93 citations · 133 across the 8 of their papers we have counts for
8 papers
OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation
Bohao Peng, Xiaoyang Wu, Li Jiang +4
The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models, especia…
E^2-LLM: Efficient and Extreme Length Extension of Large Language Models
Jiaheng Liu, Zhiqi Bai, Yuanxing Zhang +11
Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. Existing long-context extension methods usually…
Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
Tianhe Ren, Shilong Liu, Ailing Zeng +14
We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM). This integration enables the detection and seg…
Data Pruning via Moving-one-Sample-out
Haoru Tan, Sitong Wu, Fei Du +4
In this paper, we propose a novel data-pruning approach called moving-one-sample-out (MoSo), which aims to identify and remove the least informative samples from the training set.…
Mask-Attention-Free Transformer for 3D Instance Segmentation
Xin Lai, Yuhui Yuan, Ruihang Chu +3
Recently, transformer-based methods have dominated 3D instance segmentation, where mask attention is commonly involved. Specifically, object queries are guided by the initial insta…
FocalFormer3D : Focusing on Hard Instance for 3D Object Detection
Yilun Chen, Zhiding Yu, Yukang Chen +4
False negatives (FN) in 3D object detection, {\em e.g.}, missing predictions of pedestrians, vehicles, or other obstacles, can lead to potentially dangerous situations in autonomou…