Search Results for author:
Found 9 papers, 5 papers with code
LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation
Despite the promising performance of current video segmentation models on existing benchmarks, these models still struggle with complex scenes.
The Instance-centric Transformer for the RVOS Track of LSVOS Challenge: 3rd Place Solution
Referring Video Object Segmentation is an emerging multi-modal task that aims to segment objects in the video given a natural language expression.
PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
Moreover, we provide a new motion expression guided video segmentation dataset MeViS to study the natural language-guided video understanding in complex environments.
2nd Place Solution for MeViS Track in CVPR 2024 PVUW Workshop: Motion Expression guided Video Segmentation
Motion Expression guided Video Segmentation is a challenging task that aims at segmenting objects in the video based on natural language expressions with motion descriptions.
Instance Segmentation
Referring Video Object Segmentation
+5
Beyond Literal Descriptions: Understanding and Locating Open-World Objects Aligned with Human Intentions
To promote classic VG towards human intention interpretation, we propose a new intention-driven visual grounding (IVG) task and build a large-scale IVG dataset termed IntentionVG with free-form intention expressions.
Unveiling Parts Beyond Objects: Towards Finer-Granularity Referring Expression Segmentation
To foster future research into fine-grained visual grounding our benchmark RefCOCOm the MRES-32M dataset and model UniRES will be publicly available at https://github. com/Rubics-Xuan/MRES.
Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement
In this paper, we propose a novel framework based on the adapter mechanism, namely Adaptive FSS, which can efficiently adapt the existing FSS model to the novel classes.
Unveiling Parts Beyond Objects:Towards Finer-Granularity Referring Expression Segmentation
To foster future research into fine-grained visual grounding, our benchmark RefCOCOm, the MRES-32M dataset and model UniRES will be publicly available at https://github. com/Rubics-Xuan/MRES
CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation
Referring image segmentation (RIS) is a fundamental vision-language task that intends to segment a desired object from an image based on a given natural language expression.
