A key challenge in registering pre- and post-operative brain tumor images lies in the anatomical inconsistencies caused by pathological changes and surgical resections. Recent efforts have addressed this issue by masking affected regions during optimization, but such approaches discard contextual information and rely on CNN backbones that implicitly model deformation, often overfitting to distant normal tissues and failing to capture the severe nonlinear distortions near the tumor. Correlation-based alternatives enhance generalization to diverse deformation patterns by explicitly modeling geometric correspondences, yet they frequently yield unreliable matches in and around tumor regions, disrupting the deformation field. In this paper, we propose Cross-correlation Rectification-based Registration Network (CRRNet), the first framework that introduces an active rectification mechanism specifically for robustness and structurally coherent pre- to post-operative brain tumor image registration. Specifically, CRRNet achieves this through two complementary modules: 1) a Cross-correlation Analysis-Based Inconsistency module that identifies invalid correspondences via bidirectional loop-closure evaluation on cross-correlations, and 2) a Dual-level Correspondence Rectification module that adaptively integrates contextually reliable correlations from local and long-range perspectives to restore structurally coherent matches. This synergistic design retains the strengths of cross-correlation while effectively mitigating correspondence mismatches. Extensive experiments on multiple tumor benchmarks demonstrate the superiority of CRRNet. Specifically, on the BraTS-Reg dataset, it reduces the mean registration errors by 8.18% in near-tumor regions and 3.95% in far-from-tumor regions, surpassing state-of-the-art methods.

One-shot segmentation of brain tissues is typically a dual-model iterative learning: a registration model (reg-model) warps a carefully-labeled atlas onto unlabeled images to initialize their pseudo masks for training a segmentation model (seg-model); the seg-model revises the pseudo masks to enhance the reg-model for a better warping in the next iteration. However, there is a key weakness in such dual-model iteration that the spatial misalignment inevitably caused by the reg-model could misguide the seg-model, which makes it converge on an inferior segmentation performance eventually. In this paper, we propose a novel image-aligned style transformation to reinforce the dual-model iterative learning for robust one-shot segmentation of brain tissues. Specifically, we first utilize the reg-model to warp the atlas onto an unlabeled image, and then employ the Fourier-based amplitude exchange with perturbation to transplant the style of the unlabeled image into the aligned atlas. This allows the subsequent seg-model to learn on the aligned and style-transferred copies of the atlas instead of unlabeled images, which naturally guarantees the correct spatial correspondence of an image-mask training pair, without sacrificing the diversity of intensity patterns carried by the unlabeled images. Furthermore, we introduce a feature-aware content consistency in addition to the image-level similarity to constrain the reg-model for a promising initialization, which avoids the collapse of image-aligned style transformation in the first iteration. Experimental results on two public datasets demonstrate 1) a competitive segmentation performance of our method compared to the fully-supervised method, and 2) a superior performance over other state-of-the-art with an increase of average Dice by up to 4.67%. The source code is available at: https://github.com/JinxLv/One-shot-segmentation-via-IST.

The task of answering questions about images has garnered attention as a practical service for assisting populations with visual impairments as well as a visual Turing test for the artificial intelligence community. Our first aim is to identify the common vision skills needed for both scenarios. To do so, we analyze the need for four vision skills--object recognition, text recognition, color recognition, and counting--on over 27,000 visual questions from two datasets representing both scenarios. We next quantify the difficulty of these skills for both humans and computers on both datasets. Finally, we propose a novel task of predicting what vision skills are needed to answer a question about an image. Our results reveal (mis)matches between aims of real users of such services and the focus of the AI community. We conclude with a discussion about future directions for addressing the visual question answering task.

We collect public opinions on museum artworks using online crowdsourcing techniques. We ask two research questions. First, do crowd opinions on artworks differ from expert interpretations? Second, how can museum manage large amount of crowd opinions, such that users can efficiently retrieve useful information? We address these questions through opinion modeling via semantic embedding and dimension reduction.