Search Results for author:
Found 10 papers, 7 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.
SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant
Recent advancements in the vision-language model have shown notable generalization in vision-language tasks after visual instruction tuning.
Ranked #36 on
Visual Question Answering
on MM-Vet
Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging
In this work, we tackle this challenge by marrying prompt tuning with FL to snapshot compressive imaging for the first time and propose an federated hardware-prompt learning (FedHP) method.
Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
Second, existing pruning methods generally operate upon a pre-trained network for the sparse structure determination, hard to get rid of dense model training in the traditional SR paradigm.
S^2-Transformer for Mask-Aware Hyperspectral Image Reconstruction
By observing this physical encoding procedure, two major challenges stand in the way of a high-fidelity reconstruction.
Modeling Mask Uncertainty in Hyperspectral Image Reconstruction
Recently, hyperspectral imaging (HSI) has attracted increasing research attention, especially for the ones based on a coded aperture snapshot spectral imaging (CASSI) system.
A NEW BACKBONE FOR HYPERSPECTRAL IMAGE RECONSTRUCTION
As the inverse process of snapshot compressive imaging, the hyperspectral image (HSI) reconstruction takes the 2D measurement as input and posteriorly retrieves the captured 3D spatial-spectral signal.
A Simple and Efficient Reconstruction Backbone for Snapshot Compressive Imaging
The emerging technology of snapshot compressive imaging (SCI) enables capturing high dimensional (HD) data in an efficient way.
Simultaneous Classification and Out-of-Distribution Detection Using Deep Neural Networks
Deep neural networks have achieved great success in classification tasks during the last years.
Outlier Exposure with Confidence Control for Out-of-Distribution Detection
Deep neural networks have achieved great success in classification tasks during the last years.
Ranked #1 on
Out-of-Distribution Detection
on CIFAR-100 vs SVHN
(using extra training data)
