Search Results for author: Forrest Iandola

Found 22 papers, 9 papers with code

Taming Mode Collapse in Score Distillation for Text-to-3D Generation

no code implementations31 Dec 2023 Peihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra

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.

Prompt Engineering Text to 3D

EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything

1 code implementation1 Dec 2023 Yunyang Xiong, Bala Varadarajan, Lemeng Wu, Xiaoyu Xiang, Fanyi Xiao, Chenchen Zhu, Xiaoliang Dai, Dilin Wang, Fei Sun, Forrest Iandola, Raghuraman Krishnamoorthi, Vikas Chandra

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.

Image Classification Instance Segmentation +5

On The Open Prompt Challenge In Conditional Audio Generation

no code implementations1 Nov 2023 Ernie Chang, Sidd Srinivasan, Mahi Luthra, Pin-Jie Lin, Varun Nagaraja, Forrest Iandola, Zechun Liu, Zhaoheng Ni, Changsheng Zhao, Yangyang Shi, Vikas Chandra

Text-to-audio generation (TTA) produces audio from a text description, learning from pairs of audio samples and hand-annotated text.

Audio Generation

In-Context Prompt Editing For Conditional Audio Generation

no code implementations1 Nov 2023 Ernie Chang, Pin-Jie Lin, Yang Li, Sidd Srinivasan, Gael Le Lan, David Kant, Yangyang Shi, Forrest Iandola, Vikas Chandra

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.

Audio Generation Retrieval

Enhance audio generation controllability through representation similarity regularization

no code implementations15 Sep 2023 Yangyang Shi, Gael Le Lan, Varun Nagaraja, Zhaoheng Ni, Xinhao Mei, Ernie Chang, Forrest Iandola, Yang Liu, Vikas Chandra

This paper presents an innovative approach to enhance control over audio generation by emphasizing the alignment between audio and text representations during model training.

Audio Generation Language Modelling +2

Stack-and-Delay: a new codebook pattern for music generation

no code implementations15 Sep 2023 Gael Le Lan, Varun Nagaraja, Ernie Chang, David Kant, Zhaoheng Ni, Yangyang Shi, Forrest Iandola, Vikas Chandra

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.

Language Modelling Music Generation

SqueezeNAS: Fast neural architecture search for faster semantic segmentation

1 code implementation5 Aug 2019 Albert Shaw, Daniel Hunter, Forrest Iandola, Sammy Sidhu

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 #61 on Semantic Segmentation on Cityscapes val (using extra training data)

Image Classification Neural Architecture Search +1

DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning

no code implementations17 Nov 2018 Paden Tomasello, Sammy Sidhu, Anting Shen, Matthew W. Moskewicz, Nobie Redmon, Gayatri Joshi, Romi Phadte, Paras Jain, Forrest Iandola

Recently, autonomous vehicles have created a demand for depth information, which is often obtained using hardware sensors such as Light detection and ranging (LIDAR).

Autonomous Vehicles Depth Estimation +4

Keynote: Small Neural Nets Are Beautiful: Enabling Embedded Systems with Small Deep-Neural-Network Architectures

no code implementations7 Oct 2017 Forrest Iandola, Kurt Keutzer

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.

speech-recognition Speech Recognition

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

13 code implementations4 Dec 2016 Bichen Wu, Alvin Wan, Forrest Iandola, Peter H. Jin, Kurt Keutzer

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.

Autonomous Driving Object +2

How to scale distributed deep learning?

no code implementations14 Nov 2016 Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer

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).

General Classification

libHOG: Energy-Efficient Histogram of Oriented Gradient Computation

1 code implementation ITSC 2015 Forrest Iandola, Matthew Moskewicz, Kurt Keutzer

Histogram of Oriented Gradients (HOG) features are the underlying representation in automotive computer vision applications such as collision avoidance and lane keeping.

Collision Avoidance Object Detection

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