Progressive Text-to-Image Diffusion with Soft Latent Direction

In spite of the rapidly evolving landscape of text-to-image generation, the synthesis and manipulation of multiple entities while adhering to specific relational constraints pose enduring challenges. This paper introduces an innovative progressive synthesis and editing operation that systematically incorporates entities into the target image, ensuring their adherence to spatial and relational constraints at each sequential step. Our key insight stems from the observation that while a pre-trained text-to-image diffusion model adeptly handles one or two entities, it often falters when dealing with a greater number. To address this limitation, we propose harnessing the capabilities of a Large Language Model (LLM) to decompose intricate and protracted text descriptions into coherent directives adhering to stringent formats. To facilitate the execution of directives involving distinct semantic operations-namely insertion, editing, and erasing-we formulate the Stimulus, Response, and Fusion (SRF) framework. Within this framework, latent regions are gently stimulated in alignment with each operation, followed by the fusion of the responsive latent components to achieve cohesive entity manipulation. Our proposed framework yields notable advancements in object synthesis, particularly when confronted with intricate and lengthy textual inputs. Consequently, it establishes a new benchmark for text-to-image generation tasks, further elevating the field's performance standards.Comment: 14 pages, 15 figure

Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification

Occluded person re-identification (ReID) is a challenging problem due to contamination from occluders, and existing approaches address the issue with prior knowledge cues, eg human body key points, semantic segmentations and etc, which easily fails in the presents of heavy occlusion and other humans as occluders. In this paper, we propose a feature pruning and consolidation (FPC) framework to circumvent explicit human structure parse, which mainly consists of a sparse encoder, a global and local feature ranking module, and a feature consolidation decoder. Specifically, the sparse encoder drops less important image tokens (mostly related to background noise and occluders) solely according to correlation within the class token attention instead of relying on prior human shape information. Subsequently, the ranking stage relies on the preserved tokens produced by the sparse encoder to identify k-nearest neighbors from a pre-trained gallery memory by measuring the image and patch-level combined similarity. Finally, we use the feature consolidation module to compensate pruned features using identified neighbors for recovering essential information while disregarding disturbance from noise and occlusion. Experimental results demonstrate the effectiveness of our proposed framework on occluded, partial and holistic Re-ID datasets. In particular, our method outperforms state-of-the-art results by at least 8.6% mAP and 6.0% Rank-1 accuracy on the challenging Occluded-Duke dataset.Comment: 12 pages, 9 figure

Fine-grained Appearance Transfer with Diffusion Models

Image-to-image translation (I2I), and particularly its subfield of appearance transfer, which seeks to alter the visual appearance between images while maintaining structural coherence, presents formidable challenges. Despite significant advancements brought by diffusion models, achieving fine-grained transfer remains complex, particularly in terms of retaining detailed structural elements and ensuring information fidelity. This paper proposes an innovative framework designed to surmount these challenges by integrating various aspects of semantic matching, appearance transfer, and latent deviation. A pivotal aspect of our approach is the strategic use of the predicted $x_0$ space by diffusion models within the latent space of diffusion processes. This is identified as a crucial element for the precise and natural transfer of fine-grained details. Our framework exploits this space to accomplish semantic alignment between source and target images, facilitating mask-wise appearance transfer for improved feature acquisition. A significant advancement of our method is the seamless integration of these features into the latent space, enabling more nuanced latent deviations without necessitating extensive model retraining or fine-tuning. The effectiveness of our approach is demonstrated through extensive experiments, which showcase its ability to adeptly handle fine-grained appearance transfers across a wide range of categories and domains. We provide our code at https://github.com/babahui/Fine-grained-Appearance-TransferComment: 14 pages, 15 figure

GOLM1 Stimulation of Glutamine Metabolism Promotes Osteoporosis via Inhibiting Osteogenic Differentiation of BMSCs

Background/Aims: Bone marrow mesenchymal stem cells (BMSCs) play an essential role in osteoporosis. However, the molecular mechanisms and the involvement of glutamine metabolism in osteogenic BMSCs differentiation and osteoporosis remain largely unclear. In this study, we investigated the role of Golgi membrane protein 1 (GOLM1) and glutamine metabolism in BMSCs differentiation and osteoporosis. Methods: Osteogenic differentiation-inducing media (Odi) was used to induce the osteogenic differentiation of BMSCs. The mRNA expression of GOLM1, ALP, Runx2, Osx, BSP and OCN was determined by qRT-PCR assay. Western blot assay was used to analyze GOLM1, p-mTOR, mTOR, p-S6 and S6 abundance in GOLM1 silencing and over-expressed BMSCs. Glutamine uptake, intracellular glutamine, glutamate and α-KG level was detected using indicated Kits. GOLM1 antibody, glutamine metabolism inhibitors EGCG and BPTES were used to treat ovariectomy (OVX)-induced osteoporosis. Bone mineral density and bone volume relative to tissue volume (%) were analyzed by micro-CT. Serum was collected from osteoporosis patients and healthy participants and subjected to GOLM1 determination using ELISA Kit. Results: GOLM1 expression and glutamine metabolism were suppressed by Odi. GOLM1 blockage or inhibition of glutamine metabolism promoted the osteogenic differentiation of BMSCs induced by Odi. GOLM1 activated glutamine metabolism depending on the mTOR signaling pathway. In vivo, GOLM1 antibody or combination of glutamine inhibitor EGCG and BPTES rescued the osteoporosis in an OVX-operated mouse model. Serum GOLM1 level was increased in the patients of osteoporosis compared with healthy people. Conclusion: GOLM1 stimulates glutamine metabolism to suppress the osteogenic differentiation of BMSCs and to promote osteoporosis. Therefore, GOLM1 activation of glutamine metabolism is a potential target for osteoporosis