16 papers with code • 11 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in
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Libraries
Use these libraries to find models and implementations
MVTecAD
BIG-bench
YCB-Video
BBH
Echonet-Dynamic
Subtasks
Most implemented papers
Deep Residual Learning for Image Recognition
tensorflow/models
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CVPR 2016
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
yanx27/Pointnet_Pointnet2_pytorch
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NeurIPS 2017
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Anomaly Detection via Reverse Distillation from One-Class Embedding
Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD.
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
autonomousvision/transfuser
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CVPR 2021
How should representations from complementary sensors be integrated for autonomous driving?
Visual Spatial Reasoning
cambridgeltl/visual-spatial-reasoning
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Spatial relations are a basic part of human cognition.
MTet: Multi-domain Translation for English and Vietnamese
vietai/mTet
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We introduce MTet, the largest publicly available parallel corpus for English-Vietnamese translation.
A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
ray075hl/Bi-Model-Intent-And-Slot
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NAACL 2018
The most effective algorithms are based on the structures of sequence to sequence models (or "encoder-decoder" models), and generate the intents and semantic tags either using separate models or a joint model.
Compositional Learning of Image-Text Query for Image Retrieval
In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query.
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
bionlu-coling2024/biomed-ner-intent_detection
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In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.
