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Found 10 papers, 1 papers with code
Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval
Correspondingly, a single text embedding may be less expressive to capture the video embedding and empower the retrieval.
Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation
The transductive transfer learning (TTL) method that incorporates a CycleGAN-based unpaired domain translation network has been previously proposed in the literature for effective ATR annotation.
Image Translation as Diffusion Visual Programmers
We introduce the novel Diffusion Visual Programmer (DVP), a neuro-symbolic image translation framework.
TokenMotion: Motion-Guided Vision Transformer for Video Camouflaged Object Detection Via Learnable Token Selection
The area of Video Camouflaged Object Detection (VCOD) presents unique challenges in the field of computer vision due to texture similarities between target objects and their surroundings, as well as irregular motion patterns caused by both objects and camera movement.
Deep Transductive Transfer Learning for Automatic Target Recognition
We employ a CycleGAN model to transfer the mid-wave infrared (MWIR) images to visible (VIS) domain images (or visible to MWIR domain).
TransUPR: A Transformer-based Uncertain Point Refiner for LiDAR Point Cloud Semantic Segmentation
Uncertain points are sampled from coarse semantic segmentation results of 2D image segmentation where uncertain points are located close to the object boundaries in the 2D range image representation and 3D spherical projection background points.
Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learning
Motivated by the increasing application of low-resolution LiDAR recently, we target the problem of low-resolution LiDAR-camera calibration in this work.
Constructing Multilayer Perceptrons as Piecewise Low-Order Polynomial Approximators: A Signal Processing Approach
Through the construction, a one-to-one correspondence between the approximation of an MLP and that of a piecewise low-order polynomial is established.
From Two-Class Linear Discriminant Analysis to Interpretable Multilayer Perceptron Design
Besides input layer $l_{in}$ and output layer $l_{out}$, the MLP of interest consists of two intermediate layers, $l_1$ and $l_2$.
PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classification
In PixelHop++, one can control the learning model size of fine-granularity, offering a flexible tradeoff between the model size and the classification performance.
