no code implementations • 2 Sep 2024 • Long Li, Nian Liu, Dingwen Zhang, Zhongyu Li, Salman Khan, Rao Anwer, Hisham Cholakkal, Junwei Han, Fahad Shahbaz Khan
They directly rely on raw associations which are not reliable in complex scenarios, and their image feature optimization approach is not explicit for inter-image association modeling.
no code implementations • 30 Jul 2024 • Rui Cheng, Yuze Yang, Wenxia Liu, Nian Liu, Zhaoyu Wang
This paper proposes an input convex neural network (ICNN)-Assisted optimal power flow (OPF) in distribution networks.
no code implementations • 18 Jul 2024 • Jin Zhang, Ruiheng Zhang, Yanjiao Shi, Zhe Cao, Nian Liu, Fahad Shahbaz Khan
Existing Camouflaged Object Detection (COD) methods rely heavily on large-scale pixel-annotated training sets, which are both time-consuming and labor-intensive.
1 code implementation • 22 May 2024 • Nian Liu, Xiaoxin He, Thomas Laurent, Francesco Di Giovanni, Michael M. Bronstein, Xavier Bresson
Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions; selecting spectral bases for signal transformation and parameterizing the kernel for frequency analysis.
1 code implementation • 22 May 2024 • Dingwen Zhang, Hao Li, Diqi He, Nian Liu, Lechao Cheng, Jingdong Wang, Junwei Han
Experimental evaluations conducted on MS COCO, Cityscapes, and CTW1500 datasets indicate that the QEIS models' performance can be significantly improved when pre-trained with our method.
no code implementations • 22 May 2024 • Yuan Sui, Yufei He, Nian Liu, Xiaoxin He, Kun Wang, Bryan Hooi
A distinctive feature of our approach is its blend of natural language planning with beam search to optimize the selection of reasoning paths.
no code implementations • 11 May 2024 • Renyou Xie, Xin Yin, Chaojie Li, Guo Chen, Nian Liu, Bo Zhao, ZhaoYang Dong
Distribution system state estimation (DSSE) plays a crucial role in the real-time monitoring, control, and operation of distribution networks.
1 code implementation • 8 May 2024 • Nian Liu, Shen Fan, Ting Bai, Peng Wang, Mingwei Sun, Yanhu Mo, Xiaoxiao Xu, Hong Liu, Chuan Shi
In this paper, we propose a novel social recommendation method called LSIR (\textbf{L}earning \textbf{S}ocial Graph for \textbf{I}nactive User \textbf{R}ecommendation) that learns an optimal social graph structure for social recommendation, especially for inactive users.
no code implementations • CVPR 2024 • Jieming Cui, Tengyu Liu, Nian Liu, Yaodong Yang, Yixin Zhu, Siyuan Huang
Traditional approaches in physics-based motion generation, centered around imitation learning and reward shaping, often struggle to adapt to new scenarios.
1 code implementation • NeurIPS 2023 • Donglin Xia, Xiao Wang, Nian Liu, Chuan Shi
To address this challenge, we propose the Cluster Information Transfer (CIT) mechanism (Code available at https://github. com/BUPT-GAMMA/CITGNN), which can learn invariant representations for GNNs, thereby improving their generalization ability to various and unknown test graphs with structure shift.
1 code implementation • 21 Feb 2024 • Binglu Wang, Chenxi Guo, Yang Jin, Haisheng Xia, Nian Liu
Gaze object prediction aims to predict the location and category of the object that is watched by a human.
no code implementations • 7 Feb 2024 • Yuanfang Zhang, Junxuan Li, Kaiqing Luo, Yiying Yang, Jiayi Han, Nian Liu, Denghui Qin, Peng Han, Chengpei Xu
Extensive experiments demonstrate that by leveraging V2V communication, the SSC performance can be increased by 8. 3% on geometric metric IoU and 6. 0% mIOU.
1 code implementation • 19 Jan 2024 • Siyuan Qi, Shuo Chen, Yexin Li, Xiangyu Kong, Junqi Wang, Bangcheng Yang, Pring Wong, Yifan Zhong, Xiaoyuan Zhang, Zhaowei Zhang, Nian Liu, Wei Wang, Yaodong Yang, Song-Chun Zhu
Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning.
1 code implementation • CVPR 2024 • Ziyang Luo, Nian Liu, Wangbo Zhao, Xuguang Yang, Dingwen Zhang, Deng-Ping Fan, Fahad Khan, Junwei Han
Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks.
no code implementations • CVPR 2024 • Hao Li, Dingwen Zhang, Yalun Dai, Nian Liu, Lechao Cheng, Jingfeng Li, Jingdong Wang, Junwei Han
Applying NeRF to downstream perception tasks for scene understanding and representation is becoming increasingly popular.
no code implementations • 18 Oct 2023 • Nian Liu, Ziyang Luo, Ni Zhang, Junwei Han
Our previous work, the Visual Saliency Transformer (VST), addressed this constraint from a transformer-based sequence-to-sequence perspective, to unify RGB and RGB-D SOD.
