44 citations · 74 across the 21 of their papers we have counts for
21 papers
TALENT: Target-aware Efficient Tuning for Referring Image Segmentation
Shuo Jin, Siyue Yu, Bingfeng Zhang +3
Referring image segmentation aims to segment specific targets based on a natural text expression. Recently, parameter-efficient tuning (PET) has emerged as a promising paradigm. Ho…
TF-SSD: A Strong Pipeline via Synergic Mask Filter for Training-free Co-salient Object Detection
Zhijin He, Shuo Jin, Siyue Yu +4
Co-salient Object Detection (CoSOD) aims to segment salient objects that consistently appear across a group of related images. Despite the notable progress achieved by recent train…
SynthSeg-Agents: Multi-Agent Synthetic Data Generation for Zero-Shot Weakly Supervised Semantic Segmentation
Wangyu Wu, Zhenhong Chen, Xiaowei Huang +2
Weakly Supervised Semantic Segmentation (WSSS) with image level labels aims to produce pixel level predictions without requiring dense annotations. While recent approaches have lev…
Tag-Enriched Multi-Attention with Large Language Models for Cross-Domain Sequential Recommendation
Wangyu Wu, Xuhang Chen, Zhenhong Chen +5
Cross-Domain Sequential Recommendation (CDSR) plays a crucial role in modern consumer electronics and e-commerce platforms, where users interact with diverse services such as books…
Contrastive Prompt Clustering for Weakly Supervised Semantic Segmentation
Wangyu Wu, Zhenhong Chen, Xiaowen Ma +6
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained attention for its cost-effectiveness. Most existing methods emphasize inter-class separation, ofte…
LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation
Wangyu Wu, Zhenhong Chen, Wenqiao Zhang +5
Cross-Domain Sequential Recommendation (CDSR) predicts user behavior by leveraging historical interactions across multiple domains, focusing on modeling cross-domain preferences an…
Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendation
Wangyu Wu, Zhenhong Chen, Siqi Song +4
Cross-Domain Sequential Recommendation (CDSR) predicts user behavior by leveraging historical interactions across multiple domains, focusing on modeling cross-domain preferences th…
Image Fusion for Cross-Domain Sequential Recommendation
Wangyu Wu, Siqi Song, Xianglin Qiu +3
Cross-Domain Sequential Recommendation (CDSR) aims to predict future user interactions based on historical interactions across multiple domains. The key challenge in CDSR is effect…
Image Augmentation Agent for Weakly Supervised Semantic Segmentation
Wangyu Wu, Xianglin Qiu, Siqi Song +4
Weakly-supervised semantic segmentation (WSSS) has achieved remarkable progress using only image-level labels. However, most existing WSSS methods focus on designing new network st…
Prompt Categories Cluster for Weakly Supervised Semantic Segmentation
Wangyu Wu, Xianglin Qiu, Siqi Song +3
Weakly Supervised Semantic Segmentation (WSSS), which leverages image-level labels, has garnered significant attention due to its cost-effectiveness. The previous methods mainly st…
Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation
Wangyu Wu, Tianhong Dai, Zhenhong Chen +4
Weakly Supervised Semantic Segmentation (WSSS) using only image-level labels has gained significant attention due to its cost-effectiveness. The typical framework involves using im…
Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation
Bingfeng Zhang, Siyue Yu, Yunchao Wei +2
Weakly supervised semantic segmentation has witnessed great achievements with image-level labels. Several recent approaches use the CLIP model to generate pseudo labels for trainin…
Continual Segmentation with Disentangled Objectness Learning and Class Recognition
Yizheng Gong, Siyue Yu, Xiaoyang Wang +1
Most continual segmentation methods tackle the problem as a per-pixel classification task. However, such a paradigm is very challenging, and we find query-based segmenters with bui…
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation
Xiaoyang Wang, Huihui Bai, Limin Yu +2
Semi-supervised semantic segmentation allows model to mine effective supervision from unlabeled data to complement label-guided training. Recent research has primarily focused on c…
SFC: Shared Feature Calibration in Weakly Supervised Semantic Segmentation
Xinqiao Zhao, Feilong Tang, Xiaoyang Wang +1
Image-level weakly supervised semantic segmentation has received increasing attention due to its low annotation cost. Existing methods mainly rely on Class Activation Mapping (CAM)…
Top-K Pooling with Patch Contrastive Learning for Weakly-Supervised Semantic Segmentation
Wangyu Wu, Tianhong Dai, Xiaowei Huang +2
Weakly Supervised Semantic Segmentation (WSSS) using only image-level labels has gained significant attention due to cost-effectiveness. Recently, Vision Transformer (ViT) based me…
Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation
Wangyu Wu, Tianhong Dai, Xiaowei Huang +2
Weakly-supervised semantic segmentation (WSSS), which aims to train segmentation models solely using image-level labels, has achieved significant attention. Existing methods primar…
Trajectory Poisson multi-Bernoulli mixture filter for traffic monitoring using a drone
Ángel F. García-Fernández, Jimin Xiao
This paper proposes a multi-object tracking (MOT) algorithm for traffic monitoring using a drone equipped with optical and thermal cameras. Object detections on the images are obta…
Fully and Weakly Supervised Referring Expression Segmentation with End-to-End Learning
Hui Li, Mingjie Sun, Jimin Xiao +2
Referring Expression Segmentation (RES), which is aimed at localizing and segmenting the target according to the given language expression, has drawn increasing attention. Existing…
Towards Simple and Accurate Human Pose Estimation with Stair Network
Chenru Jiang, Kaizhu Huang, Shufei Zhang +3
In this paper, we focus on tackling the precise keypoint coordinates regression task. Most existing approaches adopt complicated networks with a large number of parameters, leading…
Progressive Sample Mining and Representation Learning for One-Shot Person Re-identification with Adversarial Samples
Hui Li, Jimin Xiao, Mingjie Sun +2
In this paper, we aim to tackle the one-shot person re-identification problem where only one image is labelled for each person, while other images are unlabelled. This task is chal…