Search Results for author: Bowen Liu

Found 15 papers, 9 papers with code

MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding

no code implementations CVPR 2023 Bowen Liu, Yu Chen, Rakesh Chowdary Machineni, Shiyu Liu, Hun-Seok Kim

In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal mode for feature domain prediction adapting to different motion patterns.

Benchmarking MS-SSIM +4

Unified Signal Compression Using a GAN with Iterative Latent Representation Optimization

1 code implementation23 Sep 2021 Bowen Liu, Changwoo Lee, Ang Cao, Hun-Seok Kim

We propose a unified signal compression framework that uses a generative adversarial network (GAN) to compress heterogeneous signals.

Image Compression MS-SSIM +1

Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles

no code implementations11 Aug 2021 Liuhui Ding, Dachuan Li, Bowen Liu, Wenxing Lan, Bing Bai, Qi Hao, Weipeng Cao, Ke Pei

Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles.

Autonomous Driving Motion Planning +2

Deep Learning in Latent Space for Video Prediction and Compression

1 code implementation CVPR 2021 Bowen Liu, Yu Chen, Shiyu Liu, Hun-Seok Kim

The proposed method first learns the efficient lower-dimensional latent space representation of each video frame and then performs inter-frame prediction in that latent domain.

Anomaly Detection Event Detection +2

Uncertainty-aware Joint Salient Object and Camouflaged Object Detection

2 code implementations CVPR 2021 Aixuan Li, Jing Zhang, Yunqiu Lv, Bowen Liu, Tong Zhang, Yuchao Dai

Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding.

object-detection Object Detection +1

Simultaneously Localize, Segment and Rank the Camouflaged Objects

1 code implementation CVPR 2021 Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, Deng-Ping Fan

With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects.

object-detection Object Detection

A First Look into DeFi Oracles

no code implementations9 May 2020 Bowen Liu, Pawel Szalachowski, Jianying Zhou

In this paper, we present the first study of DeFi oracles deployed in practice.

Cryptography and Security

Open Graph Benchmark: Datasets for Machine Learning on Graphs

16 code implementations NeurIPS 2020 Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec

We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.

Knowledge Graphs

Probabilistic K-means Clustering via Nonlinear Programming

no code implementations10 Jan 2020 Yujian Li, Bowen Liu, Zhaoying Liu, Ting Zhang

In theory, we can solve the model by active gradient projection, while inefficiently.

Unified Signal Compression Using Generative Adversarial Networks

1 code implementation8 Dec 2019 Bowen Liu, Ang Cao, Hun-Seok Kim

We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals.

Strategies for Pre-training Graph Neural Networks

9 code implementations ICLR 2020 Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec

Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.

Graph Classification Molecular Property Prediction +2

Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

2 code implementations NeurIPS 2018 Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec

Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.

Graph Generation Molecular Graph Generation

Retrosynthetic reaction prediction using neural sequence-to-sequence models

no code implementations6 Jun 2017 Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, Vijay Pande

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem.

Machine Translation Translation

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