LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation

• URL: http://arxiv.org/abs/2306.08736v3
• Date: Tue, 2 Apr 2024 03:10:53 GMT
• Title: LoSh: Long-Short Text Joint Prediction Network for Referring Video Object Segmentation
• Authors: Linfeng Yuan, Miaojing Shi, Zijie Yue, Qijun Chen,
• Abstract summary: Referring video object segmentation (RVOS) aims to segment the target instance referred by a given text expression in a video clip. The text expression normally contains sophisticated description of the instance's appearance, action, and relation with others. We tackle this problem by taking a subject-centric short text expression from the original long text expression.
• Score: 18.832338318596648
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring video object segmentation (RVOS) aims to segment the target instance referred by a given text expression in a video clip. The text expression normally contains sophisticated description of the instance's appearance, action, and relation with others. It is therefore rather difficult for a RVOS model to capture all these attributes correspondingly in the video; in fact, the model often favours more on the action- and relation-related visual attributes of the instance. This can end up with partial or even incorrect mask prediction of the target instance. We tackle this problem by taking a subject-centric short text expression from the original long text expression. The short one retains only the appearance-related information of the target instance so that we can use it to focus the model's attention on the instance's appearance. We let the model make joint predictions using both long and short text expressions; and insert a long-short cross-attention module to interact the joint features and a long-short predictions intersection loss to regulate the joint predictions. Besides the improvement on the linguistic part, we also introduce a forward-backward visual consistency loss, which utilizes optical flows to warp visual features between the annotated frames and their temporal neighbors for consistency. We build our method on top of two state of the art pipelines. Extensive experiments on A2D-Sentences, Refer-YouTube-VOS, JHMDB-Sentences and Refer-DAVIS17 show impressive improvements of our method.Code is available at https://github.com/LinfengYuan1997/Losh.

Related papers

• VrdONE: One-stage Video Visual Relation Detection [30.983521962897477]
  Video Visual Relation Detection (VidVRD) focuses on understanding how entities over time and space in videos. Traditional methods for VidVRD, challenged by its complexity, typically split the task into two parts: one for identifying what relation are present and another for determining their temporal boundaries. We propose VrdONE, a streamlined yet efficacious one-stage model for VidVRD.
  arXiv  Detail & Related papers  (2024-08-18T08:38:20Z)
• Relation Rectification in Diffusion Model [64.84686527988809]
  We introduce a novel task termed Relation Rectification, aiming to refine the model to accurately represent a given relationship it initially fails to generate. We propose an innovative solution utilizing a Heterogeneous Graph Convolutional Network (HGCN) The lightweight HGCN adjusts the text embeddings generated by the text encoder, ensuring the accurate reflection of the textual relation in the embedding space.
  arXiv  Detail & Related papers  (2024-03-29T15:54:36Z)
• Zero-Shot Dense Video Captioning by Jointly Optimizing Text and Moment [10.567291051485194]
  We propose ZeroTA, a novel method for dense video captioning in a zero-shot manner. Our method does not require any videos or annotations for training; instead, it localizes and describes events within each input video at test time.
  arXiv  Detail & Related papers  (2023-07-05T23:01:26Z)
• Learning Grounded Vision-Language Representation for Versatile Understanding in Untrimmed Videos [57.830865926459914]
  We propose a vision-language learning framework for untrimmed videos, which automatically detects informative events. Instead of coarse-level video-language alignments, we present two dual pretext tasks to encourage fine-grained segment-level alignments. Our framework is easily to tasks covering visually-grounded language understanding and generation.
  arXiv  Detail & Related papers  (2023-03-11T11:00:16Z)
• Jointly Visual- and Semantic-Aware Graph Memory Networks for Temporal Sentence Localization in Videos [67.12603318660689]
  We propose a novel Hierarchical Visual- and Semantic-Aware Reasoning Network (HVSARN) HVSARN enables both visual- and semantic-aware query reasoning from object-level to frame-level. Experiments on three datasets demonstrate that our HVSARN achieves a new state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-03-02T08:00:22Z)
• Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training [70.83385449872495]
  The correlation between the vision and text is essential for video moment retrieval (VMR) Existing methods rely on separate pre-training feature extractors for visual and textual understanding. We propose a generic method, referred to as Visual-Dynamic Injection (VDI), to empower the model's understanding of video moments.
  arXiv  Detail & Related papers  (2023-02-28T19:29:05Z)
• Correspondence Matters for Video Referring Expression Comprehension [64.60046797561455]
  Video Referring Expression (REC) aims to localize the referent objects described in the sentence to visual regions in the video frames. Existing methods suffer from two problems: 1) inconsistent localization results across video frames; 2) confusion between the referent and contextual objects. We propose a novel Dual Correspondence Network (dubbed as DCNet) which explicitly enhances the dense associations in both the inter-frame and cross-modal manners.
  arXiv  Detail & Related papers  (2022-07-21T10:31:39Z)
• Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency [28.352140544936198]
  Weakly supervised instance segmentation reduces the cost of annotations required to train models. We show that these issues can be better addressed by training with weakly labeled videos instead of images. We are the first to explore the use of these video signals to tackle weakly supervised instance segmentation.
  arXiv  Detail & Related papers  (2021-03-23T23:20:46Z)
• ClawCraneNet: Leveraging Object-level Relation for Text-based Video Segmentation [47.7867284770227]
  Text-based video segmentation is a challenging task that segments out the natural language referred objects in videos. We introduce a novel top-down approach by imitating how we human segment an object with the language guidance. Our method outperforms state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2021-03-19T09:31:08Z)
• Spatio-Temporal Graph for Video Captioning with Knowledge Distillation [50.034189314258356]
  We propose a graph model for video captioning that exploits object interactions in space and time. Our model builds interpretable links and is able to provide explicit visual grounding. To avoid correlations caused by the variable number of objects, we propose an object-aware knowledge distillation mechanism.
  arXiv  Detail & Related papers  (2020-03-31T03:58:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.