Self-supervised depth estimation from monocular cameras in diverse outdoor conditions, such as daytime, rain, and nighttime, is challenging due to the difficulty of learning universal representations and the severe lack of labeled real-world adverse data. Previous methods either rely on synthetic inputs and pseudo-depth labels or directly apply daytime strategies to adverse conditions, resulting in suboptimal results. In this paper, we present the first synthetic-to-real robust depth estimation framework, incorporating motion and structure priors to capture real-world knowledge effectively. In the synthetic adaptation, we transfer motion-structure knowledge inside cost volumes for better robust representation, using a frozen daytime model to train a depth estimator in synthetic adverse conditions. In the innovative real adaptation, which targets to fix synthetic-real gaps, models trained earlier identify the weather-insensitive regions with a designed consistency-reweighting strategy to emphasize valid pseudo-labels. We introduce a new regularization by gathering explicit depth distribution to constrain the model facing real-world data. Experiments show that our method outperforms the state-of-the-art across diverse conditions in multi-frame and single-frame evaluations. We achieve improvements of 7.5% and 4.3% in Ab-sRel and RMSE on average for nuScenes and Robotcar datasets (daytime, nighttime, rain). In zero-shot evaluation of DrivingStereo (rain, fog), our method generalizes better than previous ones. The code is at Syn2Real-Depth.

Video large language models (ViLLMs) excel in general video understanding, e.g., recognizing activities like talking and eating, but struggle with identity-aware comprehension, such as "Wilson is receiving chemotherapy" or "Tom is discussing with Sarah", limiting their applicability in smart healthcare and smart home environments. To address this limitation, we propose a one-shot learning framework PVChat, the first personalized ViLLM that enables subject-aware question answering (QA) from a single video for each subject. Our approach optimizes a Mixture-of-Heads (MoH) enhanced ViLLM on a synthetically augmented video-QA dataset, leveraging a progressive image-to-video learning strategy. Specifically, we introduce an automated augmentation pipeline that synthesizes identity-preserving positive samples and retrieves hard negatives from existing video corpora, generating a diverse training dataset with four QA types: existence, appearance, action, and location inquiries. To enhance subject-specific learning, we propose a ReLU Routing MoH attention mechanism, alongside two novel objectives: (1) Smooth Proximity Regularization for progressive learning through exponential distance scaling and (2) Head Activation Enhancement for balanced attention routing. Finally, we adopt a two-stage training strategy, transitioning from image pre-training to video fine-tuning, enabling a gradual learning process from static attributes to dynamic representations. We evaluate PVChat on diverse datasets covering medical scenarios, TV series, anime, and real-world footage, demonstrating its superiority in personalized feature understanding after learning from a single video, compared to state-of-the-art ViLLMs.

Image Alignment between Synthetic Aperture Radar (SAR) and Electro-Optical (EO) imagery is a task that has comprehensive remote sensing capabilities. Traditional deep learning-based SAR-optical image matching models heavily rely on supervised learning with expensive annotated datasets, leading to reduced accuracy and overfitting when encountering insufficient data during training. To tackle this issue, this paper proposes a student-teacher framework for Domain Adaptation (DA) approach, transferring deep learning models from well-annotated source domains like normal outside optical images to non-annotated SAR-EO target domains. Additionally, in contrast to previous methods which usually use CNN or ordinary Transformer structure to extract features from image pairs, we use self and cross attention mechanisms in Transformer to obtain feature descriptors that are conditioned on both multimodal images. The larger global receptive field and better feature extraction capability provided by this Transformer shows its ability to accommodate large disparities of multimodal data and manage fewer textures such as rural areas with forest or desert in satellite images, where previous backbones usually struggle to produce repeatable and correct interest points.

We present a deep homography mixture motion model for single image rolling shutter correction. Rolling shutter (RS) effects are often caused by row-wise exposure delay in the widely adopted CMOS sensor. Previous methods often require more than one frame for the correction, leading to data quality requirements. Few approaches address the more challenging task of single image RS correction, which often adopt designs like trajectory estimation or long rectangular kernels, to learn the camera motion parameters of an RS image, to restore the global shutter (GS) image. In this work, we adopt a more straightforward method to learn deep homography mixture motion between an RS image and its corresponding GS image, without large solution space or strict restrictions on image features. We show that dividing an image into blocks with a Gaussian weight of block scanlines fits well for the RS setting. Moreover, instead of directly learning the motion mapping, we learn coefficients that assemble several motion bases to produce the correction motion, where these bases are learned from the consecutive frames of natural videos beforehand. Experiments show that our method outperforms existing single RS methods statistically and visually, in both synthesized and real RS images. Our code and dataset are available at https://github.com/DavidYan2001/Deep_HM.