Based on the modeling method, we present FocusFlow, a framework consisting of 1) a mix loss function combined with a classic photometric loss function and our proposed Conditional Point Control Loss (CPCL) function for diverse point-wise supervision; 2) a conditioned controlling model which substitutes the conventional feature encoder by our proposed Condition Control Encoder (CCE).
They typically align visual features with semantic features obtained from word embedding by the supervision of seen classes' annotations.
Concept Factorization (CF), as a novel paradigm of representation learning, has demonstrated superior performance in multi-view clustering tasks.
In this paper, we propose a Panoramic Computational Imaging Engine (PCIE) to address minimalist and high-quality panoramic imaging.
The performance of video frame interpolation is inherently correlated with the ability to handle motion in the input scene.
LiDAR and camera are two essential sensors for 3D object detection in autonomous driving.
Limited by hardware cost and system size, camera's Field-of-View (FoV) is not always satisfactory.
Ranked #1 on Seeing Beyond the Visible on KITTI360-EX
Further, we propose Computational Imaging Assisted Domain Adaptation (CIADA) to leverage prior knowledge of CI for robust performance in SSOA.
With our proposed QCM, the downstream fusion module receives visual features that are more discriminative and focused on the desired object described in the expression, leading to more accurate predictions.
In this paper, we propose an Annular Computational Imaging (ACI) framework to break the optical limit of light-weight PAL design.
To provide an effective analysis method for this type of dynamic graph data, we propose a snapshot generation algorithm involving Human-In-Loop to help users divide the dynamic graphs into multi-granularity and hierarchical snapshots for further analysis.
Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times.
Ranked #3 on Deblurring on GoPro (using extra training data)
Recently, deep learning methods have dominated image super-resolution and achieved remarkable performance on visible images; however, IR images have received less attention.