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Found 7 papers, 0 papers with code
Improving Image De-raining Using Reference-Guided Transformers
Image de-raining is a critical task in computer vision to improve visibility and enhance the robustness of outdoor vision systems.
MoNDE: Mixture of Near-Data Experts for Large-Scale Sparse Models
Mixture-of-Experts (MoE) large language models (LLM) have memory requirements that often exceed the GPU memory capacity, requiring costly parameter movement from secondary memories to the GPU for expert computation.
Multi-task Learning for Real-time Autonomous Driving Leveraging Task-adaptive Attention Generator
Real-time processing is crucial in autonomous driving systems due to the imperative of instantaneous decision-making and rapid response.
Memory-guided Image De-raining Using Time-Lapse Data
To address this problem, we propose a novel network architecture based on a memory network that explicitly helps to capture long-term rain streak information in the time-lapse data.
DIML/CVL RGB-D Dataset: 2M RGB-D Images of Natural Indoor and Outdoor Scenes
This manual is intended to provide a detailed description of the DIML/CVL RGB-D dataset.
Wide and Narrow: Video Prediction from Context and Motion
Video prediction, forecasting the future frames from a sequence of input frames, is a challenging task since the view changes are influenced by various factors, such as the global context surrounding the scene and local motion dynamics.
A Large RGB-D Dataset for Semi-supervised Monocular Depth Estimation
In this paper, we present a simple yet effective approach for monocular depth estimation using stereo image pairs.
