1.1k citations · 1.2k across the 21 of their papers we have counts for
23 papers
What to Preserve in Recursive Computation: A Local Predictive Sufficiency Principle
Peilin Wang, Feng Shiyang, Hongfu Gao +4
Recursive computation repeatedly compresses or reuses intermediate states, creating a simple tension: information that must remain useful across longer recursive paths is also expo…
LSNet: See Large, Focus Small
Ao Wang, Hui Chen, Zijia Lin +2
Vision network designs, including Convolutional Neural Networks and Vision Transformers, have significantly advanced the field of computer vision. Yet, their complex computations p…
YOLOE: Real-Time Seeing Anything
Ao Wang, Lihao Liu, Hui Chen +3
Object detection and segmentation are widely employed in computer vision applications, yet conventional models like YOLO series, while efficient and accurate, are limited by predef…
YOLO-UniOW: Efficient Universal Open-World Object Detection
Lihao Liu, Juexiao Feng, Hui Chen +4
Traditional object detection models are constrained by the limitations of closed-set datasets, detecting only categories encountered during training. While multimodal models have e…
Promptable Anomaly Segmentation with SAM Through Self-Perception Tuning
Hui-Yue Yang, Hui Chen, Ao Wang +7
Segment Anything Model (SAM) has made great progress in anomaly segmentation tasks due to its impressive generalization ability. However, existing methods that directly apply SAM t…
[CLS] Token Tells Everything Needed for Training-free Efficient MLLMs
Ao Wang, Fengyuan Sun, Hui Chen +3
Multimodal Large Language Models (MLLMs) have recently demonstrated strong performance across a wide range of vision-language tasks, garnering significant attention in the computer…
PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient Generation
Ao Wang, Hui Chen, Jiaxin Li +6
Recently, large vision-language models (LVLMs) have rapidly gained popularity for their strong generation and reasoning capabilities given diverse multimodal inputs. However, these…
Context Enhancement with Reconstruction as Sequence for Unified Unsupervised Anomaly Detection
Hui-Yue Yang, Hui Chen, Lihao Liu +5
Unsupervised anomaly detection (AD) aims to train robust detection models using only normal samples, while can generalize well to unseen anomalies. Recent research focuses on a uni…
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence
Mengyao Lyu, Tianxiang Hao, Xinhao Xu +4
Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Acti…
YOLOv10: Real-Time End-to-End Object Detection
Ao Wang, Hui Chen, Lihao Liu +4
Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detec…
PYRA: Parallel Yielding Re-Activation for Training-Inference Efficient Task Adaptation
Yizhe Xiong, Hui Chen, Tianxiang Hao +6
Recently, the scale of transformers has grown rapidly, which introduces considerable challenges in terms of training overhead and inference efficiency in the scope of task adaptati…
One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications
Mengyao Lyu, Yuhong Yang, Haiwen Hong +6
The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors. Existing concept erasing…
Towards Efficient Vision-Language Tuning: More Information Density, More Generalizability
Tianxiang Hao, Mengyao Lyu, Hui Chen +4
With the advancement of large pre-trained vision-language models, effectively transferring the knowledge embedded within these foundational models to downstream tasks has become a…
RepViT-SAM: Towards Real-Time Segmenting Anything
Ao Wang, Hui Chen, Zijia Lin +2
Segment Anything Model (SAM) has shown impressive zero-shot transfer performance for various computer vision tasks recently. However, its heavy computation costs remain daunting fo…
MiLe Loss: a New Entropy-Weighed Loss for Mitigating the Bias of Learning Difficulties in Large Language Models
Zhenpeng Su, Xing Wu, Xue Bai +5
Generative language models are usually pretrained on large text corpus via predicting the next token (i.e., sub-word/word/phrase) given the previous ones. Recent works have demonst…
RepViT: Revisiting Mobile CNN From ViT Perspective
Ao Wang, Hui Chen, Zijia Lin +2
Recently, lightweight Vision Transformers (ViTs) demonstrate superior performance and lower latency, compared with lightweight Convolutional Neural Networks (CNNs), on resource-con…
Consolidator: Mergeable Adapter with Grouped Connections for Visual Adaptation
Tianxiang Hao, Hui Chen, Yuchen Guo +1
Recently, transformers have shown strong ability as visual feature extractors, surpassing traditional convolution-based models in various scenarios. However, the success of vision…
Box-Level Active Detection
Mengyao Lyu, Jundong Zhou, Hui Chen +7
Active learning selects informative samples for annotation within budget, which has proven efficient recently on object detection. However, the widely used active detection benchma…
Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels
Zixuan Ding, Ao Wang, Hui Chen +5
Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, \ie, CLIP,…
Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection
Fan Yang, Xinhao Xu, Hui Chen +4
The ground plane prior is a very informative geometry clue in monocular 3D object detection (M3OD). However, it has been neglected by most mainstream methods. In this paper, we ide…
IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
Hui Chen, Guiguang Ding, Xudong Liu +3
Enabling bi-directional retrieval of images and texts is important for understanding the correspondence between vision and language. Existing methods leverage the attention mechani…
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
Qianhui Wu, Zijia Lin, Guoxin Wang +4
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER). While all existing metho…
GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition
Hui Chen, Zijia Lin, Guiguang Ding +3
The dominant approaches for named entity recognition (NER) mostly adopt complex recurrent neural networks (RNN), e.g., long-short-term-memory (LSTM). However, RNNs are limited by t…