Diffusion models have shown strong promise for image super-resolution (ISR). However, current approaches often underuse pretrained diffusion backbones and lack constraints on the sampling trajectory, which degrades structural consistency and fine details. For that, we introduce the trajectory consistent diffusion model (TCDM) for super-resolution, which jointly optimizes the sampling process through lightweight components and inference-time strategies while keeping the diffusion backbone frozen, yielding high-fidelity, detail-rich reconstructions. First, we propose a dynamic semantic selection (DSS) mechanism that records early intermediates, matches them to upsampled low-resolution features, and reconditions sampling with the best match to reduce the mismatch between conditioning and noise scale. Next, we design a cross-step aggregation guidance (CAG) strategy that aggregates features from the current state with the selected intermediate to enforce trajectory-level consistency in noise prediction. Finally, we present a plug-and-play frequency enhancement adapter (FE-Adapter) that injects different frequency-domain cues into the encoder during training, strengthening high-frequency perception while preserving global structures. Extensive experiments on multiple ISR benchmarks show that TCDM achieves strong structural fidelity and competitive no-reference perceptual quality, offering a favorable fidelity-perception trade-off.

Large-scale multi-modal models have demonstrated remarkable performance across various visual recognition tasks by leveraging extensive paired multi-modal training data. However, in real-world applications, the presence of missing or incomplete modality inputs often leads to significant performance degradation. Recent research has focused on prompt-based strategies to tackle this issue; however, existing methods are hindered by two major limitations: (1) static prompts lack the flexibility to adapt to varying missing-data conditions, and (2) basic prompt-tuning methods struggle to ensure reliable performance when critical modalities are missing.To address these challenges, we propose a novel Synergistic Prompting (SyP) framework for robust visual recognition with missing modalities. The proposed SyP introduces two key innovations: (I) a Dynamic Adapter, which computes adaptive scaling factors to dynamically generate prompts, replacing static parameters for flexible multi-modal adaptation, and (II) a Synergistic Prompting Strategy, which combines static and dynamic prompts to balance information across modalities, ensuring robust reasoning even when key modalities are missing. The proposed SyP achieves significant performance improvements over existing approaches across three widely-used visual recognition datasets, demonstrating robustness under diverse missing rates and conditions. Extensive experiments and ablation studies validate its effectiveness in handling missing modalities, highlighting its superior adaptability and reliability.

High-definition (HD) map construction methods are crucial for providing precise and comprehensive static environmental information, which is essential for autonomous driving systems. While Camera-LiDAR fusion techniques have shown promising results by integrating data from both modalities, existing approaches primarily focus on improving model accuracy, often neglecting the robustness of perception models—a critical aspect for real-world applications. In this paper, we explore strategies to enhance the robustness of multi-modal fusion methods for HD map construction while maintaining high accuracy. We propose three key components: data augmentation, a novel multi-modal fusion module, and a modality dropout training strategy. These components are evaluated on a challenging dataset containing 13 types of multi-sensor corruption. Experimental results demonstrate that our proposed modules significantly enhance the robustness of baseline methods. Furthermore, our approach achieves state-of-the-art performance on the clean validation set of the NuScenes dataset. Our findings provide valuable insights for developing more robust and reliable HD map construction models, advancing their applicability in real-world autonomous driving scenarios. Project website: https://robomap-123.github.io/.

Learning correspondences aims to find correct correspondences (inliers) from the initial correspondence set with an uneven correspondence distribution and a low inlier rate, which can be regarded as graph data. Recent advances usually use graph neural networks (GNNs) to build a single type of graph or simply stack local graphs into the global one to complete the task. But they ignore the complementary relationship between different types of graphs, which can effectively capture potential relationships among sparse correspondences. To address this problem, we propose MGNet to effectively combine multiple complementary graphs. To obtain information integrating implicit and explicit local graphs, we construct local graphs from implicit and explicit aspects and combine them effectively, which is used to build a global graph. Moreover, we propose Graph Soft Degree Attention (GSDA) to make full use of all sparse correspondence information at once in the global graph, which can capture and amplify discriminative features. Extensive experiments demonstrate that MGNet outperforms state-of-the-art methods in different visual tasks. The code is provided in https://github.com/DAILUANYUAN/MGNet-2024AAAI.

