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Mingcan Ma

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AAAI Conference 2025 Conference Paper

FreeGen: Bridging Visual-Linguistic Discrepancies Towards Diffusion-based Pixel-level Data Synthesis

Wenzhuang Wang
Mingcan Ma
Yong Chen
Changqun Xia
Zhenbao Liang
Jia Li

Text-to-image diffusion model has inspired research into text-to-data synthesis without human intervention, where spatial attentions correlated with semantic entities in text prompts are primarily interpreted as pseudo-masks. However, these vannila attentions often deliver visual-linguistic discrepancies, in which the associations between image features and entity-level tokens are unstable and divergent, yielding inferior masks for realistic applications, especially in more practical open-vocabulary settings. To tackle this issue, we propose a novel text-guided self-driven generative paradigm, termed FreeGen, which addresses the discrepancies by recalibrating intrinsic visual-linguistic correlations and serves as a real-data-free method to automatically synthesize open-vocabulary pixel-level data for arbitrary entities. Specifically, we first learn an Attention Self-Rectification mechanism to reproject the inherent attention matrices to achieve robust semantic alignment, thereby obtaining class-discriminative masks. A Temporal Fluctuation Factor is present to assess mask quality based on its variation over uniform sampling timesteps, enabling the selection of reliable masks. These masks are then employed as self-supervised signals to support the learning of an Entity-level Grounding Decoder in a self-training manner, thus producing open-vocabulary segmentation results. Extensive experiments show that the existing segmenters trained on FreeGen narrow the performance gap with real data counterparts and remarkably outperform the state-of-the-art methods.

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AAAI Conference 2021 Conference Paper

Pyramidal Feature Shrinking for Salient Object Detection

Mingcan Ma
Changqun Xia
Jia Li

Recently, we have witnessed the great progress of salient object detection (SOD), which benefits from the effectiveness of various feature aggregation strategies. However, existing methods usually aggregate the low-level features containing details and the high-level features containing semantics over a large span, which introduces noise into the aggregated features and generates inaccurate saliency maps. In this paper, we propose a pyramidal feature shrinking network (PFSNet), which aims to aggregate adjacent feature nodes in pairs with layer-by-layer shrinkage, so that the aggregated features fuse effective details and semantics and discard interference information. Specifically, a pyramidal shrinking decoder (PSD) is proposed to aggregate adjacent features hierarchically in an asymptotic manner. Unlike other methods that aggregate features with significantly different information, this method only focuses on adjacent feature nodes in each layer and shrinks them to a final unique feature node. Besides, we propose an adjacent fusion module (AFM) to perform mutual spatial enhancement between the adjacent features to dynamically weight the features and adaptively fuse the appropriate information. Besides, a scale-aware enrichment module (SEM) based on the features extracted from the backbone is utilized to obtain rich scale information and generate diverse initial features with dilated convolutions. Extensive quantitative and qualitative experiments demonstrate that the proposed intuitive framework outperforms 14 state-of-the-art approaches on 5 public datasets.

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