Search Results for author:
Found 18 papers, 7 papers with code
MagicDriveDiT: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control
The rapid advancement of diffusion models has greatly improved video synthesis, especially in controllable video generation, which is essential for applications like autonomous driving.
MagicDrive3D: Controllable 3D Generation for Any-View Rendering in Street Scenes
While controllable generative models for images and videos have achieved remarkable success, high-quality models for 3D scenes, particularly in unbounded scenarios like autonomous driving, remain underdeveloped due to high data acquisition costs.
Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases
Moreover, with our CODA-LM, we build CODA-VLM, a new driving LVLM surpassing all open-sourced counterparts on CODA-LM.
DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception
Furthermore, image syntheses from DetDiffusion can effectively augment training data, significantly enhancing downstream detection performance.
GuardT2I: Defending Text-to-Image Models from Adversarial Prompts
Recent advancements in Text-to-Image (T2I) models have raised significant safety concerns about their potential misuse for generating inappropriate or Not-Safe-For-Work (NSFW) contents, despite existing countermeasures such as NSFW classifiers or model fine-tuning for inappropriate concept removal.
TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion Models
Despite remarkable achievements in video synthesis, achieving granular control over complex dynamics, such as nuanced movement among multiple interacting objects, still presents a significant hurdle for dynamic world modeling, compounded by the necessity to manage appearance and disappearance, drastic scale changes, and ensure consistency for instances across frames.
Non-Cross Diffusion for Semantic Consistency
In diffusion models, deviations from a straight generative flow are a common issue, resulting in semantic inconsistencies and suboptimal generations.
MMA-Diffusion: MultiModal Attack on Diffusion Models
In recent years, Text-to-Image (T2I) models have seen remarkable advancements, gaining widespread adoption.
MagicDrive: Street View Generation with Diverse 3D Geometry Control
Recent advancements in diffusion models have significantly enhanced the data synthesis with 2D control.
DiffGuard: Semantic Mismatch-Guided Out-of-Distribution Detection using Pre-trained Diffusion Models
There is a recent work that directly applies it to OOD detection, which employs a conditional Generative Adversarial Network (cGAN) to enlarge semantic mismatch in the image space.
Generative Adversarial Network
Out-of-Distribution Detection
Out-of-Distribution Detection with Semantic Mismatch under Masking
This paper proposes a novel out-of-distribution (OOD) detection framework named MoodCat for image classifiers.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation
This paper proposes a simple baseline framework for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation, named DeciWatch.
Ranked #1 on
2D Human Pose Estimation
on JHMDB (2D poses only)
What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction
For legitimate inputs that are correctly inferred, the synthetic output tries to reconstruct the input.
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
In this work, we propose a novel tree-structured wavelet neural network for time series signal analysis, namely T-WaveNet, by taking advantage of an inherent property of various types of signals, known as the dominant frequency range.
Relational Graph Neural Network Design via Progressive Neural Architecture Search
We propose a novel solution to addressing a long-standing dilemma in the representation learning of graph neural networks (GNNs): how to effectively capture and represent useful information embedded in long-distance nodes to improve the performance of nodes with low homophily without leading to performance degradation in nodes with high homophily.
MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis
Consequently, we propose a novel learning-based solution to model such contradictions for AE detection.
ModuleNet: Knowledge-inherited Neural Architecture Search
Unlike previous search algorithms, and benefiting from inherited knowledge, our method is able to directly search for architectures in the macro space by NSGA-II algorithm without tuning parameters in these \textit{module}s. Experiments show that our strategy can efficiently evaluate the performance of new architecture even without tuning weights in convolutional layers.
Privacy for Rescue: A New Testimony Why Privacy is Vulnerable In Deep Models
Existing research works rely on metrics that are either impractical or insufficient to measure the effectiveness of privacy protection methods in the above scenario, especially from the aspect of a single user.
