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JD Tech Talk
JD Tech Talk
Nov 15, 2019 · Artificial Intelligence

Legal Case Similarity Competition at CCL 2019: Dataset, Task Transformation, and Model Solutions

The article reviews the CCL “Chinese Law Research Cup” similarity competition, describing the legal text dataset, converting the triple‑sample task to a binary similarity problem, outlining challenges such as long documents, and summarizing the BERT‑based Siamese, InferSent, and triplet‑loss models that achieved top‑10 results.

BERTCCL 2019Siamese network
0 likes · 8 min read
Legal Case Similarity Competition at CCL 2019: Dataset, Task Transformation, and Model Solutions
Meitu Technology
Meitu Technology
Jul 12, 2018 · Artificial Intelligence

DeepHash: Large-Scale Multimedia Content Analysis and Retrieval for Short Video Platforms

DeepHash is Meitu’s large‑scale short‑video analysis and retrieval system that converts deep‑learned visual features into compact binary hash codes via a MobileNet‑based CNN and triplet‑loss training, enabling fast, robust similarity search across billions of videos with sub‑second latency and minimal storage.

Feature ExtractionLarge Scaledeep learning
0 likes · 15 min read
DeepHash: Large-Scale Multimedia Content Analysis and Retrieval for Short Video Platforms
Tongcheng Travel Technology Center
Tongcheng Travel Technology Center
Sep 8, 2017 · Artificial Intelligence

Challenges and Techniques in Image Search: Facenet Model and Triplet Loss

The article discusses the evolution of image search engines, outlines key challenges such as image quality, watermarks, speed, and feature extraction, and explains how the Facenet deep‑learning model with Triplet loss can be used to generate compact image embeddings for efficient similarity search.

Feature ExtractionImage Searchcomputer vision
0 likes · 7 min read
Challenges and Techniques in Image Search: Facenet Model and Triplet Loss