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Evolution of Machine Translation Machine Translation (MT) is a core NLP discipline focused on transforming text from a source language to a target language automatically. As global communication accelerates, the demand for robust MT systems has grown significantly. The field has evolved through three primary paradigms: Rule-Based MT (1950s–1980 ...

Tencent YouTu Lab had 14 papers accepted at ECCV 2024, covering diverse areas in computer vision including anomaly detection, image editing, and multimodal learning. Below are highlights of selected papers: Multimodal Label Relevance Ranking via Reinforcement Learning Proposes LR²PPO, a reinforcement learning approach for ranking label relevanc ...

No labeling requirements Training uses only normal samples Supports CPU-based training Fast inference speeds Best suited for projects with clearly visible defects. Note: Image dimensions should be multiples of 32 for optimal performance. Performance Metrics With recall at 92.1%, 7.9% of normal samples are falsely flagged as anomalies. Precisi ...

import torch import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.transforms as T import os import pathlib import warnings import random import matplotlib.pyplot as plt warnings.filterwarnings("ignore") # ========================================== # 1. Dataset Preparation # ==================== ...

Added Mask-RCNN based instance segmentation plugin with sample implementation. #include "VP.h" #include "../nodes/vp_file_src_node.h" #include "../nodes/infers/vp_mask_rcnn_detector_node.h" #include "../nodes/track/vp_sort_track_node.h" #include "../nodes/osd/vp_osd_node_v3.h" #include "../ ...

Assume your data is structured as folows: data/ ├── dog/ │ ├── dog001.jpg │ └── dog002.jpg └── cat/ ├── cat001.jpg └── cat002.jpg First, import necessary modules and prepare the dataset: import torch import torchvision from torchvision import transforms import torch.nn as nn import torch.optim as optim base_path = './data' prepro ...

Processing variable-length sequences through Recurrent Neural Networks (RNNs) requires special handling. PyTorch provides two key functions for this: pack_padded_sequence and pad_packed_sequence. The core concept involves transforming padded batch tensors into a packed format for efficient RNN computation and then restoring the batch structure. ...

1. BERT Architecture BERT stands for Bidirectional Encoder Representations from Transformers, a language model developed by Google in late 2018. It leverages the Transformer encoder architecture combined with bidirectional training to capture contextual information from both directions. The key innovation lies in the pretraining methodology, wh ...

Basic Creation Methods torch.tensor: Create a tensor from existing data. torch.Tensor: Create a tensor with a given shape, or from data if provided. torch.IntTensor, torch.FloatTensor, torch.DoubleTensor: Create a tensor of a specific data type. 1.1 Create Tensor from Existing Data # 1. Create tensor from existing data def test01(): # ...

Implementing Semantic Image Segmentation with FCN using MindSpore Background This article explores the implementation of Fully Convolutional Networks (FCN) for semantic image segmentation using MindSpore, an open-source deep learning framework developed by Huawei. FCN Introduction Fully Convolutional Networks (FCN) represent a significant adv ...