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- Domain Adaptive Faster R-CNN for Object Detection in the Wild1,634
This work builds on the recent state-of-the-art Faster R-CNN model, and design two domain adaptation components, on image level and instance level, to reduce the domain discrepancy, based on $$-divergence theory.
- Semantic Foggy Scene Understanding with Synthetic Data1,504
A complete pipeline to add synthetic fog to real, clear-weather images using incomplete depth information is developed and supervised learning with the authors' synthetic data significantly improves the performance of state-of-the-art CNN for SFSU on Foggy Driving.
- 1,069
- Continual Test-Time Domain Adaptation861
The proposed CoTTA proposes to stochastically restore a small part of the neurons to the source pre-trained weights during each iteration to help preserve source knowledge in the longterm and demonstrates the effectiveness of the approach on four classification tasks and a segmentation task for continual test-time adaptation.
- 778
- DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation650
A novel UDA method based on a Transformer encoder and a multi-level context-aware feature fusion decoder is proposed, DAFormer, which improves the state of the art in UDA by 10.8 mIoU for GTA→Cityscapes and 5.4 mIOU for Syruhia→City Scapes and enables learning even difficult classes such as train, bus, and truck well.
- MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation412
A Masked Image Consistency module to enhance UDA by learning spatial context relations of the target domain as additional clues for robust visual recognition and significantly improves the state-of-the-art performance across the different recognition tasks for synthetic- to-real, day-to-nighttime, and clear-toadverse-weather UDA.
- Fast Optical Flow Using Dense Inverse Search393
The Dense Inverse Search-based method (DIS) is the efficient search of correspondences inspired by the inverse compositional image alignment proposed by Baker and Matthews (2001, 2004), making DIS ideal for real-time applications.
- 378
- End-to-End Urban Driving by Imitating a Reinforcement Learning Coach358
A reinforcement learning expert is trained that maps bird’s-eye view images to continuous low-level actions and provides in-formative supervision signals for imitation learning agents to learn from that achieves expert-level performance.
- Decoupling Zero-Shot Semantic Segmentation319
A simple and effective zero-shot semantic segmentation model, called ZegFormer, is proposed, which outperforms the previous methods on ZS3 standard benchmarks by large margins, e.g., 22 points on the PAS-CAL VOC and 3 points onThe COCO-Stuff in terms of mIoU for unseen classes.
- 316
- Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime305
A novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time, to alleviate the cost of human annotation for nighttime images by transferring knowledge from standard daytime conditions.
- MTR++: Multi-Agent Motion Prediction With Symmetric Scene Modeling and Guided Intention Querying297
The MTR framework achieves state-of-the-art performance on the highly-competitive motion prediction benchmarks, while the MTR++ framework surpasses its precursor, exhibiting enhanced performance and efficiency in predicting accurate multimodal future trajectories for multiple agents.
- Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding297
A novel method is proposed, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data.
- Deep Gradient Learning for Efficient Camouflaged Object Detection283
The proposed DGNet performs well in the polyp segmentation, defect detection, and transparent object segmentation tasks, and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82% parameters.
- 283
- Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather251
This work addresses the challenging task of LiDAR-based 3D object detection in foggy weather by simulating physically accurate fog into clear-weather scenes, so that the abundant existing real datasets captured in clear weather can be repurposed for this task.
- Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation212
A curriculum framework to gradually adapt semantic segmentation models from day to night through progressively darker times of day, exploiting cross-time-of-day correspondences between daytime images from a reference map and dark images to guide the label inference in the dark domains and a novel uncertainty-aware evaluation framework.
- LiDAR Snowfall Simulation for Robust 3D Object Detection196
This paper proposes a physically based method to simulate the effect of snowfall on real clear-weather LiDAR point clouds, and conducts an extensive evaluation using several state-of-the-art 3D object detection methods to show that this simulation consistently yields significant performance gains on the real snowy STF dataset.
- End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners186
360-degree surround-view cameras help avoid failures made with a single front-view camera, in particular for city driving and intersection scenarios; and route planners help the driving task significantly, especially for steering angle prediction.
- Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation174
This work proposes to explicitly learn the task feature correlation to strengthen the target semantic predictions with the help of target depth estimation, and uses the depth prediction discrepancy from source and target depth decoders to approximate the pixel-wise adaptation difficulty.
- Jointly Optimized Regressors for Image Super‐resolution166
A collection of regressors are jointly learned, which collectively yield the smallest super‐resolving error for all training data, and this method is conceptually simple and computationally efficient, yet very effective.
- Don’t Forget The Past: Recurrent Depth Estimation from Monocular Video162
This work is the first to successfully exploit recurrent networks for real-time self-supervised monocular depth estimation and completion and outperforms previous depth estimation methods of the three popular groups.
- Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding158
A novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both labeled synthetic Foggy data and unlabeled real foggy data.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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