1 code implementation • NeurIPS 2023 • Yue Yu, Xiao Wang, Mengmei Zhang, Nian Liu, Chuan Shi
To this end, we propose the PrOvable Training (POT) for GCL, which regularizes the training of GCL to encode node embeddings that follows the GCL principle better.
1 code implementation • ICCV 2023 • Nian Liu, Kepan Nan, Wangbo Zhao, Yuanwei Liu, Xiwen Yao, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Junwei Han, Fahad Shahbaz Khan
We decompose the query video information into a clip prototype and a memory prototype for capturing local and long-term internal temporal guidance, respectively.
1 code implementation • ICCV 2023 • Chun-Mei Feng, Kai Yu, Nian Liu, Xinxing Xu, Salman Khan, WangMeng Zuo
However, the performance of the global model is often hampered by non-i. i. d.
1 code implementation • 16 Jul 2023 • Jialun Pei, Tao Jiang, He Tang, Nian Liu, Yueming Jin, Deng-Ping Fan, Pheng-Ann Heng
We propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet.
2 code implementations • 26 May 2023 • Zhaowei Zhang, Ceyao Zhang, Nian Liu, Siyuan Qi, Ziqi Rong, Song-Chun Zhu, Shuguang Cui, Yaodong Yang
We conduct evaluations with new auto-metric \textit{value rationality} to represent the ability of LLMs to align with specific values.
1 code implementation • CVPR 2023 • Long Li, Junwei Han, Ni Zhang, Nian Liu, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Fahad Shahbaz Khan
Then, we use two types of pre-defined tokens to mine co-saliency and background information via our proposed contrast-induced pixel-to-token correlation and co-saliency token-to-token correlation modules.
Ranked #1 on
Co-Salient Object Detection
on CoSal2015
no code implementations • 24 Apr 2023 • Nian Liu, Xiao Wang, Hui Han, Chuan Shi
Specifically, two views of a HIN (network schema and meta-path views) are proposed to learn node embeddings, so as to capture both of local and high-order structures simultaneously.
no code implementations • 3 Apr 2023 • Xinwei Liu, Kiran Raja, Renfang Wang, Hong Qiu, Hucheng Wu, Dechao Sun, Qiguang Zheng, Nian Liu, Xiaoxia Wang, Gehang Huang, Raghavendra Ramachandra, Christoph Busch
Further, existing databases for latent fingerprint recognition do not have a large number of unique subjects/fingerprint instances or do not provide ground truth/reference fingerprint images to conduct a cross-comparison against the latent.
no code implementations • CVPR 2023 • Hao Li, Dingwen Zhang, Nian Liu, Lechao Cheng, Yalun Dai, Chao Zhang, Xinggang Wang, Junwei Han
Inspired by the recent success of the Prompting technique, we introduce a new pre-training method that boosts QEIS models by giving Saliency Prompt for queries/kernels.
no code implementations • 4 Nov 2022 • Haodi Jiang, Qin Li, Zhihang Hu, Nian Liu, Yasser Abduallah, Ju Jing, Genwei Zhang, Yan Xu, Wynne Hsu, Jason T. L. Wang, Haimin Wang
We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data.
1 code implementation • 13 Oct 2022 • Yuanwei Liu, Nian Liu, Xiwen Yao, Junwei Han
To solve this problem, we are the first to introduce an intermediate prototype for mining both deterministic category information from the support and adaptive category knowledge from the query.
Ranked #33 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
1 code implementation • 5 Oct 2022 • Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, Jian Pei
Then we theoretically prove that GCL is able to learn the invariance information by contrastive invariance theorem, together with our GAME rule, for the first time, we uncover that the learned representations by GCL essentially encode the low-frequency information, which explains why GCL works.
2 code implementations • 20 May 2022 • Le Yang, Junwei Han, Tao Zhao, Nian Liu, Dingwen Zhang
To tackle this issue, we make an early effort to study temporal action localization from the perspective of multi-modality feature learning, based on the observation that different actions exhibit specific preferences to appearance or motion modality.
1 code implementation • CVPR 2022 • Yuanwei Liu, Nian Liu, Qinglong Cao, Xiwen Yao, Junwei Han, Ling Shao
Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO-free target object segmentation result.
no code implementations • 19 Jan 2022 • Shaohua Fan, Xiao Wang, Chuan Shi, Kun Kuang, Nian Liu, Bai Wang
Then to remove the bias in GNN estimation, we propose a novel Debiased Graph Neural Networks (DGNN) with a differentiated decorrelation regularizer.
2 code implementations • 14 Jan 2022 • Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo, Chuan Shi
Furthermore, we maintain the performance of estimated views and the final view and reduce the mutual information of every two views.
1 code implementation • 17 Dec 2021 • Guangyu Guo, Dingwen Zhang, Longfei Han, Nian Liu, Ming-Ming Cheng, Junwei Han
Then, a Teacher-Assistant-Student (TAS) framework is further established to disentangle pixel distillation into the model compression stage and input compression stage, which significantly reduces the overall complexity of pixel distillation and the difficulty of distilling intermediate knowledge.
