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Apr 15, 2026 · Artificial Intelligence

Boosting Bag Item Identification with Metric Learning: A ZhiZhuan Case Study

ZhiZhuan’s in‑house “photo‑to‑SKU” system tackles large‑scale bag identification by combining dual‑stage object detection, metric‑learning‑based embedding training, and a hybrid vector‑plus‑scalar retrieval pipeline, achieving superior top‑K accuracy over third‑party solutions while addressing fine‑grained visual nuances and long‑tail SKU coverage.

Deep LearningEmbeddingbag identification

0 likes · 16 min read

Boosting Bag Item Identification with Metric Learning: A ZhiZhuan Case Study

HyperAI Super Neural

Jan 22, 2026 · Artificial Intelligence

Mapping the Human E3 Ubiquitin Ligase Landscape with Metric Learning

A German research team integrated protein sequences, domain architectures, 3D structures, functional annotations and expression profiles to build a multi‑scale, metric‑learning classification of the human E3 ubiquitin ligase repertoire, revealing family hierarchies, essential enzymes for cell viability and new drug‑target opportunities.

CRISPR screeningE3 ligasesdrug discovery

0 likes · 14 min read

Mapping the Human E3 Ubiquitin Ligase Landscape with Metric Learning

Data Party THU

Sep 25, 2025 · Artificial Intelligence

Mastering Triplet Loss in Sentence‑Transformers: A Step‑by‑Step Guide

This article explains the concept of triplet loss, its mathematical formulation, the different batch‑wise implementations in the sentence_transformers library, their advantages and drawbacks, and provides a complete Python example for training a text‑embedding model with Triplet Loss.

EmbeddingPyTorchPython

0 likes · 12 min read

Mastering Triplet Loss in Sentence‑Transformers: A Step‑by‑Step Guide

HelloTech

Jun 21, 2023 · Artificial Intelligence

Overview of Haro Intelligent Customer Service: Algorithms, Challenges, and AI Solutions

Haro’s intelligent customer service combines a smart FAQ recommender and a conversational chatbot that leverages matching‑based intent recognition, large‑scale domain pre‑training, metric‑learning for new intents, and fine‑tuned generative LLMs, achieving 82 % top‑1 accuracy while reducing human workload and outlining future API‑orchestrated, multimodal AI enhancements.

AINLPlarge language model

0 likes · 10 min read

Overview of Haro Intelligent Customer Service: Algorithms, Challenges, and AI Solutions

DataFunSummit

Jan 10, 2022 · Artificial Intelligence

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

This article explains how deep learning transforms raw physical‑world data into dense vectors, defines the significance of vector retrieval, surveys common use cases such as image, video, and text search, discusses challenges in representation learning, and reviews high‑performance approximate nearest‑neighbor algorithms and practical deployments.

AI applicationsDeep Learningapproximate nearest neighbor

0 likes · 21 min read

Understanding Vector Retrieval: Principles, Applications, and High‑Performance Algorithms

Amap Tech

Nov 4, 2021 · Artificial Intelligence

POI Signboard Image Retrieval: Technical Solution, Model Design, and Future Directions

To efficiently filter unchanged POI signboards, the authors propose a multimodal image‑retrieval system that combines enhanced global and local visual features with BERT‑encoded OCR text, using metric learning and alignment techniques to achieve over 95 % accuracy while handling occlusion, viewpoint variation, and subtle text changes.

Computer VisionDeep LearningMultimodal Learning

0 likes · 17 min read

POI Signboard Image Retrieval: Technical Solution, Model Design, and Future Directions

Kuaishou Tech

Apr 6, 2021 · Artificial Intelligence

Frequency-Aware Feature Learning with Single-Center Loss for Face Forgery Detection

Researchers from USTC and Kuaishou propose a frequency‑aware feature learning framework that combines a data‑driven adaptive frequency module with a novel single‑center loss, achieving state‑of‑the‑art performance on deepfake detection while addressing class‑distribution challenges.

AI securityComputer Visiondeepfake detection

0 likes · 7 min read

Frequency-Aware Feature Learning with Single-Center Loss for Face Forgery Detection