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Found 7 papers, 1 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.
Discriminative Spatial-Semantic VOS Solution: 1st Place Solution for 6th LSVOS
Video object segmentation (VOS) is a crucial task in computer vision, but current VOS methods struggle with complex scenes and prolonged object motions.
Learning Spatial-Semantic Features for Robust Video Object Segmentation
Tracking and segmenting multiple similar objects with complex or separate parts in long-term videos is inherently challenging due to the ambiguity of target parts and identity confusion caused by occlusion, background clutter, and long-term variations.
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.
1st Place Solution for MOSE Track in CVPR 2024 PVUW Workshop: Complex Video Object Segmentation
In this challenge, we propose a semantic embedding video object segmentation model and use the salient features of objects as query representations.
Spatial-Temporal Multi-level Association for Video Object Segmentation
In addition, we propose a spatial-temporal memory to assist feature association and temporal ID assignment and correlation.
Simple Contrastive Representation Adversarial Learning for NLP Tasks
In this paper, adversarial training is performed to generate challenging and harder learning adversarial examples over the embedding space of NLP as learning pairs.
