7 citations · 13 across the 9 of their papers we have counts for
9 papers · 1 filter
FineXtrol: Controllable Motion Generation via Fine-Grained Text
Keming Shen, Bizhu Wu, Junliang Chen +2
Recent works have sought to enhance the controllability and precision of text-driven motion generation. Some approaches leverage large language models (LLMs) to produce more detail…
DisFaceRep: Representation Disentanglement for Co-occurring Facial Components in Weakly Supervised Face Parsing
Xiaoqin Wang, Xianxu Hou, Meidan Ding +4
Face parsing aims to segment facial images into key components such as eyes, lips, and eyebrows. While existing methods rely on dense pixel-level annotations, such annotations are…
FaceBench: A Multi-View Multi-Level Facial Attribute VQA Dataset for Benchmarking Face Perception MLLMs
Xiaoqin Wang, Xusen Ma, Xianxu Hou +6
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in various tasks. However, effectively evaluating these MLLMs on face perception remains largely…
SLAMs: Semantic Learning based Activation Map for Weakly Supervised Semantic Segmentation
Junliang Chen, Xiaodong Zhao, Minmin Liu +1
Recent mainstream weakly-supervised semantic segmentation (WSSS) approaches mainly relies on image-level classification learning, which has limited representation capacity. In this…
SemFormer: Semantic Guided Activation Transformer for Weakly Supervised Semantic Segmentation
Junliang Chen, Xiaodong Zhao, Cheng Luo +1
Recent mainstream weakly supervised semantic segmentation (WSSS) approaches are mainly based on Class Activation Map (CAM) generated by a CNN (Convolutional Neural Network) based i…
Sample hardness based gradient loss for long-tailed cervical cell detection
Minmin Liu, Xuechen Li, Xiangbo Gao +3
Due to the difficulty of cancer samples collection and annotation, cervical cancer datasets usually exhibit a long-tailed data distribution. When training a detector to detect the…
Delving into the Scale Variance Problem in Object Detection
Junliang Chen, Xiaodong Zhao, Linlin Shen
Object detection has made substantial progress in the last decade, due to the capability of convolution in extracting local context of objects. However, the scales of objects are d…
Selective Multi-Scale Learning for Object Detection
Junliang Chen, Weizeng Lu, Linlin Shen
Pyramidal networks are standard methods for multi-scale object detection. Current researches on feature pyramid networks usually adopt layer connections to collect features from ce…
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation
Jinheng Xie, Jianfeng Xiang, Junliang Chen +3
While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), s…