11 papers with code • 3 benchmarks • 1 datasets

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Most implemented papers

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

charlesq34/pointnet2 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

facebookresearch/llama 18 Jul 2023

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

hq-deng/RD4AD CVPR 2022

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 CVPR 2021

How should representations from complementary sensors be integrated for autonomous driving?

MTet: Multi-domain Translation for English and Vietnamese

vietai/mTet 11 Oct 2022

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 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

ecom-research/ComposeAE 19 Jun 2020

In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query.

An efficient encoder-decoder architecture with top-down attention for speech separation

JusperLee/TDANet 30 Sep 2022

In addition, a large-size version of TDANet obtained SOTA results on three datasets, with MACs still only 10\% of Sepformer and the CPU inference time only 24\% of Sepformer.

Gradient Gating for Deep Multi-Rate Learning on Graphs

tk-rusch/gradientgating 2 Oct 2022

We present Gradient Gating (G$^2$), a novel framework for improving the performance of Graph Neural Networks (GNNs).

Dual Cross-Attention for Medical Image Segmentation

gorkemcanates/dual-cross-attention 30 Mar 2023

DCA addresses the semantic gap between encoder and decoder features by sequentially capturing channel and spatial dependencies across multi-scale encoder features.