Hierarchical Visual Prompt Learning for Continual Video Instance Segmentation

• URL: http://arxiv.org/abs/2508.08612v1
• Date: Tue, 12 Aug 2025 03:49:08 GMT
• Title: Hierarchical Visual Prompt Learning for Continual Video Instance Segmentation
• Authors: Jiahua Dong, Hui Yin, Wenqi Liang, Hanbin Zhao, Henghui Ding, Nicu Sebe, Salman Khan, Fahad Shahbaz Khan,
• Abstract summary: Video instance segmentation (VIS) has gained significant attention for its capability in tracking and segmenting object instances across video frames.<n>Most of the existing VIS approaches unrealistically assume that the categories of object instances remain fixed over time.<n>We develop a novel Hierarchical Visual Prompt Learning model that overcomes catastrophic forgetting of previous categories from both frame-level and video-level perspectives.
• Score: 115.74044261016554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video instance segmentation (VIS) has gained significant attention for its capability in tracking and segmenting object instances across video frames. However, most of the existing VIS approaches unrealistically assume that the categories of object instances remain fixed over time. Moreover, they experience catastrophic forgetting of old classes when required to continuously learn object instances belonging to new categories. To resolve these challenges, we develop a novel Hierarchical Visual Prompt Learning (HVPL) model that overcomes catastrophic forgetting of previous categories from both frame-level and video-level perspectives. Specifically, to mitigate forgetting at the frame level, we devise a task-specific frame prompt and an orthogonal gradient correction (OGC) module. The OGC module helps the frame prompt encode task-specific global instance information for new classes in each individual frame by projecting its gradients onto the orthogonal feature space of old classes. Furthermore, to address forgetting at the video level, we design a task-specific video prompt and a video context decoder. This decoder first embeds structural inter-class relationships across frames into the frame prompt features, and then propagates task-specific global video contexts from the frame prompt features to the video prompt. Through rigorous comparisons, our HVPL model proves to be more effective than baseline approaches. The code is available at https://github.com/JiahuaDong/HVPL.

Related papers

• Temporal Prompting Matters: Rethinking Referring Video Object Segmentation [64.82333675385802]
  Referring Video Object (RVOS) aims to segment the object referred to by the query sentence in the video.<n>Most existing methods require end-to-end training with dense mask annotations.<n>We propose a Temporal Prompt Generation and Selection (Tenet) framework to address the referring and video factors.
  arXiv  Detail & Related papers  (2025-10-08T17:59:57Z)
• Enhancing Long Video Question Answering with Scene-Localized Frame Grouping [19.83545369186771]
  Current Multimodal Large Language Models (MLLMs) often perform poorly in long video understanding.<n>We propose a new scenario under the video question-answering task, SceneQA.<n>We introduce a novel method called SLFG to combine individual frames into semantically coherent scene frames.
  arXiv  Detail & Related papers  (2025-08-05T02:28:58Z)
• ThinkVideo: High-Quality Reasoning Video Segmentation with Chain of Thoughts [64.93416171745693]
  Reasoning Video Object is a challenging task, which generates a mask sequence from an input video and an implicit, complex text query.<n>Existing works probe into the problem by finetuning Multimodal Large Language Models (MLLM) for segmentation-based output, while still falling short in difficult cases on videos given temporally-sensitive queries.<n>We propose ThinkVideo, a novel framework which leverages the zero-shot Chain-of-Thought (CoT) capability of MLLM to address these challenges.
  arXiv  Detail & Related papers  (2025-05-24T07:01:31Z)
• Spatio-temporal Prompting Network for Robust Video Feature Extraction [74.54597668310707]
  Frametemporal is one of the main challenges in the field of video understanding. Recent approaches exploit transformer-based integration modules to obtain quality-of-temporal information. We present a neat and unified framework called N-Temporal Prompting Network (NNSTP) It can efficiently extract video features by adjusting the input features in the network backbone.
  arXiv  Detail & Related papers  (2024-02-04T17:52:04Z)
• SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation [35.063881868130075]
  This paper studies referring video object segmentation (RVOS) by boosting video-level visual-linguistic alignment. We propose Semantic-assisted Object Cluster (SOC), which aggregates video content and textual guidance for unified temporal modeling and cross-modal alignment. We conduct extensive experiments on popular RVOS benchmarks, and our method outperforms state-of-the-art competitors on all benchmarks by a remarkable margin.
  arXiv  Detail & Related papers  (2023-05-26T15:13:44Z)
• The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge--Track 3: Referring Video Object Segmentation [18.630453674396534]
  ReferFormer aims to segment object instances in a given video referred by a language expression in all video frames. This work proposes several tricks to boost further, including cyclical learning rates, semi-supervised approach, and test-time augmentation inference. The improved ReferFormer ranks 2nd place on CVPR2022 Referring Youtube-VOS Challenge.
  arXiv  Detail & Related papers  (2022-06-24T02:15:06Z)
• Efficient Video Instance Segmentation via Tracklet Query and Proposal [62.897552852894854]
  Video Instance aims to simultaneously classify, segment, and track multiple object instances in videos. Most clip-level methods are neither end-to-end learnable nor real-time. This paper proposes EfficientVIS, a fully end-to-end framework with efficient training and inference.
  arXiv  Detail & Related papers  (2022-03-03T17:00:11Z)
• HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images [123.65233334380251]
  We propose HODOR: a novel method that effectively leveraging annotated static images for understanding object appearance and scene context. As a result, HODOR achieves state-of-the-art performance on the DAVIS and YouTube-VOS benchmarks. Without any architectural modification, HODOR can also learn from video context around single annotated video frames.
  arXiv  Detail & Related papers  (2021-12-16T18:59:53Z)
• End-to-End Video Instance Segmentation with Transformers [84.17794705045333]
  Video instance segmentation (VIS) is the task that requires simultaneously classifying, segmenting and tracking object instances of interest in video. Here, we propose a new video instance segmentation framework built upon Transformers, termed VisTR, which views the VIS task as a direct end-to-end parallel sequence decoding/prediction problem. For the first time, we demonstrate a much simpler and faster video instance segmentation framework built upon Transformers, achieving competitive accuracy.
  arXiv  Detail & Related papers  (2020-11-30T02:03:50Z)

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.