Trending Research
AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One
nvlabs/radio
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A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks.
Spectrally Pruned Gaussian Fields with Neural Compensation
However, this comes with high memory consumption, e. g., a well-trained Gaussian field may utilize three million Gaussian primitives and over 700 MB of memory.
WavCraft: Audio Editing and Generation with Natural Language Prompts
We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing.
PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
magic-research/PLLaVA
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arXiv 2024
PLLaVA achieves new state-of-the-art performance on modern benchmark datasets for both video question-answer and captioning tasks.
Dense Captioning
Video-based Generative Performance Benchmarking
+1
Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
This collaborative prompting approach empowers a single LM to simultaneously act as a comprehensive orchestrator and a panel of diverse experts, significantly enhancing its performance across a wide array of tasks.
FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent
This paper introduces FlowMap, an end-to-end differentiable method that solves for precise camera poses, camera intrinsics, and per-frame dense depth of a video sequence.
MicroDreamer: Zero-shot 3D Generation in $\sim$20 Seconds by Score-based Iterative Reconstruction
In this paper, we introduce score-based iterative reconstruction (SIR), an efficient and general algorithm for 3D generation with a multi-view score-based diffusion model.
CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data
apple/corenet
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Contrastive learning has emerged as a transformative method for learning effective visual representations through the alignment of image and text embeddings.
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
We present Visual AutoRegressive modeling (VAR), a new generation paradigm that redefines the autoregressive learning on images as coarse-to-fine "next-scale prediction" or "next-resolution prediction", diverging from the standard raster-scan "next-token prediction".
Ranked #7 on
Image Generation
on ImageNet 256x256
MemGPT: Towards LLMs as Operating Systems
Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis.
