Search Results for author:
Found 6 papers, 3 papers with code
Do Vision and Language Encoders Represent the World Similarly?
In the absence of statistical similarity in aligned encoders like CLIP, we show that a possible matching of unaligned encoders exists without any training.
Learning Saliency From Fixations
We present a novel approach for saliency prediction in images, leveraging parallel decoding in transformers to learn saliency solely from fixation maps.
Do VSR Models Generalize Beyond LRS3?
The Lip Reading Sentences-3 (LRS3) benchmark has primarily been the focus of intense research in visual speech recognition (VSR) during the last few years.
Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping
Visual Speech Recognition (VSR) differs from the common perception tasks as it requires deeper reasoning over the video sequence, even by human experts.
One-Step Distributional Reinforcement Learning
Reinforcement learning (RL) allows an agent interacting sequentially with an environment to maximize its long-term expected return.
Distributional Reinforcement Learning
reinforcement-learning
+1
Rethinking 360deg Image Visual Attention Modelling With Unsupervised Learning.
This performance is achieved using an encoder that is trained in a completely unsupervised way and a relatively lightweight supervised decoder (3. 8 X fewer parameters in the case of the ResNet50 encoder).
