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20142023
most citedObject Detectors Emerge in Deep Scene CNNs

715 citations · 4.1k across the 100 of their papers we have counts for

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Showing 2022Show all

16 papers · 1 filter

cs.CV2022★ 1 cited

DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis

Yinghao Xu, Menglei Chai, Zifan Shi +8

Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of obj…

cs.CV2022★ 2 cited

Towards Smooth Video Composition

Qihang Zhang, Ceyuan Yang, Yujun Shen +2

Video generation requires synthesizing consistent and persistent frames with dynamic content over time. This work investigates modeling the temporal relations for composing video w…

cs.LG2022★ 2 cited

Optimistic Curiosity Exploration and Conservative Exploitation with Linear Reward Shaping

Hao Sun, Lei Han, Rui Yang +3

In this work, we study the simple yet universally applicable case of reward shaping in value-based Deep Reinforcement Learning (DRL). We show that reward shifting in the form of th…

cs.RO2022★ 1 cited

TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarios

Lan Feng, Quanyi Li, Zhenghao Peng +2

Diverse and realistic traffic scenarios are crucial for evaluating the AI safety of autonomous driving systems in simulation. This work introduces a data-driven method called Traff…

cs.CV2022★ 12 cited

Improving GANs with A Dynamic Discriminator

Ceyuan Yang, Yujun Shen, Yinghao Xu +3

Discriminator plays a vital role in training generative adversarial networks (GANs) via distinguishing real and synthesized samples. While the real data distribution remains the sa…

cs.CV2022★ 2 cited

V2XP-ASG: Generating Adversarial Scenes for Vehicle-to-Everything Perception

Hao Xiang, Runsheng Xu, Xin Xia +3

Recent advancements in Vehicle-to-Everything communication technology have enabled autonomous vehicles to share sensory information to obtain better perception performance. With th…