21 citations · 41 across the 12 of their papers we have counts for
14 papers
STDec: Spatio-Temporal Stability Guided Decoding for dLLMs
Yuzhe Chen, Jiale Cao, Xuyang Liu +3
Diffusion Large Language Models (dLLMs) have achieved rapid progress, viewed as a promising alternative to the autoregressive paradigm. However, most dLLM decoders still adopt a gl…
Dual Contrastive Network for Few-Shot Remote Sensing Image Scene Classification
Zhong Ji, Liyuan Hou, Xuan Wang +2
Few-shot remote sensing image scene classification (FS-RSISC) aims at classifying remote sensing images with only a few labeled samples. The main challenges lie in small inter-clas…
SNNSIR: A Simple Spiking Neural Network for Stereo Image Restoration
Ronghua Xu, Jin Xie, Jing Nie +2
Spiking Neural Networks (SNNs), characterized by discrete binary activations, offer high computational efficiency and low energy consumption, making them well-suited for computatio…
CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation
Lin Sun, Jiale Cao, Jin Xie +2
Contrastive Language-Image Pre-training (CLIP) exhibits strong zero-shot classification ability on various image-level tasks, leading to the research to adapt CLIP for pixel-level…
iSeg: An Iterative Refinement-based Framework for Training-free Segmentation
Lin Sun, Jiale Cao, Jin Xie +2
Stable diffusion has demonstrated strong image synthesis ability to given text descriptions, suggesting it to contain strong semantic clue for grouping objects. The researchers hav…
Vision Foundation Model Driven Foreground-Aware Pseudo-LiDAR Generation for Monocular 3D Object Detection
Bonan Ding, Jin Xie, Jing Nie +2
Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations that can be processed by LiDAR-base…
Implicit and Explicit Language Guidance for Diffusion-based Visual Perception
Hefeng Wang, Jiale Cao, Jin Xie +2
Text-to-image diffusion models have shown powerful ability on conditional image synthesis. With large-scale vision-language pre-training, diffusion models are able to generate high…
CLIP-VIS: Adapting CLIP for Open-Vocabulary Video Instance Segmentation
Wenqi Zhu, Jiale Cao, Jin Xie +2
Open-vocabulary video instance segmentation strives to segment and track instances belonging to an open set of categories in a videos. The vision-language model Contrastive Languag…
Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects
Xiaoheng Jiang, Feng Yan, Yang Lu +6
Surface defect inspection plays an important role in the process of industrial manufacture and production. Though Convolutional Neural Network (CNN) based defect inspection methods…
SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation
Bin Xie, Jiale Cao, Jin Xie +2
Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. Most existing methods explore utilizing pre-train…
DFormer: Diffusion-guided Transformer for Universal Image Segmentation
Hefeng Wang, Jiale Cao, Rao Muhammad Anwer +3
This paper introduces an approach, named DFormer, for universal image segmentation. The proposed DFormer views universal image segmentation task as a denoising process using a diff…
Transformer-based stereo-aware 3D object detection from binocular images
Hanqing Sun, Yanwei Pang, Jiale Cao +2
Transformers have shown promising progress in various visual object detection tasks, including monocular 2D/3D detection and surround-view 3D detection. More importantly, the atten…
LEAPS: End-to-End One-Step Person Search With Learnable Proposals
Zhiqiang Dong, Jiale Cao, Rao Muhammad Anwer +3
We propose an end-to-end one-step person search approach with learnable proposals, named LEAPS. Given a set of sparse and learnable proposals, LEAPS employs a dynamic person search…
ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection
Aqi Gao, Yanwei Pang, Jing Nie +2
Fast stereo based 3D object detectors have made great progress recently. However, they lag far behind high-precision stereo based methods in accuracy. We argue that the main reason…