1 code implementation • 2 Oct 2021 • Nian Liu, Wangbo Zhao, Dingwen Zhang, Junwei Han, Ling Shao
On the other hand, instead of processing the twokinds of data separately, we build a novel dual graph modelto guide the focal stack fusion process using all-focus pat-terns.
1 code implementation • ICCV 2021 • Ni Zhang, Junwei Han, Nian Liu, Ling Shao
In this paper, we propose a novel consensus-aware dynamic convolution model to explicitly and effectively perform the "summarize and search" process.
Ranked #3 on
Co-Salient Object Detection
on CoSal2015
no code implementations • 8 Jul 2021 • Nian Liu, Long Li, Wangbo Zhao, Junwei Han, Ling Shao
Conventional salient object detection models cannot differentiate the importance of different salient objects.
2 code implementations • 26 May 2021 • Yujia Sun, Geng Chen, Tao Zhou, Yi Zhang, Nian Liu
Camouflaged object detection (COD) is a challenging task due to the low boundary contrast between the object and its surroundings.
Ranked #6 on
Camouflaged Object Segmentation
on PCOD_1200
3 code implementations • 19 May 2021 • Xiao Wang, Nian Liu, Hui Han, Chuan Shi
Then the cross-view contrastive learning, as well as a view mask mechanism, is proposed, which is able to extract the positive and negative embeddings from two views.
2 code implementations • ICCV 2021 • Nian Liu, Ni Zhang, Kaiyuan Wan, Ling Shao, Junwei Han
We also develop a token-based multi-task decoder to simultaneously perform saliency and boundary detection by introducing task-related tokens and a novel patch-task-attention mechanism.
Ranked #1 on
RGB-D Salient Object Detection
on NJUD
no code implementations • 15 Apr 2021 • Yiding Zhang, Xiao Wang, Chuan Shi, Nian Liu, Guojie Song
We also find that the performance of some hyperbolic GCNs can be improved by simply replacing the graph operations with those we defined in this paper.
1 code implementation • CVPR 2021 • Wangbo Zhao, Jing Zhang, Long Li, Nick Barnes, Nian Liu, Junwei Han
Significant performance improvement has been achieved for fully-supervised video salient object detection with the pixel-wise labeled training datasets, which are time-consuming and expensive to obtain.
3 code implementations • 19 Jan 2021 • Mingchen Zhuge, Deng-Ping Fan, Nian Liu, Dingwen Zhang, Dong Xu, Ling Shao
We define the concept of integrity at both a micro and macro level.
no code implementations • ICCV 2021 • Nian Liu, Wangbo Zhao, Dingwen Zhang, Junwei Han, Ling Shao
In this paper, we model the information fusion within focal stack via graph networks.
1 code implementation • 12 Oct 2020 • Nian Liu, Ni Zhang, Ling Shao, Junwei Han
Early fusion and the result fusion schemes fuse RGB and depth information at the input and output stages, respectively, hence incur the problem of distribution gap or information loss.
1 code implementation • CVPR 2020 • Nian Liu, Ni Zhang, Junwei Han
Considering the reliability of the other modality's attention, we further propose a selection attention to weight the newly added attention term.
Ranked #20 on
RGB-D Salient Object Detection
on NJU2K
1 code implementation • ECCV 2018 • Ji Zhu, Hua Yang, Nian Liu, Minyoung Kim, Wenjun Zhang, Ming-Hsuan Yang
In this paper, we propose an online Multi-Object Tracking (MOT) approach which integrates the merits of single object tracking and data association methods in a unified framework to handle noisy detections and frequent interactions between targets.
Ranked #5 on
Online Multi-Object Tracking
on MOT16
2 code implementations • 15 Dec 2018 • Nian Liu, Junwei Han, Ming-Hsuan Yang
We propose three specific formulations of the PiCANet via embedding the pixel-wise contextual attention mechanism into the pooling and convolution operations with attending to global or local contexts.
2 code implementations • CVPR 2018 • Nian Liu, Junwei Han, Ming-Hsuan Yang
We formulate the proposed PiCANet in both global and local forms to attend to global and local contexts, respectively.
Ranked #7 on
RGB Salient Object Detection
on SOC
2 code implementations • 6 Oct 2016 • Nian Liu, Junwei Han
Furthermore, the proposed DSCLSTM model can significantly boost the saliency detection performance by incorporating both global spatial interconnections and scene context modulation, which may uncover novel inspirations for studies on them in computational saliency models.
no code implementations • CVPR 2016 • Nian Liu, Junwei Han
Then a novel hierarchical recurrent convolutional neural network (HRCNN) is adopted to further hierarchically and progressively refine the details of saliency maps step by step via integrating local context information.
Ranked #18 on
RGB Salient Object Detection
on DUTS-TE
(max F-measure metric)
no code implementations • CVPR 2015 • Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu
It is believed that eye movements in free-viewing of natural scenes are directed by both bottom-up visual saliency and top-down visual factors.