Trending Research
Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
For fine-grained language understanding, we train a Multimodal Large Language Model to refine the captions of the images.
GLACE: Global Local Accelerated Coordinate Encoding
cvg/glace
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We propose GLACE, which integrates pre-trained global and local encodings and enables SCR to scale to large scenes with only a single small-sized network.
TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representation
We address the problem of regressing 3D human pose and shape from a single image, with a focus on 3D accuracy.
Ranked #32 on
3D Human Pose Estimation
on 3DPW
SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing
modelscope/swift
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Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis.
VideoTetris: Towards Compositional Text-to-Video Generation
Diffusion models have demonstrated great success in text-to-video (T2V) generation.
CV-VAE: A Compatible Video VAE for Latent Generative Video Models
ailab-cvc/cv-vae
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Moreover, since current diffusion-based approaches are often implemented using pre-trained text-to-image (T2I) models, directly training a video VAE without considering the compatibility with existing T2I models will result in a latent space gap between them, which will take huge computational resources for training to bridge the gap even with the T2I models as initialization.
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
cmavro/gnn-rag
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In our GNN-RAG framework, the GNN acts as a dense subgraph reasoner to extract useful graph information, while the LLM leverages its natural language processing ability for ultimate KGQA.
Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models
Large Language Models (LLMs) are often described as being instances of foundation models - that is, models that transfer strongly across various tasks and conditions in few-show or zero-shot manner, while exhibiting scaling laws that predict function improvement when increasing the pre-training scale.
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
While Transformers have been the main architecture behind deep learning's success in language modeling, state-space models (SSMs) such as Mamba have recently been shown to match or outperform Transformers at small to medium scale.
$\textit{S}^3$Gaussian: Self-Supervised Street Gaussians for Autonomous Driving
Photorealistic 3D reconstruction of street scenes is a critical technique for developing real-world simulators for autonomous driving.
