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Found 15 papers, 8 papers with code
PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation
Unlike unconditional or text-conditioned dynamics generation, action-conditioned dynamics requires perceiving the physical material properties of objects and grounding the 3D motion prediction on these properties, such as object stiffness.
Exploring the Physical World Adversarial Robustness of Vehicle Detection
Through this pipeline, we establish the Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive experiments involving three detection models and three physical adversarial attacks.
RobustMQ: Benchmarking Robustness of Quantized Models
Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources.
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection
Thus, virtual simulation experiments can provide a solution to this challenge.
Benchmarking the Robustness of Quantized Models
To address this gap, we thoroughly evaluated the robustness of quantized models against various noises (adversarial attacks, natural corruptions, and systematic noises) on ImageNet.
Analyzing Physical Impacts Using Transient Surface Wave Imaging
The subtle vibrations on an object's surface contain information about the object's physical properties and its interaction with the environment.
ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries
In this work, we propose ViP3D, a query-based visual trajectory prediction pipeline that exploits rich information from raw videos to directly predict future trajectories of agents in a scene.
MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries
In contrast to prior works, MUTR3D does not explicitly rely on the spatial and appearance similarity of objects.
FUTR3D: A Unified Sensor Fusion Framework for 3D Detection
Sensor fusion is an essential topic in many perception systems, such as autonomous driving and robotics.
Embracing Single Stride 3D Object Detector with Sparse Transformer
In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases.
Ranked #3 on
3D Object Detection
on waymo cyclist
DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
This top-down approach outperforms its bottom-up counterpart in which object bounding box prediction follows per-pixel depth estimation, since it does not suffer from the compounding error introduced by a depth prediction model.
Ranked #7 on
Robust Camera Only 3D Object Detection
on nuScenes-C
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for Video Frame Interpolation (VFI).
Domain-Aware Dynamic Networks
In this work, we propose a method to improve the model capacity without increasing inference-time complexity.
Interpreting Adversarially Trained Convolutional Neural Networks
Our findings shed some light on why AT-CNNs are more robust than those normally trained ones and contribute to a better understanding of adversarial training over CNNs from an interpretation perspective.
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
Adversarial training, typically formulated as a robust optimization problem, is an effective way of improving the robustness of deep networks.
