Search Results for author: Changan Chen

Found 14 papers, 6 papers with code

Novel-View Acoustic Synthesis

no code implementations20 Jan 2023 Changan Chen, Alexander Richard, Roman Shapovalov, Vamsi Krishna Ithapu, Natalia Neverova, Kristen Grauman, Andrea Vedaldi

We introduce the novel-view acoustic synthesis (NVAS) task: given the sight and sound observed at a source viewpoint, can we synthesize the sound of that scene from an unseen target viewpoint?

Neural Rendering Novel View Synthesis

Few-Shot Audio-Visual Learning of Environment Acoustics

no code implementations8 Jun 2022 Sagnik Majumder, Changan Chen, Ziad Al-Halah, Kristen Grauman

Room impulse response (RIR) functions capture how the surrounding physical environment transforms the sounds heard by a listener, with implications for various applications in AR, VR, and robotics.

audio-visual learning

Sound Adversarial Audio-Visual Navigation

1 code implementation ICLR 2022 Yinfeng Yu, Wenbing Huang, Fuchun Sun, Changan Chen, Yikai Wang, Xiaohong Liu

In this work, we design an acoustically complex environment in which, besides the target sound, there exists a sound attacker playing a zero-sum game with the agent.

Navigate Visual Navigation

Visual Acoustic Matching

no code implementations CVPR 2022 Changan Chen, Ruohan Gao, Paul Calamia, Kristen Grauman

We introduce the visual acoustic matching task, in which an audio clip is transformed to sound like it was recorded in a target environment.

Learning Audio-Visual Dereverberation

no code implementations14 Jun 2021 Changan Chen, Wei Sun, David Harwath, Kristen Grauman

The visual environment surrounding a human speaker reveals important cues about the room geometry, materials, and speaker location, all of which influence the precise reverberation effects in the audio stream.

Automatic Speech Recognition Speaker Identification +2

Semantic Audio-Visual Navigation

no code implementations CVPR 2021 Changan Chen, Ziad Al-Halah, Kristen Grauman

We propose a transformer-based model to tackle this new semantic AudioGoal task, incorporating an inferred goal descriptor that captures both spatial and semantic properties of the target.

Visual Navigation

Learning to Set Waypoints for Audio-Visual Navigation

1 code implementation ICLR 2021 Changan Chen, Sagnik Majumder, Ziad Al-Halah, Ruohan Gao, Santhosh Kumar Ramakrishnan, Kristen Grauman

In audio-visual navigation, an agent intelligently travels through a complex, unmapped 3D environment using both sights and sounds to find a sound source (e. g., a phone ringing in another room).

Visual Navigation

VisualEchoes: Spatial Image Representation Learning through Echolocation

no code implementations ECCV 2020 Ruohan Gao, Changan Chen, Ziad Al-Halah, Carl Schissler, Kristen Grauman

Several animal species (e. g., bats, dolphins, and whales) and even visually impaired humans have the remarkable ability to perform echolocation: a biological sonar used to perceive spatial layout and locate objects in the world.

Monocular Depth Estimation Representation Learning +2

SoundSpaces: Audio-Visual Navigation in 3D Environments

2 code implementations ECCV 2020 Changan Chen, Unnat Jain, Carl Schissler, Sebastia Vicenc Amengual Gari, Ziad Al-Halah, Vamsi Krishna Ithapu, Philip Robinson, Kristen Grauman

Moving around in the world is naturally a multisensory experience, but today's embodied agents are deaf---restricted to solely their visual perception of the environment.

Navigate Visual Navigation

Relational Graph Learning for Crowd Navigation

1 code implementation28 Sep 2019 Changan Chen, Sha Hu, Payam Nikdel, Greg Mori, Manolis Savva

We present a relational graph learning approach for robotic crowd navigation using model-based deep reinforcement learning that plans actions by looking into the future.

Graph Learning reinforcement Learning

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

5 code implementations24 Sep 2018 Changan Chen, Yuejiang Liu, Sven Kreiss, Alexandre Alahi

We propose to (i) rethink pairwise interactions with a self-attention mechanism, and (ii) jointly model Human-Robot as well as Human-Human interactions in the deep reinforcement learning framework.

Human Dynamics Navigate +3

Constraint-Aware Deep Neural Network Compression

no code implementations ECCV 2018 Changan Chen, Frederick Tung, Naveen Vedula, Greg Mori

Deep neural network compression has the potential to bring modern resource-hungry deep networks to resource-limited devices.

Neural Network Compression Pedestrian Detection

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