Two-view correspondence pruning aims to accurately remove incorrect correspondences (outliers) from initial ones. Graph Neural Networks (GNNs) incorporated by Multilayer Perceptrons (MLPs) are treated as a powerful manner to handle sparse and unevenly distributed data. However, the expression capability of correspondence features obtained by MLPs is limited by their inherent insufficient of context information. In addition, previous works directly utilize the outputs of off-the-shelf GNNs, thus leading to confusion between sparse correspondence attribute features and their global structural information. To alleviate these issues, we propose a two-view correspondence pruning network TrGa. Specifically, we firstly use complete Transformer structures instead of context-agnostic MLPs to capture correspondence features with global context information and stronger expression capability. After that, we introduce the Concatenation Graph Node and Global Structure (CGNS) block to separately capture the interaction patterns among sparse correspondence attribute features and the global structural information among them, which can prevent their confusion. Finally, the proposed Feature Dimension Transformation and Enhancement (FDTE) block is applied for dimension transformation and feature augmentation. Additionally, we propose an efficient variant C-TrGa, in which the similarity matrix of the proposed C-Transformer is computed along the channel dimension. Extensive experiments demonstrate that the proposed TrGa and C-TrGa outperform state-of-the-art methods in different computer vision tasks.

Building high-quality correspondences is critical in the feature-based point cloud registration pipelines. However, existing single-sequence learning frameworks are difficult to accurately and adequately capture contextual information, leaving a large proportion of outliers between two low-overlap scenes. In this paper, we present a progressive guidance network (PG-Net) to gather rich contextual information and exclude outliers. Specifically, we design a novel iterative structure that exploits the inlier probabilities of correspondences to guide the classification of initial correspondences progressively. This structure can mitigate outlier effects with robust contextual information to obtain more accurate model estimation. In addition, to sufficiently capture contextual information, we propose a grouped dense fusion attention feature embedding module to enhance the representation of inliers and significant channel-spatial. Meanwhile, we propose a two-stage neural spectral matching module to compute the inlier probability of each correspondence and estimate a 3D transformation model in a coarse-to-fine manner. Experiments results on indoor and outdoor datasets using distinct 3D local descriptors demonstrate that our PG-Net surpasses state-of-the-art outlier removal methods. Especially compared to the recent outlier removal network PointDSC, our PG-Net improves the registration recall by 4.06% on the indoor dataset with the FPFH descriptor. Source code: https://github.com/changcaiyang/PG-Net.

Establishing superior-quality correspondences in an image pair is pivotal to many subsequent computer vision tasks. Using Euclidean distance between correspondences to find neighbors and extract local information is a common strategy in previous works. However, most such works ignore similar sparse semantics information between two given images and cannot capture local topology among correspondences well. Therefore, to deal with the above problems, Multiple Sparse Semantics Dynamic Graph Network (MS2DG-Net) is proposed, in this paper, to predict probabilities of correspondences as inliers and recover camera poses. MS2 DG-Net dynamically builds sparse semantics graphs based on sparse semantics similarity between two given images, to capture local topology among correspondences, while maintaining permutation-equivariant. Extensive experiments prove that MS2 DG-Net outperforms state-of-the-art methods in outlier removal and camera pose estimation tasks on the public datasets with heavy outliers. Source code:https://github.com/changcaiyang/MS2DG-Net

In this letter, we propose a remote sensing image matching method that is simple yet efficient to deal with different deformations. Inspired by the region growing strategy used in image segmentation, we integrate the motion consistency into the general region growing pipeline from a novel perspective. Specifically, we first obtain a subset with a high ratio inlier as the seed correspondence set. Then, to find more reliable correspondences, we formulate the motion consistency into the correspondence growing criterion, which is general to be suitable to many remote sensing applications. Extensive experimental results on the public available remote sensing data set show that our method achieves the best performance compared with state-of-the-art methods.