predictive architectures learn by predicting latent representations of missing observations, yet many masked JEPAs are evaluated primarily through the encoders they produce.
By Zihan Zhou, Qifu Wen, Xi Zeng
arXiv:2609.01530v1 Announce Type: new
Abstract: Self-supervised pre-training via cross-view completion learns strong features for 3D vision from co-visible regions of image pairs. However, the refere...
By Thibaut Loiseau, Guillaume Bourmaud, Vincent Lepetit
arXiv:2606. 31421v1 Announce Type: cross Abstract: Single-stage video object detectors are increasingly deployed in time-critical applications, yet it remains unclear whether these models genuinely reason over temporal context or merely exploit a single informative frame-a gap hidden by standard metrics, which reward correct predictions regardless of how they are reached.
By Karam Tomotaki-Dawoud, Anna Hilsmann, Peter Eisert, Sebastian Bosse