Harnessing Vision-Language Pretrained Models with Temporal-Aware Adaptation for Referring Video Object Segmentation
- URL: http://arxiv.org/abs/2405.10610v2
- Date: Sun, 22 Sep 2024 06:32:08 GMT
- Title: Harnessing Vision-Language Pretrained Models with Temporal-Aware Adaptation for Referring Video Object Segmentation
- Authors: Zikun Zhou, Wentao Xiong, Li Zhou, Xin Li, Zhenyu He, Yaowei Wang,
- Abstract summary: Current Referring Video Object (RVOS) methods typically use vision and language models pretrained independently as backbones.
We propose a temporal-aware prompt-tuning method, which adapts pretrained representations for pixel-level prediction.
Our method performs favorably against state-of-the-art algorithms and exhibits strong generalization abilities.
- Score: 34.37450315995176
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The crux of Referring Video Object Segmentation (RVOS) lies in modeling dense text-video relations to associate abstract linguistic concepts with dynamic visual contents at pixel-level. Current RVOS methods typically use vision and language models pretrained independently as backbones. As images and texts are mapped to uncoupled feature spaces, they face the arduous task of learning Vision-Language (VL) relation modeling from scratch. Witnessing the success of Vision-Language Pretrained (VLP) models, we propose to learn relation modeling for RVOS based on their aligned VL feature space. Nevertheless, transferring VLP models to RVOS is a deceptively challenging task due to the substantial gap between the pretraining task (static image/region-level prediction) and the RVOS task (dynamic pixel-level prediction). To address this transfer challenge, we introduce a framework named VLP-RVOS which harnesses VLP models for RVOS through temporal-aware adaptation. We first propose a temporal-aware prompt-tuning method, which not only adapts pretrained representations for pixel-level prediction but also empowers the vision encoder to model temporal contexts. We further customize a cube-frame attention mechanism for robust spatial-temporal reasoning. Besides, we propose to perform multi-stage VL relation modeling while and after feature extraction for comprehensive VL understanding. Extensive experiments demonstrate that our method performs favorably against state-of-the-art algorithms and exhibits strong generalization abilities.
Related papers
- VL-GPT: A Generative Pre-trained Transformer for Vision and Language
Understanding and Generation [79.02357561313785]
We introduce Vision-Language Generative Pre-trained Transformer (VL-GPT), a transformer model proficient at concurrently perceiving and generating visual and linguistic data.
VL-GPT achieves a unified pre-training approach for both image and text modalities by employing a straightforward auto-regressive objective.
arXiv Detail & Related papers (2023-12-14T18:59:43Z) - Expedited Training of Visual Conditioned Language Generation via
Redundancy Reduction [61.16125290912494]
$textEVL_textGen$ is a framework designed for the pre-training of visually conditioned language generation models.
We show that our approach accelerates the training of vision-language models by a factor of 5 without a noticeable impact on overall performance.
arXiv Detail & Related papers (2023-10-05T03:40:06Z) - CAVL: Learning Contrastive and Adaptive Representations of Vision and
Language [10.57079240576682]
Visual and linguistic pre-training aims to learn vision and language representations together.
Current pre-trained models tend to take lots of computation resources for fine-tuning when transferred to downstream tasks.
We present a simple but effective approach for learning Contrastive and Adaptive representations of Vision and Language, namely CAVL.
arXiv Detail & Related papers (2023-04-10T05:54:03Z) - Teaching Structured Vision&Language Concepts to Vision&Language Models [46.344585368641006]
We introduce the collective notion of Structured Vision&Language Concepts (SVLC)
SVLC includes object attributes, relations, and states which are present in the text and visible in the image.
We propose a more elegant data-driven approach for enhancing VL models' understanding of SVLCs.
arXiv Detail & Related papers (2022-11-21T18:54:10Z) - LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal
Modeling [48.283659682112926]
We propose LiteVL, which adapts a pre-trained image-language model BLIP into a video-text model directly on downstream tasks.
We also propose a non-parametric pooling mechanism to adaptively reweight the fine-grained video embedding conditioned on the text.
arXiv Detail & Related papers (2022-10-21T13:03:49Z) - VL-BEiT: Generative Vision-Language Pretraining [107.25298505511184]
We introduce a vision-language foundation model called VL-BEiT, which is a bidirectional multimodal Transformer learned by generative pretraining.
Specifically, we perform masked vision-language modeling on image-text pairs, masked language modeling on texts, and masked image modeling on images.
arXiv Detail & Related papers (2022-06-02T16:14:19Z) - PEVL: Position-enhanced Pre-training and Prompt Tuning for
Vision-language Models [127.17675443137064]
We introduce PEVL, which enhances the pre-training and prompt tuning of vision-language models with explicit object position modeling.
PEVL reformulates discretized object positions and language in a unified language modeling framework.
We show that PEVL enables state-of-the-art performance on position-sensitive tasks such as referring expression comprehension and phrase grounding.
arXiv Detail & Related papers (2022-05-23T10:17:53Z) - Behind the Scene: Revealing the Secrets of Pre-trained
Vision-and-Language Models [65.19308052012858]
Recent Transformer-based large-scale pre-trained models have revolutionized vision-and-language (V+L) research.
We present VALUE, a set of meticulously designed probing tasks to decipher the inner workings of multimodal pre-training.
Key observations: Pre-trained models exhibit a propensity for attending over text rather than images during inference.
arXiv Detail & Related papers (2020-05-15T01:06:54Z)
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.