Trending Research
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.
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.
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.
LBM: Latent Bridge Matching for Fast Image-to-Image Translation
gojasper/lbm
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In this paper, we introduce Latent Bridge Matching (LBM), a new, versatile and scalable method that relies on Bridge Matching in a latent space to achieve fast image-to-image translation.
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.
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.
Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
osilly/vision-r1
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However, direct training with RL struggles to activate complex reasoning capabilities such as questioning and reflection in MLLMs, due to the absence of substantial high-quality multimodal reasoning data.
Scaling Synthetic Data Creation with 1,000,000,000 Personas
camel-ai/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.
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees
alexfrom0815/Online-3D-BPP-PCT
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ICLR 2022
PCT is a full-fledged description of the state and action space of bin packing which can support packing policy learning based on deep reinforcement learning (DRL).
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL
tidedra/lmm-r1
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Enhancing reasoning in Large Multimodal Models (LMMs) faces unique challenges from the complex interplay between visual perception and logical reasoning, particularly in compact 3B-parameter architectures where architectural constraints limit reasoning capacity and modality alignment.
