290 citations · 944 across the 42 of their papers we have counts for
36 papers · 1 filter
Impostor: An Agent-Curated Benchmark for Realistic AIGC Manipulation Localization
Zhenliang Li, Yutao Hu, Qixiong Wang +5
Recent advances in generative image editing have improved the realism and controllability of localized image manipulation, raising new challenges for image manipulation detection a…
Beyond One-to-One: Rethinking the Referring Image Segmentation
Yutao Hu, Qixiong Wang, Wenqi Shao +4
Referring image segmentation aims to segment the target object referred by a natural language expression. However, previous methods rely on the strong assumption that one sentence…
Dense Affinity Matching for Few-Shot Segmentation
Hao Chen, Yonghan Dong, Zheming Lu +4
Few-Shot Segmentation (FSS) aims to segment the novel class images with a few annotated samples. In this paper, we propose a dense affinity matching (DAM) framework to exploit the…
Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler
Shaohui Lin, Wenxuan Huang, Jiao Xie +5
Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs, which can be effectively applied to edge devices and cloud services. In this pape…
SegGPT Meets Co-Saliency Scene
Yi Liu, Shoukun Xu, Dingwen Zhang +1
Co-salient object detection targets at detecting co-existed salient objects among a group of images. Recently, a generalist model for segmenting everything in context, called SegGP…
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,…
Confidence-guided Centroids for Unsupervised Person Re-Identification
Yunqi Miao, Jiankang Deng, Guiguang Ding +1
Unsupervised person re-identification (ReID) aims to train a feature extractor for identity retrieval without exploiting identity labels. Due to the blind trust in imperfect cluste…
Physically-Based Face Rendering for NIR-VIS Face Recognition
Yunqi Miao, Alexandros Lattas, Jiankang Deng +2
Near infrared (NIR) to Visible (VIS) face matching is challenging due to the significant domain gaps as well as a lack of sufficient data for cross-modality model training. To over…
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…
LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers
Zhuoxu Huang, Zhiyou Zhao, Banghuai Li +1
Transformer with its underlying attention mechanism and the ability to capture long-range dependencies makes it become a natural choice for unordered point cloud data. However, sep…
MAPLE: Masked Pseudo-Labeling autoEncoder for Semi-supervised Point Cloud Action Recognition
Xiaodong Chen, Wu Liu, Xinchen Liu +3
Recognizing human actions from point cloud videos has attracted tremendous attention from both academia and industry due to its wide applications like automatic driving, robotics,…
Boosting Video-Text Retrieval with Explicit High-Level Semantics
Haoran Wang, Di Xu, Dongliang He +4
Video-text retrieval (VTR) is an attractive yet challenging task for multi-modal understanding, which aims to search for relevant video (text) given a query (video). Existing metho…
Temporal Saliency Query Network for Efficient Video Recognition
Boyang Xia, Zhihao Wang, Wenhao Wu +2
Efficient video recognition is a hot-spot research topic with the explosive growth of multimedia data on the Internet and mobile devices. Most existing methods select the salient f…
Semi-supervised Object Detection via Virtual Category Learning
Changrui Chen, Kurt Debattista, Jungong Han
Due to the costliness of labelled data in real-world applications, semi-supervised object detectors, underpinned by pseudo labelling, are appealing. However, handling confusing sam…
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
Xiaohan Ding, Xiangyu Zhang, Yizhuang Zhou +3
We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a…
Hybrid Routing Transformer for Zero-Shot Learning
De Cheng, Gerong Wang, Bo Wang +3
Zero-shot learning (ZSL) aims to learn models that can recognize unseen image semantics based on the training of data with seen semantics. Recent studies either leverage the global…
On Exploring Pose Estimation as an Auxiliary Learning Task for Visible-Infrared Person Re-identification
Yunqi Miao, Nianchang Huang, Xiao Ma +2
Visible-infrared person re-identification (VI-ReID) has been challenging due to the existence of large discrepancies between visible and infrared modalities. Most pioneering approa…
RepMLPNet: Hierarchical Vision MLP with Re-parameterized Locality
Xiaohan Ding, Honghao Chen, Xiangyu Zhang +2
Compared to convolutional layers, fully-connected (FC) layers are better at modeling the long-range dependencies but worse at capturing the local patterns, hence usually less favor…
LODE: Deep Local Deblurring and A New Benchmark
Zerun Wang, Liuyu Xiang, Fan Yang +6
While recent deep deblurring algorithms have achieved remarkable progress, most existing methods focus on the global deblurring problem, where the image blur mostly arises from sev…
Information Symmetry Matters: A Modal-Alternating Propagation Network for Few-Shot Learning
Zhong Ji, Zhishen Hou, Xiyao Liu +2
Semantic information provides intra-class consistency and inter-class discriminability beyond visual concepts, which has been employed in Few-Shot Learning (FSL) to achieve further…
Manipulating Identical Filter Redundancy for Efficient Pruning on Deep and Complicated CNN
Xiaohan Ding, Tianxiang Hao, Jungong Han +2
The existence of redundancy in Convolutional Neural Networks (CNNs) enables us to remove some filters/channels with acceptable performance drops. However, the training objective of…
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
Xiaohan Ding, Chunlong Xia, Xiangyu Zhang +3
We propose RepMLP, a multi-layer-perceptron-style neural network building block for image recognition, which is composed of a series of fully-connected (FC) layers. Compared to con…
Exploring Modality-shared Appearance Features and Modality-invariant Relation Features for Cross-modality Person Re-Identification
Nianchang Huang, Jianan Liu, Qiang Zhang +1
Most existing cross-modality person re-identification works rely on discriminative modality-shared features for reducing cross-modality variations and intra-modality variations. De…
Few-Cost Salient Object Detection with Adversarial-Paced Learning
Dingwen Zhang, Haibin Tian, Jungong Han
Detecting and segmenting salient objects from given image scenes has received great attention in recent years. A fundamental challenge in training the existing deep saliency detect…