M-BEST-RQ: A Multi-Channel Speech Foundation Model for Wearable Devices
M-BEST-RQ is a multi-channel speech foundation model designed to leverage large-scale self-supervised learning for tasks on wearable devices such as smart glasses, enabling array-geometry agnostic representations and strong performance across multiple downstream applications.
High-Resolution Lunar Landing Simulations for Vision-Based Navigation
High-performance global lunar terrain models and simulations are critical for developing and validating vision-based navigation algorithms for future lunar missions.
단일 이미지에서 3D 어두운 면 탐험하기
단일 이미지에서 다양하고 일관된 3D 객체를 효율적으로 생성하는 Vista3D 프레임워크를 제안한다.
Unveiling the Hidden 3D Dimensions of Objects from a Single Image: Vista3D, an Efficient and Diverse 3D Generation Framework
Vista3D is a framework that efficiently generates diverse and consistent 3D objects from a single input image by leveraging a coarse-to-fine approach and an angular-based composition of diffusion priors.
Qwen2-VL: 다양한 해상도에서 세계에 대한 비전-언어 모델의 인식 향상
Qwen2-VL은 기존의 고정된 해상도 접근 방식을 재정의하여 이미지를 다양한 해상도로 동적으로 처리할 수 있는 기능을 제공합니다. 이를 통해 모델은 인간의 지각 과정과 더 밀접하게 연계된 효율적이고 정확한 시각적 표현을 생성할 수 있습니다.
Qwen2-VL: 任意解像度における視覚言語モデルの世界認識の向上
Qwen2-VLシリーズは、従来の固定解像度アプローチを刷新し、動的解像度処理機能を導入することで、画像の詳細情報を効率的かつ正確に表現できるようになった。また、マルチモーダルな位置情報埋め込みを活用し、テキスト、画像、動画の融合を強化した。これにより、Qwen2-VLは視覚認識能力を大幅に向上させている。
Qwen2-VL: Advancing Vision-Language Models to Perceive the World at Any Resolution
Qwen2-VL, a series of advanced vision-language models, introduces novel mechanisms to dynamically process images and videos of varying resolutions, enabling more efficient and accurate visual representations that closely align with human perception.
3次元視覚における重要な手法であるバンドル調整を、PyTorchのイーガーモードで効率的に実装する
PyTorchのイーガーモードで効率的にバンドル調整を実行できる新しいフレームワークを提案する。これにより、深層学習モデルとの統合が容易になり、柔軟性と適応性が向上する。
Realistic Long-Term 3D Human Motion Forecasting with Multimodal Scene Context
The authors propose a scene-aware social transformer model (SAST) that can efficiently forecast long-term (10 seconds) human motion in complex multi-person environments by leveraging both motion and scene context information.
Null-Shot Image Super-Resolution Using Deep Identity Learning: A Computationally Efficient Approach Independent of Image Data
The proposed NSSR-DIL model learns the inverse degradation kernel from the degradation kernel itself, without the need for low-resolution (LR) image input, enabling computationally efficient and data-independent image super-resolution.
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