Search Results for author:
Found 10 papers, 3 papers with code
CascadedGaze: Efficiency in Global Context Extraction for Image Restoration
Image restoration tasks traditionally rely on convolutional neural networks.
Ranked #1 on
Image Denoising
on SIDD
GVFs in the Real World: Making Predictions Online for Water Treatment
In this paper we investigate the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant.
Movement-induced Priors for Deep Stereo
We propose a method for fusing stereo disparity estimation with movement-induced prior information.
Deep Latent Space Learning for Cross-modal Mapping of Audio and Visual Signals
We quantitatively and qualitatively evaluate the proposed approach on VoxCeleb, a benchmarks audio-visual dataset on a multitude of tasks including cross-modal verification, cross-modal matching, and cross-modal retrieval.
Do Cross Modal Systems Leverage Semantic Relationships?
Our proposed measure evaluates the semantic similarity between the image and text representations in the latent embedding space.
Learning Inward Scaled Hypersphere Embedding: Exploring Projections in Higher Dimensions
Majority of the current dimensionality reduction or retrieval techniques rely on embedding the learned feature representations onto a computable metric space.
Image and Encoded Text Fusion for Multi-Modal Classification
To learn feature representations of resulting images, standard Convolutional Neural Networks (CNNs) are employed for the classification task.
Seeing Colors: Learning Semantic Text Encoding for Classification
The question we answer with this work is: can we convert a text document into an image to exploit best image classification models to classify documents?
Git Loss for Deep Face Recognition
Conventionally, CNNs have been trained with softmax as supervision signal to penalize the classification loss.
Ranked #8 on
Face Verification
on YouTube Faces DB
Revisiting Cross Modal Retrieval
We show that text encodings can capture semantic relationships between multiple modalities.
