4 papers
Redundant Queries in DETR-Based 3D Detection Methods: Unnecessary and Prunable
Lizhen Xu, Zehao Wu, Wenzhao Qiu +4
Query-based models are extensively used in 3D object detection tasks, with a wide range of pre-trained checkpoints readily available online. However, despite their popularity, thes…
Positional Prompt Tuning for Efficient 3D Representation Learning
Shaochen Zhang, Zekun Qi, Runpei Dong +2
We rethink the role of positional encoding in 3D representation learning and fine-tuning. We argue that using positional encoding in point Transformer-based methods serves to aggre…
Accelerate 3D Object Detection Models via Zero-Shot Attention Key Pruning
Lizhen Xu, Xiuxiu Bai, Xiaojun Jia +2
Query-based methods with dense features have demonstrated remarkable success in 3D object detection tasks. However, the computational demands of these models, particularly with lar…
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…