Search Results for author:
Found 8 papers, 6 papers with code
Scalable Language Model with Generalized Continual Learning
In this study, we introduce the Scalable Language Model (SLM) to overcome these limitations within a more challenging and generalized setting, representing a significant advancement toward practical applications for continual learning.
OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation
This exploration led to the creation of Omni-Adaptive 3D CNNs (OA-CNNs), a family of networks that integrates a lightweight module to greatly enhance the adaptivity of sparse CNNs at minimal computational cost.
Ranked #5 on
3D Semantic Segmentation
on ScanNet200
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding
To address this issue, we propose GroupContrast, a novel approach that combines segment grouping and semantic-aware contrastive learning.
LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model
While LISA effectively bridges the gap between segmentation and large language models to enable reasoning segmentation, it poses certain limitations: unable to distinguish different instances of the target region, and constrained by the pre-defined textual response formats.
Prompt Highlighter: Interactive Control for Multi-Modal LLMs
Without tuning on LLaVA-v1. 5, our method secured 70. 7 in the MMBench test and 1552. 5 in MME-perception.
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
In contrast, such privilege has not yet fully benefited 3D deep learning, mainly due to the limited availability of large-scale 3D datasets.
Ranked #3 on
3D Semantic Segmentation
on ScanNet200
(using extra training data)
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract
We hope our work can benefit broader industrial applications where novel classes with limited annotations are required to be decently identified.
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations.
Ranked #6 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
