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Found 11 papers, 4 papers with code
Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning
The current approach, which we call "direct optimization", requires deep changes within each framework to improve the training performance for each hardware backend (CPUs, GPUs, FPGAs, ASICs) and requires $\mathcal{O}(fp)$ effort; where $f$ is the number of frameworks and $p$ is the number of platforms.
Using Scene Graph Context to Improve Image Generation
Generating realistic images from scene graphs asks neural networks to be able to reason about object relationships and compositionality.
Image Generation from Scene Graphs
Open-Ended Question Answering
+1
Heuristics for Image Generation from Scene Graphs
Existing scene graph to image models have two stages: (1) a scene composition stage, and an (2) image generation stage.
Prediction of GNSS Phase Scintillations: A Machine Learning Approach
We propose a novel architecture and loss function to predict 1 hour in advance the magnitude of phase scintillations within a time window of plus-minus 5 minutes with state-of-the-art performance.
Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder
High energy particles originating from solar activity travel along the the Earth's magnetic field and interact with the atmosphere around the higher latitudes.
Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks
This necessitates increased memory footprint and computational requirements for training.
ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning
With only 4M VLP data, ManagerTower achieves superior performances on various downstream VL tasks, especially 79. 15% accuracy on VQAv2 Test-Std, 86. 56% IR@1 and 95. 64% TR@1 on Flickr30K.
Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks
We investigate 9 foundational image-text models on a diverse set of video tasks that include video action recognition (video AR), video retrieval (video RT), video question answering (video QA), video multiple choice (video MC) and video captioning (video CP).
SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples
Our approach utilizes Stable Diffusion with cross attention control to produce sets of counterfactual image-text pairs that are highly similar in their depiction of a subject (e. g., a given occupation) while differing only in their depiction of intersectional social attributes (e. g., race & gender).
Uncovering Bias in Large Vision-Language Models with Counterfactuals
We comprehensively evaluate the text produced by different LVLMs under this counterfactual generation setting and find that social attributes such as race, gender, and physical characteristics depicted in input images can significantly influence toxicity and the generation of competency-associated words.
LVLM-Intrepret: An Interpretability Tool for Large Vision-Language Models
In this work, we present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models.
