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Found 15 papers, 7 papers with code
A 2D Semantic-Aware Position Encoding for Vision Transformers
Traditional approaches like absolute position encoding and relative position encoding primarily focus on 1D linear position relationship, often neglecting the semantic similarity between distant yet contextually related patches.
NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks
Existing Visual-Language-Action (VLA) models have shown promising performance in zero-shot scenarios, demonstrating impressive task execution and reasoning capabilities.
PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model Inference
To address this, we propose PromptDistill, a novel, training-free method that improves inference efficiency while preserving generation quality.
Goku: Flow Based Video Generative Foundation Models
This paper introduces Goku, a state-of-the-art family of joint image-and-video generation models leveraging rectified flow Transformers to achieve industry-leading performance.
Scalable Language Models with Posterior Inference of Latent Thought Vectors
We propose a novel family of language models, Latent-Thought Language Models (LTMs), which incorporate explicit latent thought vectors that follow an explicit prior model in latent space.
TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization
We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model with 515M parameters, capable of generating up to 30 seconds of 44. 1kHz audio in just 3. 7 seconds on a single A40 GPU.
Ranked #2 on
Audio Generation
on AudioCaps
Utilizing Large Language Models for Natural Interface to Pharmacology Databases
The drug development process necessitates that pharmacologists undertake various tasks, such as reviewing literature, formulating hypotheses, designing experiments, and interpreting results.
Implicit Neural Representation as a Differentiable Surrogate for Photon Propagation in a Monolithic Neutrino Detector
As the size of a table increases with detector volume for a fixed resolution, this method scales poorly for future larger detectors.
clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIP
We introduce a new method to efficiently create text-to-image models from a pre-trained CLIP and StyleGAN.
Multiple Pairwise Ranking Networks for Personalized Video Summarization
In this paper, we investigate video summarization in the supervised setting.
NPRportrait 1.0: A Three-Level Benchmark for Non-Photorealistic Rendering of Portraits
Despite the recent upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer, the state of performance evaluation in this field is limited, especially compared to the norms in the computer vision and machine learning communities.
HoloGAN: Unsupervised learning of 3D representations from natural images
This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.
RenderNet: A deep convolutional network for differentiable rendering from 3D shapes
We present RenderNet, a differentiable rendering convolutional network with a novel projection unit that can render 2D images from 3D shapes.
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis.
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images.
