Video Object Segmentation (VOS) is a vital task in computer vision, focusing
on distinguishing foreground objects from the background across video frames.
Our work draws inspiration from the Cutie model, and we investigate the effects
of object memory, the total number of memory frames, and input resolution on
segmentation performance. This report validates the effectiveness of our
inference method on the coMplex video Object SEgmentation (MOSE) dataset, which
features complex occlusions. Our experimental results demonstrate that our
approach achieves a J\&F score of 0.8139 on the test set, securing the third
position in the final ranking. These findings highlight the robustness and
accuracy of our method in handling challenging VOS scenarios.

视频目标分割是计算机视觉中的重要任务，研究了切割模型的灵感、对象记忆、内存帧总数和输入分辨率对分割性能的影响，并在复杂视频目标分割（MOSE）数据集上验证了我们方法的有效性，实验结果表明在测试集上达到了 0.8139 的 J&F 得分，在处理具有挑战性的 VOS 场景中具有强大的鲁棒性和准确性。

CVPR 2024 PVUW 复杂视频对象分割 MOSE 赛道第三名解决方案

3rd Place Solution for MOSE Track in CVPR 2024 PVUW workshop: Complex  Video Object Segmentation

Image captioning is a challenging task where the machine automatically
describes an image by sentences or phrases. It often requires a large number of
paired image-sentence annotations for training. However, a pre-trained
captioning model can hardly be applied to a new domain in which some novel
object categories exist, i.e., the objects and their description words are
unseen during model training. To correctly caption the novel object, it
requires professional human workers to annotate the images by sentences with
the novel words. It is labor expensive and thus limits its usage in real-world
applications.
In this paper, we introduce the zero-shot novel object captioning task where
the machine generates descriptions without extra sentences about the novel
object. To tackle the challenging problem, we propose a Decoupled Novel Object
Captioner (DNOC) framework that can fully decouple the language sequence model
from the object descriptions. DNOC has two components. 1) A Sequence Model with
the Placeholder (SM-P) generates a sentence containing placeholders. The
placeholder represents an unseen novel object. Thus, the sequence model can be
decoupled from the novel object descriptions. 2) A key-value object memory
built upon the freely available detection model, contains the visual
information and the corresponding word for each object. The SM-P will generate
a query to retrieve the words from the object memory. The placeholder will then
be filled with the correct word, resulting in a caption with novel object
descriptions. The experimental results on the held-out MSCOCO dataset
demonstrate the ability of DNOC in describing novel concepts in the zero-shot
novel object captioning task.

本文提出了一种零样本学习的图像字幕生成方法，通过将对象描述与语言模型解耦，在一个包含新颖对象的未知领域中生成字幕，其结果表明该方法在零样本新颖对象字幕任务中表现良好。