Paper-Conference

Dissecting Deep RL with High Update Ratios: Combatting Value Divergence
We show that deep reinforcement learning can maintain its ability to learn without resetting network parameters in settings where the …
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When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning
We investigate the impact of auxiliary learning tasks such as observation reconstruction and latent self-prediction on the …
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Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention
Understanding road geometry is a critical component of the autonomous vehicle (AV) stack. While high-definition (HD) maps can readily …
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Watch Your Steps: Local Image and Scene Editing by Text Instruction
Denoising diffusion models have enabled high-quality image generation and editing. We present a method to localize the desired edit …
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LEOD: Label-Efficient Object Detection for Event Cameras
Object detection with event cameras benefits from the sensor’s low latency and high dynamic range. However, it is costly to fully label …
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Producing and Leveraging Online Map Uncertainty in Trajectory Prediction
High-definition (HD) maps have played an integral role in the development of modern autonomous vehicle (AV) stacks albeit with high …
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SPAD: Spatially Aware Multi-View Diffusers
We present SPAD, a novel approach for creating consistent multi-view images from text prompts or single images. To enable multi-view …
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Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers
Large-scale multi-task robotic manipulation systems often rely on text to specify the task. In this work, we explore whether a robot …
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AvatarOne: Monocular 3D Human Animation
Reconstructing realistic human avatars from monocular videos is a challenge that demands intricate modeling of 3D surface and …
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iNVS: Repurposing Diffusion Inpainters for Novel View Synthesis
We present a method for generating consistent novel views from a single source image. Our approach focuses on maximizing the reuse of …
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