Search Results for author:
Found 10 papers, 1 papers with code
ReMamber: Referring Image Segmentation with Mamba Twister
Referring Image Segmentation (RIS) leveraging transformers has achieved great success on the interpretation of complex visual-language tasks.
AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation
The results show the superior performance of attribute decomposition-aggregation.
Audio-aware Query-enhanced Transformer for Audio-Visual Segmentation
The goal of the audio-visual segmentation (AVS) task is to segment the sounding objects in the video frames using audio cues.
Multi-Modal Prototypes for Open-Set Semantic Segmentation
In this paper, we define a unified setting termed as open-set semantic segmentation (O3S), which aims to learn seen and unseen semantics from both visual examples and textual names.
Annotation-free Audio-Visual Segmentation
The objective of Audio-Visual Segmentation (AVS) is to localise the sounding objects within visual scenes by accurately predicting pixel-wise segmentation masks.
Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning
Interactive segmentation has recently been explored to effectively and efficiently harvest high-quality segmentation masks by iteratively incorporating user hints.
DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery
However, the challenges exist as there is one structural difference between generative and discriminative models, which limits the direct use.
Constraint and Union for Partially-Supervised Temporal Sentence Grounding
Temporal sentence grounding aims to detect the event timestamps described by the natural language query from given untrimmed videos.
Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models
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.
Transforming the Interactive Segmentation for Medical Imaging
The goal of this paper is to interactively refine the automatic segmentation on challenging structures that fall behind human performance, either due to the scarcity of available annotations or the difficulty nature of the problem itself, for example, on segmenting cancer or small organs.
