In this paper, we address the problem of video-based rain streak removal by developing an event-aware multi-patch progressive neural network. Rain streaks in video exhibit correlations in both temporal and spatial dimensions. Existing methods have difficulties in modeling the characteristics. Based on the observation, we propose to develop a module encoding events from neuromorphic cameras to facilitate deraining. Events are captured asynchronously at pixel-level only when intensity changes by a margin exceeding a certain threshold. Due to this property, events contain considerable information about moving objects including rain streaks passing though the camera across adjacent frames. Thus we suggest that utilizing it properly facilitates deraining performance non-trivially. In addition, we develop a multi-patch progressive neural network. The multi-patch manner enables various receptive fields by partitioning patches and the progressive learning in different patch levels makes the model emphasize each patch level to a different extent. Extensive experiments show that our method guided by events outperforms the state-of-the-art methods by a large margin in synthetic and real-world datasets.

Multi-modal image fusion (MMIF) aims to integrate complementary information from heterogeneous sensor modalities. However, substantial cross-modality discrepancies hinder joint scene representation and lead to semantic degradation in the fused output. To address this limitation, we propose C²MFuse, a novel framework designed to preserve content while ensuring cross-modality consistency. To the best of our knowledge, this is the first MMIF approach to explicitly disentangle style and content representations across modalities for image fusion. C²MFuse introduces a content-preserving style normalization mechanism that suppresses modality-specific variations while maintaining the underlying scene structure. The normalized features are then progressively aggregated to enhance fine-grained details and improve content completeness. In light of the lack of ground truth and the inherent ambiguity of the fused distribution, we further align the fused representation with a well-defined source modality, thereby enhancing semantic consistency and reducing distributional uncertainty. Additionally, we introduce an adaptive consistency loss with learnable transformation, which provides dynamic, modality-aware supervision by enforcing global consistency across heterogeneous inputs. Extensive experiments on five datasets across three representative MMIF tasks demonstrate that C²MFuse achieves efficient and high-quality fusion, surpasses existing methods, and generalizes effectively to downstream visual applications.

Recent advancements in deep learning, particularly through Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have led to significant progress in nighttime image deraining. However, current architectures still struggle to strike an optimal balance between computational efficiency and restoration performance. Moreover, existing methods often fail to fully exploit the intrinsic characteristics of low-light conditions and inadequately model the interaction between rain and illumination. To overcome these challenges, we propose NDMamba, a dual-prior-guided state-space model that addresses nighttime deraining by incorporating degradation cues related to both lighting and rain distribution. Inspired by the Retinex theory, which suggests that rain streak distribution is influenced by the reflectance component of a scene, we propose a Prior Extraction Module (PEM) to jointly model lighting conditions and rain degradation. Furthermore, we design a Prior-Guided Mamba Block (PGMB), which comprises a Lighting-Adaptive Vision State-Space Module (LVSSM) that incorporates illumination priors, and a Rain Distribution Guidance Module (RDGM) to enhance local features in a more refined manner. Extensive experiments demonstrate that NDMamba outperforms state-of-the-art methods on both synthetic and real-world benchmark datasets. Our code is publicly available at https://github.com/tandaily/NDMamba.

Many applications of large language models need a decision rather than free text. Examples include assigning a category, giving a score, answering yes or no, or choosing the next action. Prior work has used language models for such decisions in several ways, including zero-shot classification, reward models and verifiers, LLM-as-a-Judge, safety classifiers, and model cascades. In most of these approaches, the model generates text, and the decision must then be extracted from it. Recently, a class of models has emerged that answers typed questions directly, without generating text. Given an input and a set of questions with predefined answer types, such a model returns, for each question, an answer in the declared format together with probabilities over the possible answers, and for some question types a confidence score. We call these models typed decision models. The commercial model Jev made this design widely known, and it was soon followed by many open models and by similar services from other providers. Research on typed decision models is growing quickly but is scattered across many communities. This survey reviews the area. We trace its roots in earlier work and give a formal description of typed decision models whose outputs depend on the question type. We analyze how these models are combined with stronger fallback models, and show that such a combination improves accuracy only if the fallback corrects more errors than it introduces on the inputs passed to it. We then review model designs, training methods, sources of efficiency, applications, benchmarks, and known failure modes. In the reported settings, typed decision models are often much faster and cheaper than generative models, and they are often competitive on tasks where the needed evidence is stated in the input. They tend to perform worse on tasks that require multi-step reasoning and against supervised models trained on in-domain data. Their probabilities also depend on how a question is asked, and they can be misled by option names and by untrusted input. We conclude with open problems for future research.