8k
63 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model for generating novel image captions.
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks and achieving better optimization performance.
CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model
CLUEbenchmark/CLUECorpus2020
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In this paper, we introduce the Chinese corpus from CLUE organization, CLUECorpus2020, a large-scale corpus that can be used directly for self-supervised learning such as pre-training of a language model, or language generation.
Global-and-Local Collaborative Learning for Co-Salient Object Detection
HarveyYeung/GLNet-TCYB2022-MindSpore.git
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In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives.
The CropAndWeed Dataset: A Multi-Modal Learning Approach for Efficient Crop and Weed Manipulation
Precision Agriculture and especially the application of automated weed intervention represents an increasingly essential research area, as sustainability and efficiency considerations are becoming more and more relevant.
VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation
VPP leverages structured voxel representation in the proposed Voxel Semantic Generator and the sparsity of unstructured point representation in the Point Upsampler, enabling efficient generation of multi-category objects.
LongQLoRA: Efficient and Effective Method to Extend Context Length of Large Language Models
We present LongQLoRA, an efficient and effective method to extend context length of large language models with less training resources.
Learning to Evaluate Image Captioning
richardaecn/cvpr18-caption-eval
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CVPR 2018
To address these two challenges, we propose a novel learning based discriminative evaluation metric that is directly trained to distinguish between human and machine-generated captions.
Fast and accurate object detection in high resolution 4K and 8K video using GPUs
previtus/AttentionPipeline
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Machine learning has celebrated a lot of achievements on computer vision tasks such as object detection, but the traditionally used models work with relatively low resolution images.
Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications
Joint SR and ITM is an intricate task, where high frequency details must be restored for SR, jointly with the local contrast, for ITM.
