DRAgent introduces a discriminative reasoning framework for Referring Expression Segmentation that replaces direct coordinate prediction with a two‑stage selection process. First, a detector generates candidate boxes; then a multimodal large language model (MLLM) discriminates the correct target through high‑recall screening and instance‑wise verification. The chosen box serves as a spatial prompt for a foundation segmentation model, and a self‑consistency‑filtered data pipeline is used for LoRA‑based fine‑tuning, yielding competitive results on RefCOCO, RefCOCO+, and RefCOCOg.
By Yujie Qi, Luyan Zhang
arXiv:2511.22281v2 Announce Type: replace
Abstract: Observing certain patches in an image reduces the uncertainty of others. Their realization lowers the distribution entropy of each remaining patch...
By Wei Guo, Shunqi Mao, Zhuonan Liang, Xuanhua Yin, Heng Wang, Weidong Cai
arXiv:2608.22619v1 Announce Type: cross
Abstract: Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-t...
By Md Maklachur Rahman, Md Hasan Al Banna, Saraf Anjum, Mahmudul Hasan, Tracy Hammond
arXiv:2608.22064v1 Announce Type: new
Abstract: We present our solution for the MOSEv2 track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge at ECCV 2026. The challenge evaluates r...
By Mingqi Gao, Sijie Li, Jungong Han
arXiv:2608.22039v1 Announce Type: new
Abstract: Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging came...
By Sara Sabour, Linyi Jin, Richard Tucker, Amir Hertz, Marcus Brubaker, Saurabh Saxena, Junhwa Hur, Andrea Tagliasacchi, Deqing Sun, David J. Fleet, Richard Szeliski, Noah Snavely
arXiv:2608.21754v1 Announce Type: new
Abstract: Despite the success of data pruning (DP) in reducing training data sizes and improving downstream model performance in classification and segmentation...
By Dongyao Zhu, Ranga Raju Vatsavai
arXiv:2608.22532v1 Announce Type: new
Abstract: Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-fr...
By Tal Grossman, Noa Cahan, Hayit Greenspan
arXiv:2501.04001v4 Announce Type: replace
Abstract: This work presents Sa2VA, the first comprehensive, unified model for dense grounded understanding of both images and videos. Unlike existing multi-...
By Haobo Yuan, Xiangtai Li, Tao Zhang, Yueyi Sun, Zilong Huang, Shilin Xu, Shunping Ji, Yunhai Tong, Lu Qi, Jiashi Feng, Ming-Hsuan Yang
arXiv:2608.23175v1 Announce Type: new
Abstract: In light-field (LF) imaging systems, dense spatial sampling from a camera array enables powerful post-capture capabilities such as refocusing and depth...
By Sakshi Goel, Ayush Goyal, K S Venkatesh, Koteswar Rao Jerripothula
FlashReg is a GPU‑accelerated method for point cloud registration that bypasses the construction of dense second‑order compatibility graphs. It builds a sparse second‑order graph directly from a binary first‑order graph using Fast First‑ and Second‑Order Graph (FFSOG) construction, then performs a dataflow‑optimized 3‑clique search to select correspondence pivots and enumerate triples. On indoor and outdoor benchmarks, FlashReg achieves 2–3× lower correspondence‑to‑pose latency than TurboReg while using roughly half the peak tensor memory on an embedded GPU, making it suitable for high‑throughput onboard perception pipelines.
By Ziyang Yu, Xiang Li, Qiong Chang, Jun Miyazaki
arXiv:2211.04340v2 Announce Type: replace
Abstract: Autonomous driving systems must be capable of making quick decisions based on the perceived environment and specific driving conditions. Perception...
By Markus K\"angsepp, Meelis Kull
arXiv:2107.11800v2 Announce Type: replace
Abstract: Scene text detection has become an important research area in computer vision. However, dynamic changes in scenes and the complex diversity of text...
By Pengwen Dai, Feiyang He, Chaolang Li, Xugong Qin, Wenqi Ren, Xiaochun Cao
arXiv:2608.23249v1 Announce Type: new
Abstract: We consider a multistatic radio-frequency imaging problem with anisotropy, in which the reflection from a point depends on the positions of the transmi...
By Amir Rezaei, Wen-Xin Pan, Giuseppe Caire
arXiv:2501.16760v2 Announce Type: replace
Abstract: Automated interpretation of seismic images using deep learning methods is challenging because of the limited availability of training data. Few-sho...
By Surojit Saha, Ross Whitaker
arXiv:2606.22342v2 Announce Type: replace
Abstract: How does research evolve, and can we trace it at the level of individual claims? Scientific progress is not simply a uniform accumulation of facts....
By Abdul Muntakim, Md Abdullah Al Hafiz Khan, Sadid Hasan, Yong Pei
arXiv:2608.22526v1 Announce Type: new
Abstract: We introduce RS$^3$-Prune, a training-free token-pruning recipe that instantiates as a small set of inference time hooks atop existing video object seg...
By Avilasha Mandal, Sarvesh Shashikumar
arXiv:2608.22366v1 Announce Type: new
Abstract: Vision-language models (VLMs) are increasingly being used for document understanding, yet their role in Arabic and Islamic manuscript recognition remai...
By Moshiur Farazi, Firoj Alam, Abderrahmane Maaradji, Zakaria Maamar, Hamdy Mubarak, Wajdi Zaghouani
arXiv:2603.00604v2 Announce Type: replace
Abstract: High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to o...
By Keiller Nogueira, Codrut-Andrei Diaconu, D\'avid Kerekes, Jakob Gawlikowski, C\'edric L\'eonard, Nassim Ait Ali Braham, June Moh Goo, Zichao Zeng, Zhipeng Liu, Pallavi Jain, Andrea Nascetti, Ronny H\"ansch
arXiv:2608.09735v2 Announce Type: replace
Abstract: Hand and finger disorders are leading contributors to musculoskeletal disability, creating a clinical need for precise methods to quantify joint mo...
By Emmett Chen, Neal Chen, Xiang Li, Quanzheng Li, Siyeop Yoon
The paper introduces LoG, a localization‑infused vision‑language fusion framework for text‑guided medical image segmentation. LoG jointly performs multi‑scale target localization to explicitly capture target‑oriented semantics and employs three levels of localization‑infused fusion—feature, attention, and loss—to integrate spatial information into segmentation. Experiments on three benchmark datasets across three imaging modalities show that LoG consistently outperforms state‑of‑the‑art methods.
By Songyue Han, Mingye Zou, Shuchang Ye, Lei Bi, Mingyuan Meng