Trending Research
Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens
Recent advancements in large language models (LLMs) have driven significant progress in zero-shot text-to-speech (TTS) synthesis.
YOLOE: Real-Time Seeing Anything
THU-MIG/yoloe
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Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predefined categories, hindering adaptability in open scenarios.
Scaling Synthetic Data Creation with 1,000,000,000 Personas
lightaime/camel
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We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data.
VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control
Video inpainting, which aims to restore corrupted video content, has experienced substantial progress.
Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts
bytedance/flux
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The inter-device communication of a MoE layer can occupy 47% time of the entire model execution with popular models and frameworks.
GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control
Our results demonstrate more precise camera control than prior work, as well as state-of-the-art results in sparse-view novel view synthesis, even in challenging settings such as driving scenes and monocular dynamic video.
Visual-RFT: Visual Reinforcement Fine-Tuning
Reinforcement Fine-Tuning (RFT) in Large Reasoning Models like OpenAI o1 learns from feedback on its answers, which is especially useful in applications when fine-tuning data is scarce.
Few-Shot Object Detection
Fine-Grained Image Classification
+4
AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
Introducing AgiBot World, a large-scale platform comprising over 1 million trajectories across 217 tasks in five deployment scenarios, we achieve an order-of-magnitude increase in data scale compared to existing datasets.
FoundationStereo: Zero-Shot Stereo Matching
However, achieving strong zero-shot generalization - a hallmark of foundation models in other computer vision tasks - remains challenging for stereo matching.
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
Traditional methods for reasoning segmentation rely on supervised fine-tuning with categorical labels and simple descriptions, limiting its out-of-domain generalization and lacking explicit reasoning processes.
