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Dong Jing

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2 papers

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AAAI Conference 2025 Conference Paper

Leveraging Large Vision-Language Model as User Intent-Aware Encoder for Composed Image Retrieval

Zelong Sun
Dong Jing
Guoxing Yang
Nanyi Fei
Zhiwu Lu

Composed Image Retrieval (CIR) aims to retrieve target images from candidate set using a hybrid-modality query consisting of a reference image and a relative caption that describes the user intent. Recent studies attempt to utilize Vision-Language Pre-training Models (VLPMs) with various fusion strategies for addressing the task. However, these methods typically fail to simultaneously meet two key requirements of CIR: comprehensively extracting visual information and faithfully following the user intent. In this work, we propose CIR-LVLM, a novel framework that leverages the large vision-language model (LVLM) as the powerful user intent-aware encoder to better meet these requirements. Our motivation is to explore the advanced reasoning and instruction-following capabilities of LVLM for accurately understanding and responding the user intent. Furthermore, we design a novel hybrid intent instruction module to provide explicit intent guidance at two levels: (1) The task prompt clarifies the task requirement and assists the model in discerning user intent at the task level. (2) The instance-specific soft prompt, which is adaptively selected from the learnable prompt pool, enables the model to better comprehend the user intent at the instance level compared to a universal prompt for all instances. CIR-LVLM achieves state-of-the-art performance across three prominent benchmarks with acceptable inference efficiency. We believe this study provides fundamental insights into CIR-related fields.

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NeurIPS Conference 2024 Conference Paper

FineCLIP: Self-distilled Region-based CLIP for Better Fine-grained Understanding

Dong Jing
Xiaolong He
Yutian Luo
Nanyi Fei
Guoxing Yang
Wei Wei
Huiwen Zhao
Zhiwu Lu

Contrastive Language-Image Pre-training (CLIP) achieves impressive performance on tasks like image classification and image-text retrieval by learning on large-scale image-text datasets. However, CLIP struggles with dense prediction tasks due to the poor grasp of the fine-grained details. Although existing works pay attention to this issue, they achieve limited improvements and usually sacrifice the important visual-semantic consistency. To overcome these limitations, we propose FineCLIP, which keeps the global contrastive learning to preserve the visual-semantic consistency and further enhances the fine-grained understanding through two innovations: 1) A real-time self-distillation scheme that facilitates the transfer of representation capability from global to local features. 2) A semantically-rich regional contrastive learning paradigm with generated region-text pairs, boosting the local representation capabilities with abundant fine-grained knowledge. Both cooperate to fully leverage diverse semantics and multi-grained complementary information. To validate the superiority of our FineCLIP and the rationality of each design, we conduct extensive experiments on challenging dense prediction and image-level tasks. All the observations demonstrate the effectiveness of FineCLIP.

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