110 citations · 584 across the 45 of their papers we have counts for
14 papers · 1 filter
Dynamic Token Pruning in Plain Vision Transformers for Semantic Segmentation
Quan Tang, Bowen Zhang, Jiajun Liu +2
Vision transformers have achieved leading performance on various visual tasks yet still suffer from high computational complexity. The situation deteriorates in dense prediction ta…
BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Biao Wu, Yutong Xie, Zeyu Zhang +6
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors. Identifying, localizing a…
Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection
Bingqing Zhang, Sen Wang, Yifan Liu +3
Current video object detection (VOD) models often encounter issues with over-aggregation due to redundant aggregation strategies, which perform feature aggregation on every frame.…
SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning
Muzhi Zhu, Hengtao Li, Hao Chen +5
Current closed-set instance segmentation models rely on pre-defined class labels for each mask during training and evaluation, largely limiting their ability to detect novel object…
Category Feature Transformer for Semantic Segmentation
Quan Tang, Chuanjian Liu, Fagui Liu +5
Aggregation of multi-stage features has been revealed to play a significant role in semantic segmentation. Unlike previous methods employing point-wise summation or concatenation f…
CTVIS: Consistent Training for Online Video Instance Segmentation
Kaining Ying, Qing Zhong, Weian Mao +7
The discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS). Instance embedding learning is direc…
A Dynamic Feature Interaction Framework for Multi-task Visual Perception
Yuling Xi, Hao Chen, Ning Wang +4
Multi-task visual perception has a wide range of applications in scene understanding such as autonomous driving. In this work, we devise an efficient unified framework to solve mul…
BPKD: Boundary Privileged Knowledge Distillation For Semantic Segmentation
Liyang Liu, Zihan Wang, Minh Hieu Phan +3
Current knowledge distillation approaches in semantic segmentation tend to adopt a holistic approach that treats all spatial locations equally. However, for dense prediction, stude…
SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers
Bowen Zhang, Liyang Liu, Minh Hieu Phan +3
This paper investigates the capability of plain Vision Transformers (ViTs) for semantic segmentation using the encoder-decoder framework and introduces \textbf{SegViTv2}. In this s…
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification
Jintao Rong, Hao Chen, Linlin Ou +3
The Contrastive Language-Image Pretraining (CLIP) model has been widely used in various downstream vision tasks. The few-shot learning paradigm has been widely adopted to augment i…
Segment Anything in High Quality
Lei Ke, Mingqiao Ye, Martin Danelljan +4
The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being…
Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning
Sen Zhao, Wei Wei, Yifan Liu +6
Conversational recommendation systems (CRS) aim to timely and proactively acquire user dynamic preferred attributes through conversations for item recommendation. In each turn of C…
Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction
Caiyuan Chu, Ya Li, Yifan Liu +4
Intent recognition is critical for task-oriented dialogue systems. However, for emerging domains and new services, it is difficult to accurately identify the key intent of a conver…
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
Xiangyu Li, Gongning Luo, Kuanquan Wang +31
Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT (NCCT) scans is significant in clinical practice. Existing hemorrhage segmentation methods usually ignores…