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- MagicDrive: Street View Generation with Diverse 3D Geometry Control310
This paper introduces MagicDrive, a novel street view generation framework, offering diverse 3D geometry controls including camera poses, road maps, and 3D bounding boxes, together with textual descriptions, achieved through tailored encoding strategies.
- DDP: Diffusion Model for Dense Visual Prediction254
The method, called DDP, efficiently extends the denoising diffusion process into the modern perception pipeline and shows attractive properties such as dynamic inference and uncertainty awareness, in contrast to previous single-step discriminative methods.
- G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model233
G-LLaVA is developed, which demonstrates exceptional performance in solving geometric problems, significantly outperforming GPT-4-V on the MathVista benchmark with only 7B parameters.
- Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition215
This paper proposes a novel approach, called Self-supervised Aggregation of Diverse Experts, which consists of a new skill-diverse expert learning strategy that trains multiple experts from a single and stationary long-tailed dataset to separately handle different class distributions.
- OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization145
This work first identifies and measure two distinct kinds of distribution shifts that are ubiquitous in various datasets, and compares OoD generalization algorithms across two groups of benchmarks, revealing their strengths on one shift as well as limitations on the other shift.
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- Mixture of Cluster-Conditional LoRA Experts for Vision-Language Instruction Tuning128
A novel Mixture of Experts (MoE) architecture designed to activate task-customized model parameters based on instruction clusters is proposed, and a separate universal expert is further incorporated to improve generalization abilities of MoCLE for novel instructions.
- ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning115
This work considers semi-supervised continual learning (SSCL) that incrementally learns from partially labeled data, and proposes deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN), which continually passes the learned data distribution to the classifier.
- MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Control111
This work proposes MagicDriveV2, a novel approach that integrates the MVDiT block and spatial-temporal conditional encoding to enable multiview video generation and precise geometric control, and introduces an efficient method for obtaining contextual descriptions for videos to support diverse textual control.
- Automated Evaluation of Large Vision-Language Models on Self-Driving Corner Cases105
This work proposes CODA-LM, the very first benchmark for the automatic evaluation of LVLMs for self-driving corner cases, and builds CODA-VLM, a new driving LVLM surpassing all open-sourced counterparts on CODA-LM.
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- DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation89
DecAug is proposed, a novel decomposed feature representation and semantic augmentation approach for OoD generalization that outperforms other state-of-the-art methods on various OoD datasets, which is among the very few methods that can deal with different types of OoDgeneralization challenges.
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- MagicDrive3D: Controllable 3D Generation for Any-View Rendering in Street Scenes86
Experiments show that MagicDrive3D generates diverse, high-quality 3D driving scenes, supports any-view rendering, and enhances downstream tasks like BEV segmentation, demonstrating its potential for autonomous driving simulation and beyond.
- A random-effects Wiener degradation model based on accelerated failure time86
An inverse Gaussian (IG) distribution can be used to characterize the unit-specific heterogeneity in degradation paths, which overcomes the disadvantages of the traditional models and provides more flexibility in the degradation modeling using Wiener processes.
- EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions64
This work proposes the EMOVA (EMotionally Omni-present Voice Assistant), to enable Large Language Models with end-to-end speech abilities while maintaining the leading vision-language performance and surprisingly notices that omni-modal alignment can further enhance vision-language and speech abilities compared with the bi-modal aligned counterparts.
- MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving64
The Multi-instance Siamese Network (MultiSiam) remarkably improves generalization ability and achieves state-of-the-art transfer performance on autonomous driving benchmarks, while existing SSL counterparts like MoCo, MoCo-v2, and BYOL show significant performance drop.
- ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement62
ILLUME+ introduces a unified dual visual tokenizer, DualViTok, which preserves both fine-grained textures and text-aligned semantics while enabling a coarse-to-fine image representation strategy for multimodal understanding and generation.
- Mixed Autoencoder for Self-Supervised Visual Representation Learning62
The proposed Mixed Autoencoder (MixedAE) achieves the state-of-the-art transfer results among masked image modeling (MIM) augmentations on different downstream tasks with significant efficiency and is the very first work to consider mixing for MIM from the perspective of pretext task design.
- When is Acceleration Unnecessary in a Degradation Test?55
This study identifies situations where acceleration is unnecessary when some common stochastic process models are used, including the Wiener, gamma, and inverse Gaussian processes.
- DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception49
DetDiffusion, for the first time, harmonizes generative and perceptive models, tackling the challenges in generating effective data for perceptive models, and introduces perception-aware loss through segmentation, improving both quality and controllability.
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- Adversarial Robustness for Unsupervised Domain Adaptation47
This work proposes to leverage intermediate representations learned by robust ImageNet models to improve the robustness of UDA models by aligning the features of the UDA model with the robust features learned by ImageNet pre-trained models along with domain adaptation training.
- NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization47
Robust Neural Architecture Search for OoD generalization (NAS-OoD), which optimizes the architecture with respect to its performance on generated OoD data by gradient descent, and reduces the error rate by more than 70% compared with the state-of-the-art method.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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