236 citations · 862 across the 49 of their papers we have counts for
50 papers
Exploring the Robustness of Human Parsers Towards Common Corruptions
Sanyi Zhang, Xiaochun Cao, Rui Wang +2
Human parsing aims to segment each pixel of the human image with fine-grained semantic categories. However, current human parsers trained with clean data are easily confused by num…
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors
Weize Chen, Yusheng Su, Jingwei Zuo +13
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-…
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
Yujia Qin, Shihao Liang, Yining Ye +16
Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to…
Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning
Weize Chen, Xu Han, Yankai Lin +3
Parameter-efficient tuning methods (PETs) have achieved promising results in tuning large pre-trained language models (PLMs). By formalizing frozen PLMs and additional tunable para…
WebCPM: Interactive Web Search for Chinese Long-form Question Answering
Yujia Qin, Zihan Cai, Dian Jin +12
Long-form question answering (LFQA) aims at answering complex, open-ended questions with detailed, paragraph-length responses. The de facto paradigm of LFQA necessitates two proced…
GFDC: A Granule Fusion Density-Based Clustering with Evidential Reasoning
Mingjie Cai, Zhishan Wu, Qingguo Li +2
Currently, density-based clustering algorithms are widely applied because they can detect clusters with arbitrary shapes. However, they perform poorly in measuring global density,…
Recyclable Tuning for Continual Pre-training
Yujia Qin, Cheng Qian, Xu Han +6
Continual pre-training is the paradigm where pre-trained language models (PLMs) continually acquire fresh knowledge from growing data and gradually get upgraded. Before an upgraded…
Attacking Pre-trained Recommendation
Yiqing Wu, Ruobing Xie, Zhao Zhang +5
Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained…
Emergent Modularity in Pre-trained Transformers
Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin +7
This work examines the presence of modularity in pre-trained Transformers, a feature commonly found in human brains and thought to be vital for general intelligence. In analogy to…
Plug-and-Play Knowledge Injection for Pre-trained Language Models
Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin +8
Injecting external knowledge can improve the performance of pre-trained language models (PLMs) on various downstream NLP tasks. However, massive retraining is required to deploy ne…
Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition
Yansong Tang, Xingyu Liu, Xumin Yu +3
Rapid progress and superior performance have been achieved for skeleton-based action recognition recently. In this article, we investigate this problem under a cross-dataset settin…
Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning
Jianglin Lu, Jie Zhou, Yudong Chen +2
Thanks to the efficient retrieval speed and low storage consumption, learning to hash has been widely used in visual retrieval tasks. However, existing hashing methods assume that…
Similarity-Aware Fusion Network for 3D Semantic Segmentation
Linqing Zhao, Jiwen Lu, Jie Zhou
In this paper, we propose a similarity-aware fusion network (SAFNet) to adaptively fuse 2D images and 3D point clouds for 3D semantic segmentation. Existing fusion-based methods ac…
Global Filter Networks for Image Classification
Yongming Rao, Wenliang Zhao, Zheng Zhu +2
Recent advances in self-attention and pure multi-layer perceptrons (MLP) models for vision have shown great potential in achieving promising performance with fewer inductive biases…
Evaluating Modules in Graph Contrastive Learning
Ganqu Cui, Yufeng Du, Cheng Yang +5
The recent emergence of contrastive learning approaches facilitates the application on graph representation learning (GRL), introducing graph contrastive learning (GCL) into the li…
WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition
Zheng Zhu, Guan Huang, Jiankang Deng +8
In this paper, we contribute a new million-scale face benchmark containing noisy 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training da…
Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search
Cheng Ma, Jiwen Lu, Jie Zhou
As hashing becomes an increasingly appealing technique for large-scale image retrieval, multi-label hashing is also attracting more attention for the ability to exploit multi-level…
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images
Lei He, Jiwen Lu, Guanghui Wang +2
Depth estimation and semantic segmentation play essential roles in scene understanding. The state-of-the-art methods employ multi-task learning to simultaneously learn models for t…
Weakly-Supervised Saliency Detection via Salient Object Subitizing
Xiaoyang Zheng, Xin Tan, Jie Zhou +2
Salient object detection aims at detecting the most visually distinct objects and producing the corresponding masks. As the cost of pixel-level annotations is high, image tags are…
Robust Facial Landmark Detection by Multi-order Multi-constraint Deep Networks
Jun Wan, Zhihui Lai, Jing Li +2
Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance. However, most of the existing heatmap regression-based facia…
SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation
An Tao, Yueqi Duan, Yi Wei +2
Most existing point cloud instance and semantic segmentation methods rely heavily on strong supervision signals, which require point-level labels for every point in the scene. Howe…
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds
Yi Wei, Ziyi Wang, Yongming Rao +2
In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unorder…
Disentangle-based Continual Graph Representation Learning
Xiaoyu Kou, Yankai Lin, Shaobo Liu +3
Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However,…
GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation
Zhanwei Xu, Yukun Cao, Cheng Jin +5
Segmentation of infected areas in chest CT volumes is of great significance for further diagnosis and treatment of COVID-19 patients. Due to the complex shapes and varied appearanc…