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Found 16 papers, 5 papers with code
Llama-Nemotron: Efficient Reasoning Models
We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an open license for enterprise use.
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression
Despite recent efforts in understanding the compression impact on large language models (LLMs) in terms of their downstream task performance and trustworthiness on relatively simpler uni-modal benchmarks (for example, question answering, common sense reasoning), their detailed study on multi-modal Large Vision-Language Models (LVLMs) is yet to be unveiled.
ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution
The dataset is curated from both CMIP6 climate model simulations and real-world observation-assimilated reanalysis datasets (ERA5, JRA-3Q, and MERRA-2), and is designed to enhance model accuracy in detecting climate change signals.
Why do LLaVA Vision-Language Models Reply to Images in English?
We uncover a surprising multilingual bias occurring in a popular class of multimodal vision-language models (VLMs).
Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals
To address this challenging problem, we conduct a large-scale study of text generated by different LVLMs under counterfactual changes to input images.
LVLM-Interpret: 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.
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.
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).
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).
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.
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.
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.
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.
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.
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
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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.
