CVPR 2025 Papers

Current learning-based Structure-from-Motion (SfM) methods struggle with videos of dynamic scenes from wide-angle cameras. We present AnyMap, a differentiable SfM framework that jointly addresses image distortion and motion estimation. By learning a general implicit camera model without predefined parameters, AnyMap effectively handles lens distortion, estimating multi-view consistent 3D geometry, camera poses, and (un)projection functions. To resolve the ambiguity where motion estimation can compensate for undistortion errors and vice versa, we introduce a low-dimensional motion representation consisting of a set of learnable basis trajectories, interpolated to produce regularized motion estimates. Experimental results show that our method produces accurate camera poses, excels in camera calibration and image rectification, and enables high-quality novel view synthesis. Our low-dimensional motion representation effectively disentangles undistortion with motion estimation, outperforming existing methods.

The ability to adapt to varying observation lengths is crucial for human trajectory prediction tasks, particularly in scenarios with limited observation lengths or missing data. Existing approaches mainly focus on introducing novel architectures or additional structural components, which substantially increase model complexity and present challenges for integration into existing models. We argue that current network architectures are sufficiently sophisticated to handle the Observation Length Shift problem, with the key challenge lying in improving feature representation for trajectories with limited lengths. To tackle this issue, we introduce a general and effective contrastive learning approach, called Contrastive Learning for Length Shift (CLLS). By incorporating contrastive learning during the training phase, our method encourages the model to extract length-invariant features, thus mitigating the impact of observation length variations. Furthermore, to better accommodate length adaptation tasks, we introduce a lightweight RNN network that, combined with CLLS, achieves state-of-the-art performance in both general prediction and observation length shift tasks. Experimental results demonstrate that our approach outperforms existing methods across multiple widely-used trajectory prediction datasets.

Recently, model merging methods have demonstrated powerful strengths in combining abilities on various tasks from multiple Large Language Models (LLMs). While previous model merging methods mainly focus on merging homogeneous models with identical architecture, they meet challenges when dealing with Multimodal Large Language Models (MLLMs) with inherent heterogeneous property, including differences in model architecture and the asymmetry in the parameter space. In this work, we propose AdaMMS, a novel model merging method tailored for heterogeneous MLLMs. Our method tackles the challenges in three steps: mapping, merging and searching. Specifically, we first design mapping function between models to apply model merging on MLLMs with different architecture. Then we apply linear interpolation on model weights to actively adapt the asymmetry in the heterogeneous MLLMs. Finally in the hyper-parameter searching step, we propose an unsupervised hyper-parameter selection method for model merging. As the first model merging method capable of merging heterogeneous MLLMs without labeled data, extensive experiments on various model combinations demonstrated that AdaMMS outperforms previous model merging methods on various vision-language benchmarks.

Anomaly detection and localization remain pivotal challenges in computer vision, with applications ranging from industrial inspection to medical diagnostics. While current supervised methods offer high precision, they are often impractical due to the scarcity of annotated data and the infrequent occurrence of anomalies. Recent advancements in unsupervised approaches, particularly reconstruction-based methods, have addressed these issues by training models exclusively on normal data, enabling them to identify anomalies during inference. However, these methods frequently rely on auxiliary networks or specialized adaptations, which can limit their robustness and practicality. This work introduces the Latent Anomaly Schrodinger Bridge (LASB), a unified unsupervised anomaly detection model that operates entirely in the latent space without requiring additional networks or custom modifications. LASB transforms anomaly images into normal images by preserving structural integrity across varying anomaly classes, lighting, and pose conditions, making it highly robust and versatile. Unlike previous methods, LASB does not focus solely on reconstructing anomaly features, but emphasizes anomaly transformation, achieving smooth anomaly-to-normal image conversions. Our method achieves state-of-the-art performance on both the MVTec-AD and VisA datasets, excelling in detection and localization tasks.

Currently, deep learning-based methods for remote sensing pansharpening have advanced rapidly. However, many existing methods struggle to fully leverage feature heterogeneity and redundancy, thereby limiting their effectiveness. To address these challenges across two key dimensions, we introduce a general adaptive dual-level weighting mechanism (ADWM), designed to enhance a wide range of existing deep-learning methods. First, Intra-Feature Weighting (IFW) evaluates correlations among channels within each feature and selectively weighs to reduce redundancy and enhance unique information. Second, Cross-Feature Weighting (CFW) adjusts contributions across layers based on inter-layer correlations, refining the final output by preserving key distinctions across feature depths. This dual-level weighting is efficiently implemented through our proposed Correlation-Aware Covariance Weighting (CACW), which generates weights by utilizing the correlations captured within the covariance matrix. Extensive experiments demonstrate the superior performance of ADWM compared to recent state-of-the-art (SOTA) methods. Furthermore, we validate the effectiveness of our approach through generality experiments, ablation studies, comparison experiments, and detailed visual analysis.