Search Results for author:
Found 25 papers, 10 papers with code
The Larger the Better? Improved LLM Code-Generation via Budget Reallocation
On the other hand, in scenarios where unit-tests are unavailable, a ranking-based selection of candidates from the smaller model falls short of the performance of a single output from larger ones.
Masked Audio Generation using a Single Non-Autoregressive Transformer
We introduce MAGNeT, a masked generative sequence modeling method that operates directly over several streams of audio tokens.
Code Llama: Open Foundation Models for Code
We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks.
Ranked #26 on
Code Generation
on MBPP
EXPRESSO: A Benchmark and Analysis of Discrete Expressive Speech Resynthesis
Recent work has shown that it is possible to resynthesize high-quality speech based, not on text, but on low bitrate discrete units that have been learned in a self-supervised fashion and can therefore capture expressive aspects of speech that are hard to transcribe (prosody, voice styles, non-verbal vocalization).
Simple and Controllable Music Generation
We tackle the task of conditional music generation.
Ranked #4 on
Text-to-Music Generation
on MusicCaps
Textually Pretrained Speech Language Models
In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models.
ReVISE: Self-Supervised Speech Resynthesis With Visual Input for Universal and Generalized Speech Regeneration
Moreover, we utilize self-supervised audio-visual speech model to initialize P-AVSR.
ReVISE: Self-Supervised Speech Resynthesis with Visual Input for Universal and Generalized Speech Enhancement
Moreover, we utilize self-supervised audio-visual speech model to initialize P-AVSR.
Ranked #1 on
Speech Recognition
on EasyCom
AudioScopeV2: Audio-Visual Attention Architectures for Calibrated Open-Domain On-Screen Sound Separation
We identify several limitations of previous work on audio-visual on-screen sound separation, including the coarse resolution of spatio-temporal attention, poor convergence of the audio separation model, limited variety in training and evaluation data, and failure to account for the trade off between preservation of on-screen sounds and suppression of off-screen sounds.
More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech
In this paper we present VDTTS, a Visually-Driven Text-to-Speech model.
Translatotron 2: High-quality direct speech-to-speech translation with voice preservation
We present Translatotron 2, a neural direct speech-to-speech translation model that can be trained end-to-end.
Improving On-Screen Sound Separation for Open-Domain Videos with Audio-Visual Self-Attention
We introduce a state-of-the-art audio-visual on-screen sound separation system which is capable of learning to separate sounds and associate them with on-screen objects by looking at in-the-wild videos.
Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds
For evaluation and semi-supervised experiments, we collected human labels for presence of on-screen and off-screen sounds on a small subset of clips.
Class-Aware Fully-Convolutional Gaussian and Poisson Denoising
We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful.
Learning to Segment via Cut-and-Paste
This paper presents a weakly-supervised approach to object instance segmentation.
Efficient Deformable Shape Correspondence via Kernel Matching
We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality.
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence
We introduce a new framework for learning dense correspondence between deformable 3D shapes.
Deep Class Aware Denoising
We further show that a significant boost in performance of up to $0. 4$ dB PSNR can be achieved by making our network class-aware, namely, by fine-tuning it for images belonging to a specific semantic class.
Deep Convolutional Denoising of Low-Light Images
Poisson distribution is used for modeling noise in photon-limited imaging.
Cloud Dictionary: Sparse Coding and Modeling for Point Clouds
With the development of range sensors such as LIDAR and time-of-flight cameras, 3D point cloud scans have become ubiquitous in computer vision applications, the most prominent ones being gesture recognition and autonomous driving.
FPGA system for real-time computational extended depth of field imaging using phase aperture coding
We present a proof-of-concept end-to-end system for computational extended depth of field (EDOF) imaging.
Image reconstruction from dense binary pixels
Recently, the dense binary pixel Gigavision camera had been introduced, emulating a digital version of the photographic film.
ASIST: Automatic Semantically Invariant Scene Transformation
We present ASIST, a technique for transforming point clouds by replacing objects with their semantically equivalent counterparts.
Spatially Coherent Random Forests
Each tree in the forest produces a segmentation of the image plane and the boundaries of the segmentations of all trees are aggregated to produce a final hierarchical contour map.
A Picture is Worth a Billion Bits: Real-Time Image Reconstruction from Dense Binary Pixels
In this work, we study a variant of a sensor with binary threshold pixels and propose a reconstruction algorithm combining an ML data fitting term with a sparse synthesis prior.
