activity
20232025
most citedFully Sparse Fusion for 3D Object Detection

49 citations · 110 across the 17 of their papers we have counts for

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14 papers · 1 filter

cs.CV2024

Monocular Occupancy Prediction for Scalable Indoor Scenes

Hongxiao Yu, Yuqi Wang, Yuntao Chen +1

Camera-based 3D occupancy prediction has recently garnered increasing attention in outdoor driving scenes. However, research in indoor scenes remains relatively unexplored. The cor…

cs.CV2024

Enhancing End-to-End Autonomous Driving with Latent World Model

Yingyan Li, Lue Fan, Jiawei He +4

In autonomous driving, end-to-end planners directly utilize raw sensor data, enabling them to extract richer scene features and reduce information loss compared to traditional plan…

cs.CV2024★ 1 cited

Continual Forgetting for Pre-trained Vision Models

Hongbo Zhao, Bolin Ni, Haochen Wang +6

For privacy and security concerns, the need to erase unwanted information from pre-trained vision models is becoming evident nowadays. In real-world scenarios, erasure requests ori…

cs.CV2024★ 9 cited

Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications

Yuwen Xiong, Zhiqi Li, Yuntao Chen +10

We introduce Deformable Convolution v4 (DCNv4), a highly efficient and effective operator designed for a broad spectrum of vision applications. DCNv4 addresses the limitations of i…

cs.CV2024★ 6 cited

MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer

Changyao Tian, Xizhou Zhu, Yuwen Xiong +10

Developing generative models for interleaved image-text data has both research and practical value. It requires models to understand the interleaved sequences and subsequently gene…

cs.CV2023★ 2 cited

Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving

Yuqi Wang, Jiawei He, Lue Fan +3

In autonomous driving, predicting future events in advance and evaluating the foreseeable risks empowers autonomous vehicles to better plan their actions, enhancing safety and effi…