When Large Vision-Language Models Meet Person Re-Identification
- URL: http://arxiv.org/abs/2411.18111v1
- Date: Wed, 27 Nov 2024 07:45:25 GMT
- Title: When Large Vision-Language Models Meet Person Re-Identification
- Authors: Qizao Wang, Bin Li, Xiangyang Xue,
- Abstract summary: We propose LVLM-ReID, a novel framework that harnesses the strengths of LVLMs to promote ReID.
Our framework integrates the semantic understanding and generation capabilities of LVLMs into end-to-end ReID training.
Our method achieves competitive results on multiple benchmarks without additional image-text annotations.
- Score: 44.604485649167216
- License:
- Abstract: Large Vision-Language Models (LVLMs) that incorporate visual models and Large Language Models (LLMs) have achieved impressive results across various cross-modal understanding and reasoning tasks. In recent years, person re-identification (ReID) has also started to explore cross-modal semantics to improve the accuracy of identity recognition. However, effectively utilizing LVLMs for ReID remains an open challenge. While LVLMs operate under a generative paradigm by predicting the next output word, ReID requires the extraction of discriminative identity features to match pedestrians across cameras. In this paper, we propose LVLM-ReID, a novel framework that harnesses the strengths of LVLMs to promote ReID. Specifically, we employ instructions to guide the LVLM in generating one pedestrian semantic token that encapsulates key appearance semantics from the person image. This token is further refined through our Semantic-Guided Interaction (SGI) module, establishing a reciprocal interaction between the semantic token and visual tokens. Ultimately, the reinforced semantic token serves as the pedestrian identity representation. Our framework integrates the semantic understanding and generation capabilities of LVLMs into end-to-end ReID training, allowing LVLMs to capture rich semantic cues from pedestrian images during both training and inference. Our method achieves competitive results on multiple benchmarks without additional image-text annotations, demonstrating the potential of LVLM-generated semantics to advance person ReID and offering a promising direction for future research.
Related papers
- FoPru: Focal Pruning for Efficient Large Vision-Language Models [11.36025001578531]
We propose Focal Pruning (FoPru), a training-free method that prunes visual tokens based on the attention-based token significance derived from the vision encoder.
Our method can prune a large number of redundant tokens while maintaining high accuracy, leading to significant improvements in inference efficiency.
arXiv Detail & Related papers (2024-11-21T14:22:38Z) - IDA-VLM: Towards Movie Understanding via ID-Aware Large Vision-Language Model [52.697180472760635]
This paper explores the potential of character identities memory and recognition across multiple visual scenarios.
We propose visual instruction tuning with ID reference and develop an ID-Aware Large Vision-Language Model, IDA-VLM.
Our research introduces a novel benchmark MM-ID, to examine LVLMs on instance IDs memory and recognition across four dimensions.
arXiv Detail & Related papers (2024-07-10T12:11:59Z) - Towards Semantic Equivalence of Tokenization in Multimodal LLM [149.11720372278273]
Vision tokenization is essential for semantic alignment between vision and language.
This paper proposes a novel dynamic Semantic-Equivalent Vision Tokenizer (SeTok)
SeTok groups visual features into semantic units via a dynamic clustering algorithm.
The resulting vision tokens effectively preserve semantic integrity and capture both low-frequency and high-frequency visual features.
arXiv Detail & Related papers (2024-06-07T17:55:43Z) - Generative Cross-Modal Retrieval: Memorizing Images in Multimodal
Language Models for Retrieval and Beyond [99.73306923465424]
We introduce a generative cross-modal retrieval framework, which assigns unique identifier strings to represent images.
By memorizing images in MLLMs, we introduce a new paradigm to cross-modal retrieval, distinct from previous discriminative approaches.
arXiv Detail & Related papers (2024-02-16T16:31:46Z) - Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization [52.935150075484074]
We introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language.
The resulting visual tokens encompass high-level semantics worthy of a word and also support dynamic sequence length varying from the image.
This unification empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously.
arXiv Detail & Related papers (2023-09-09T03:01:38Z) - RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation [53.4319652364256]
This paper presents the RefSAM model, which explores the potential of SAM for referring video object segmentation.
Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-RValModal.
We employ a parameter-efficient tuning strategy to align and fuse the language and vision features effectively.
arXiv Detail & Related papers (2023-07-03T13:21:58Z)
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.