37 citations · 89 across the 16 of their papers we have counts for
16 papers
AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation
Chaofan Ma, Yuhuan Yang, Chen Ju +3
Open-vocabulary semantic segmentation is a challenging task that requires segmenting novel object categories at inference time. Recent studies have explored vision-language pre-tra…
Audio-aware Query-enhanced Transformer for Audio-Visual Segmentation
Jinxiang Liu, Chen Ju, Chaofan Ma +3
The goal of the audio-visual segmentation (AVS) task is to segment the sounding objects in the video frames using audio cues. However, current fusion-based methods have the perform…
Multi-Modal Prototypes for Open-World Semantic Segmentation
Yuhuan Yang, Chaofan Ma, Chen Ju +4
In semantic segmentation, generalizing a visual system to both seen categories and novel categories at inference time has always been practically valuable yet challenging. To enabl…
Enhanced Multimodal Representation Learning with Cross-modal KD
Mengxi Chen, Linyu Xing, Yu Wang +1
This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Di…
Annotation-free Audio-Visual Segmentation
Jinxiang Liu, Yu Wang, Chen Ju +3
The objective of Audio-Visual Segmentation (AVS) is to localise the sounding objects within visual scenes by accurately predicting pixel-wise segmentation masks. To tackle the task…
Controllable Mesh Generation Through Sparse Latent Point Diffusion Models
Zhaoyang Lyu, Jinyi Wang, Yuwei An +3
Mesh generation is of great value in various applications involving computer graphics and virtual content, yet designing generative models for meshes is challenging due to their ir…
On Transforming Reinforcement Learning by Transformer: The Development Trajectory
Shengchao Hu, Li Shen, Ya Zhang +2
Transformer, originally devised for natural language processing, has also attested significant success in computer vision. Thanks to its super expressive power, researchers are inv…
Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models
Chaofan Ma, Yuhuan Yang, Yanfeng Wang +2
When trained at a sufficient scale, self-supervised learning has exhibited a notable ability to solve a wide range of visual or language understanding tasks. In this paper, we inve…
Registration based Few-Shot Anomaly Detection
Chaoqin Huang, Haoyan Guan, Aofan Jiang +3
This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided…
Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation
Jinxiang Liu, Chen Ju, Weidi Xie +1
We present a simple yet effective self-supervised framework for audio-visual representation learning, to localize the sound source in videos. To understand what enables to learn us…
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization
Chaoqin Huang, Qinwei Xu, Yanfeng Wang +2
Recently, anomaly detection and localization in multimedia data have received significant attention among the machine learning community. In real-world applications such as medical…
Prompting Visual-Language Models for Efficient Video Understanding
Chen Ju, Tengda Han, Kunhao Zheng +2
Image-based visual-language (I-VL) pre-training has shown great success for learning joint visual-textual representations from large-scale web data, revealing remarkable ability fo…
Webpage Saliency Prediction with Two-stage Generative Adversarial Networks
Yu Li, Ya Zhang
Web page saliency prediction is a challenge problem in image transformation and computer vision. In this paper, we propose a new model combined with web page outline information to…
Multi-Scale Spatially-Asymmetric Recalibration for Image Classification
Yan Wang, Lingxi Xie, Siyuan Qiao +3
Convolution is spatially-symmetric, i.e., the visual features are independent of its position in the image, which limits its ability to utilize contextual cues for visual recogniti…
Chinese Typeface Transformation with Hierarchical Adversarial Network
Jie Chang, Yujun Gu, Ya Zhang
In this paper, we explore automated typeface generation through image style transfer which has shown great promise in natural image generation. Existing style transfer methods for…
Deep Collaborative Learning for Visual Recognition
Yan Wang, Lingxi Xie, Ya Zhang +2
Deep neural networks are playing an important role in state-of-the-art visual recognition. To represent high-level visual concepts, modern networks are equipped with large convolut…