Search Results for author:
Found 13 papers, 6 papers with code
Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
We introduce Nemotron-H, a family of 8B and 56B/47B hybrid Mamba-Transformer models designed to reduce inference cost for a given accuracy level.
AIDE: Agentically Improve Visual Language Model with Domain Experts
The enhancement of Visual Language Models (VLMs) has traditionally relied on knowledge distillation from larger, more capable models.
Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents
Fully comprehending such documents requires an understanding of their structure -- including formatting, formulas, tables, and the reading order of multiple blocks and columns across multiple pages -- as well as semantic information for detecting elements like footnotes and image captions.
Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models
Recently, promising progress has been made by open-source vision-language models (VLMs) in bringing their capabilities closer to those of proprietary frontier models.
OMCAT: Omni Context Aware Transformer
OCTAV (Omni Context and Temporal Audio Video) is a novel dataset designed to capture event transitions across audio and video.
Audio-visual Question Answering
Audio-Visual Question Answering (AVQA)
+5
Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders
The ability to accurately interpret complex visual information is a crucial topic of multimodal large language models (MLLMs).
Personalized Federated Learning with First Order Model Optimization
While federated learning traditionally aims to train a single global model across decentralized local datasets, one model may not always be ideal for all participating clients.
World-Consistent Video-to-Video Synthesis
This is because they lack knowledge of the 3D world being rendered and generate each frame only based on the past few frames.
Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter
Specifically, we directly treat the whole encoded feature map of the input texture as transposed convolution filters and the features' self-similarity map, which captures the auto-correlation information, as input to the transposed convolution.
Hierarchical Multi-Scale Attention for Semantic Segmentation
Multi-scale inference is commonly used to improve the results of semantic segmentation.
Ranked #6 on
Semantic Segmentation
on Cityscapes val
(using extra training data)
Panoptic-based Image Synthesis
Conditional image synthesis for generating photorealistic images serves various applications for content editing to content generation.
Improving Semantic Segmentation via Video Propagation and Label Relaxation
In this paper, we present a video prediction-based methodology to scale up training sets by synthesizing new training samples in order to improve the accuracy of semantic segmentation networks.
Ranked #2 on
Semantic Segmentation
on KITTI Semantic Segmentation
(using extra training data)
Partial Convolution based Padding
In this paper, we present a simple yet effective padding scheme that can be used as a drop-in module for existing convolutional neural networks.
