6 papers · 1 filter
Refining CLIP's Spatial Awareness: A Visual-Centric Perspective
Congpei Qiu, Yanhao Wu, Wei Ke +2
Contrastive Language-Image Pre-training (CLIP) excels in global alignment with language but exhibits limited sensitivity to spatial information, leading to strong performance in ze…
Generating Multimodal Driving Scenes via Next-Scene Prediction
Yanhao Wu, Haoyang Zhang, Tianwei Lin +6
Generative models in Autonomous Driving (AD) enable diverse scene creation, yet existing methods fall short by only capturing a limited range of modalities, restricting the capabil…
Monte Carlo Diffusion for Generalizable Learning-Based RANSAC
Jiale Wang, Chen Zhao, Wei Ke +1
Random Sample Consensus (RANSAC) is a fundamental approach for robustly estimating parametric models from noisy data. Existing learning-based RANSAC methods utilize deep learning t…
Coherent and Multi-modality Image Inpainting via Latent Space Optimization
Lingzhi Pan, Tong Zhang, Bingyuan Chen +4
With the advancements in denoising diffusion probabilistic models (DDPMs), image inpainting has significantly evolved from merely filling information based on nearby regions to gen…
Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange
Yanhao Wu, Tong Zhang, Wei Ke +3
In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely…
Language-Driven Visual Consensus for Zero-Shot Semantic Segmentation
Zicheng Zhang, Tong Zhang, Yi Zhu +4
The pre-trained vision-language model, exemplified by CLIP, advances zero-shot semantic segmentation by aligning visual features with class embeddings through a transformer decoder…