huggingfaceHugging Faceあり2026-09-03
The Attention Triangle in Audio-Video Models
Audio-video diffusion models rely on cross-modal attention to coordinate text, sound, and visual content, yet
品質予測/異常検知深層学習Attention機構生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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「multimodal」の検索結果
8 件huggingfaceHugging Faceあり2026-09-01
A Glance Is All You Need: Single-Pass Fine-Grained Image Captioning with SimLoss
An image may be worth a thousand words, but most captioning models describe it in only a few. Modern vision-la
MI向き品質予測/異常検知自然言語処理ファインチューニング生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-08-31
NeoMME: A Single-Tower Multimodal-Native Multilingual Foundation Encoder for Efficient Fine-Tuning and Inference
Multimodal models often build on architectures designed for generative vision-language modeling, typically com
深層学習Transformer生成画像テキスト
- 用途
- 生成
- 難易度
- Easy
- コスト
- High
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huggingfaceGitHubありHugging Faceあり2026-08-31
MULTI3IR: A Benchmark for Multi-perspective Multi-domain Multi-modal Information Retrieval
Information retrieval (IR) increasingly targets open-ended queries that admit diverse perspectives. Existing I
深層学習軽量化・量子化検索テキストマルチモーダル
- 用途
- 検索
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-08-31
FoldingAgent: Inferring Parametric Origami Procedures from Demonstration Videos
We present FoldingAgent, an agentic framework for inferring explicit parametric folding programs directly from
自然言語処理RAG画像テキスト動画
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-08-30
SnapBench: Benchmarking Snap-and-Ask Multimodal Retrieval for Mobile Interactions
Mobile AI acts as a visual oracle, empowering users to snap a picture of something and ask for information. Sn
自然言語処理RAG画像テキストマルチモーダル
- 用途
- 技術検証・論文読解補助
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-08-25
Real-World Knowledge-Guided Change Data Synthesis for Remote Sensing
Change data synthesis provides a cost-effective solution for expanding training data and improving the perform
センサ/時系列自然言語処理RAG検出生成テキスト
- 用途
- 検出
- 難易度
- Easy
- コスト
- High
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huggingfaceHugging Faceあり2026-08-07
Training-Free Speech-Centric Omni Understanding with Frozen VLMs
Audio-visual understanding remains challenging because models must jointly interpret spoken content, visual ev
センサ/時系列自然言語処理RAGQA画像テキスト
- 用途
- QA
- 難易度
- Easy
- コスト
- High
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