Xiaofei Zhou

Chuheng Chen, Xiaofei Zhou, Geyuan Zhang et al.

Low-Rank Adaptation (LoRA) fusion has emerged as a key technique for reusing and composing learned subject and style representations for controllable generation without costly retraining. However, existing methods rely on weight-based merging, where one LoRA often dominates the other, leading to interference and degraded fidelity. This interference is structural: separately trained LoRAs occupy low-rank high-dimensional subspaces, leading to non-orthogonal and overlapping representations. In this work, we analyze the internal structure of LoRAs and find their generative behavior is dominated by a few principal directions in the low-rank subspace, which should remain free from interference during fusion. To achieve this, we propose Null Space Projection LoRA (NP-LoRA), a projection-based framework for LoRA fusion that enforces subspace separation to prevent structural interference among principal directions. Specifically, we first extract principal style directions via singular value decomposition (SVD) and then project the subject LoRA into its orthogonal null space. Furthermore, we introduce a soft projection mechanism that enables smooth control over the trade-off between subject fidelity and style consistency. Experiments show NP-LoRA consistently improves fusion quality over strong baselines (e.g., DINO and CLIP-based metrics, with human and LLM preference scores), and applies broadly across backbones and LoRA pairs without retraining.

Zeliang Song, Xiaofei Zhou

Image captioning is one of the most challenging tasks in AI, which aims to automatically generate textual sentences for an image. Recent methods for image captioning follow encoder-decoder framework that transforms the sequence of salient regions in an image into natural language descriptions. However, these models usually lack the comprehensive understanding of the contextual interactions reflected on various visual relationships between objects. In this paper, we explore explicit and implicit visual relationships to enrich region-level representations for image captioning. Explicitly, we build semantic graph over object pairs and exploit gated graph convolutional networks (Gated GCN) to selectively aggregate local neighbors' information. Implicitly, we draw global interactions among the detected objects through region-based bidirectional encoder representations from transformers (Region BERT) without extra relational annotations. To evaluate the effectiveness and superiority of our proposed method, we conduct extensive experiments on Microsoft COCO benchmark and achieve remarkable improvements compared with strong baselines.

Zeliang Song, Xiaofei Zhou, Zhendong Mao et al.

Image captioning is a challenging computer vision task, which aims to generate a natural language description of an image. Most recent researches follow the encoder-decoder framework which depends heavily on the previous generated words for the current prediction. Such methods can not effectively take advantage of the future predicted information to learn complete semantics. In this paper, we propose Context-Aware Auxiliary Guidance (CAAG) mechanism that can guide the captioning model to perceive global contexts. Upon the captioning model, CAAG performs semantic attention that selectively concentrates on useful information of the global predictions to reproduce the current generation. To validate the adaptability of the method, we apply CAAG to three popular captioners and our proposal achieves competitive performance on the challenging Microsoft COCO image captioning benchmark, e.g. 132.2 CIDEr-D score on Karpathy split and 130.7 CIDEr-D (c40) score on official online evaluation server.

Pengcheng Liao, Chuang Zhang, Xiaojun Chen et al.

Recent neural sequence to sequence models have provided feasible solutions for abstractive summarization. However, such models are still hard to tackle long text dependency in the summarization task. A high-quality summarization system usually depends on strong encoder which can refine important information from long input texts so that the decoder can generate salient summaries from the encoder's memory. In this paper, we propose an aggregation mechanism based on the Transformer model to address the challenge of long text representation. Our model can review history information to make encoder hold more memory capacity. Empirically, we apply our aggregation mechanism to the Transformer model and experiment on CNN/DailyMail dataset to achieve higher quality summaries compared to several strong baseline models on the ROUGE metrics.