技能目录

为 AI Agent 发现可复用技能。

按任务搜索真实的 GitHub 技能,并在使用前查看 Stars、信任、审计、分类和安装路径。

每个推荐都保留与其仓库、审计和安装路径的明确关联。

搜索结果: encoder

英文目录

Enhanced LanceDB memory plugin for OpenClaw — Hybrid Retrieval (Vector + BM25), Cross-Encoder Rerank, Multi-Scope Isolation, Management CLI

4.4K
Stars
73/100
信任
分类: data审计

[ACL 2024 🔥] Video-ChatGPT is a video conversation model capable of generating meaningful conversation about videos. It combines the capabilities of LLMs with a pretrained visual encoder adapted for spatiotemporal video representation. We also introduce a rigorous 'Quantitative Evaluation Benchmarking' for video-based conversational models.

1.5K
Stars
73/100
信任
分类: support-automation审计

Hybrid RAG system combining vector search, knowledge graph (LightRAG), and cross-encoder reranking — with Docling document parsing, visual intelligence (image/table captioning), agentic streaming chat, and inline citations. Powered by Gemini or local Ollama models.

317
Stars
65/100
信任
分类: rag-knowledge审计

Multilingual TTS model with voice cloning and duration control, based on T5Gemma encoder-decoder LLM

308
Stars
69/100
信任
分类: media-automation审计

Production-grade RAG API built in Rust. Hybrid search with HNSW dense vectors and BM25 sparse matching, cross-encoder reranking, layout-aware document extraction via Docling, and 94.5% accuracy on Open RAG Bench. Powered by Cerebras, Groq, Milvus, and Jina AI.

196
Stars
63/100
信任
分类: document-processing审计

[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

907
Stars
59/100
信任
分类: robotics-iot审计

LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation

522
Stars
64/100
信任
分类: robotics-iot审计

This repository implements the the encoder and decoder model with attention model for OCR

358
Stars
59/100
信任
分类: document-processing审计

Contextual Encoder-Decoder Network for Visual Saliency Prediction [Neural Networks 2020]

215
Stars
62/100
信任
分类: robotics-iot审计

PyTorch implementation of "Lagging Inference Networks and Posterior Collapse in Variational Autoencoders" (ICLR 2019)

185
Stars
62/100
信任
分类: media-automation审计