Search Results for author:
Found 22 papers, 10 papers with code
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
The resultant models, denoted as MobileLLM-LS, demonstrate a further accuracy enhancement of 0. 7%/0. 8% than MobileLLM 125M/350M.
Taming Mode Collapse in Score Distillation for Text-to-3D Generation
In this paper, we reveal that the existing score distillation-based text-to-3D generation frameworks degenerate to maximal likelihood seeking on each view independently and thus suffer from the mode collapse problem, manifesting as the Janus artifact in practice.
SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity
In this paper, we reveal that the gradient estimation in score distillation is inherent to high variance.
SqueezeSAM: User friendly mobile interactive segmentation
A common user expectation is that a click on a specific part of an object will result in the segmentation of the entire object.
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
On segment anything task such as zero-shot instance segmentation, our EfficientSAMs with SAMI-pretrained lightweight image encoders perform favorably with a significant gain (e. g., ~4 AP on COCO/LVIS) over other fast SAM models.
Ranked #3 on
Zero-Shot Instance Segmentation
on LVIS v1.0 val
On The Open Prompt Challenge In Conditional Audio Generation
Text-to-audio generation (TTA) produces audio from a text description, learning from pairs of audio samples and hand-annotated text.
In-Context Prompt Editing For Conditional Audio Generation
We show that the framework enhanced the audio quality across the set of collected user prompts, which were edited with reference to the training captions as exemplars.
CVPR 2023 Text Guided Video Editing Competition
In this paper we present a retrospective on the competition and describe the winning method.
Enhance audio generation controllability through representation similarity regularization
This paper presents an innovative approach to enhance control over audio generation by emphasizing the alignment between audio and text representations during model training.
Stack-and-Delay: a new codebook pattern for music generation
In language modeling based music generation, a generated waveform is represented by a sequence of hierarchical token stacks that can be decoded either in an auto-regressive manner or in parallel, depending on the codebook patterns.
SqueezeNAS: Fast neural architecture search for faster semantic segmentation
For real time applications utilizing Deep Neural Networks (DNNs), it is critical that the models achieve high-accuracy on the target task and low-latency inference on the target computing platform.
Ranked #68 on
Semantic Segmentation
on Cityscapes val
(using extra training data)
DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning
Recently, autonomous vehicles have created a demand for depth information, which is often obtained using hardware sensors such as Light detection and ranging (LIDAR).
Keynote: Small Neural Nets Are Beautiful: Enabling Embedded Systems with Small Deep-Neural-Network Architectures
Over the last five years Deep Neural Nets have offered more accurate solutions to many problems in speech recognition, and computer vision, and these solutions have surpassed a threshold of acceptability for many applications.
Exploring the Design Space of Deep Convolutional Neural Networks at Large Scale
Exploring the design space of CNN architectures.
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment.
How to scale distributed deep learning?
Training time on large datasets for deep neural networks is the principal workflow bottleneck in a number of important applications of deep learning, such as object classification and detection in automatic driver assistance systems (ADAS).
Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms
Results are presented for a case study of targeting the Qualcomm Snapdragon 820 mobile computing platform for CNN deployment.
libHOG: Energy-Efficient Histogram of Oriented Gradient Computation
Histogram of Oriented Gradients (HOG) features are the underlying representation in automotive computer vision applications such as collision avoidance and lane keeping.
From Captions to Visual Concepts and Back
The language model learns from a set of over 400, 000 image descriptions to capture the statistics of word usage.
Ranked #1 on
Image Captioning
on COCO Captions test
Deformable Part Models are Convolutional Neural Networks
Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition.
Ranked #27 on
Object Detection
on PASCAL VOC 2007
DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
Convolutional Neural Networks (CNNs) can provide accurate object classification.
Deformable Part Descriptors for Fine-grained Recognition and Attribute Prediction
Recognizing objects in fine-grained domains can be extremely challenging due to the subtle differences between subcategories.
Ranked #26 on
Fine-Grained Image Classification
on CUB-200-2011
