No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves

Recent studies have demonstrated that learning a meaningful internal representation can both accelerate generative training and enhance generation quality of the diffusion transformers. However, existing approaches necessitate to either introduce an additional and complex representation training framework or rely on a large-scale, pre-trained representation foundation model to provide representation guidance during the original generative training process. In this study, we posit that the unique discriminative process inherent to diffusion transformers enables them to offer such guidance without requiring external representation components. We therefore propose Self-Representation A}lignment (SRA), a simple yet straightforward method that obtain representation guidance through a self-distillation manner. Specifically, SRA aligns the output latent representation of the diffusion transformer in earlier layer with higher noise to that in later layer with lower noise to progressively enhance the overall representation learning during only generative training process. Experimental results indicate that applying SRA to DiTs and SiTs yields consistent performance improvements. Moreover, SRA not only significantly outperforms approaches relying on auxiliary, complex representation training frameworks but also achieves performance comparable to methods that heavily dependent on powerful external representation priors.

AffordanceSAM: Segment Anything Once More in Affordance Grounding

Improving the generalization ability of an affordance grounding model to recognize regions for unseen objects and affordance functions is crucial for real-world application. However, current models are still far away from such standards. To address this problem, we introduce AffordanceSAM, an effective approach that extends SAM's generalization capacity to the domain of affordance grounding. For the purpose of thoroughly transferring SAM's robust performance in segmentation to affordance, we initially propose an affordance-adaption module in order to help modify SAM's segmentation output to be adapted to the specific functional regions required for affordance grounding. We concurrently make a coarse-to-fine training recipe to make SAM first be aware of affordance objects and actions coarsely, and then be able to generate affordance heatmaps finely. Both quantitative and qualitative experiments show the strong generalization capacity of our AffordanceSAM, which not only surpasses previous methods under AGD20K benchmark but also shows evidence to handle the task with novel objects and affordance functions.

Unbiased General Annotated Dataset Generation

Pre-training backbone networks on a general annotated dataset (e.g., ImageNet) that comprises numerous manually collected images with category annotations has proven to be indispensable for enhancing the generalization capacity of downstream visual tasks. However, those manually collected images often exhibit bias, which is non-transferable across either categories or domains, thus causing the model's generalization capacity degeneration. To mitigate this problem, we present an unbiased general annotated dataset generation framework (ubGen). Instead of expensive manual collection, we aim at directly generating unbiased images with category annotations. To achieve this goal, we propose to leverage the advantage of a multimodal foundation model (e.g., CLIP), in terms of aligning images in an unbiased semantic space defined by language. Specifically, we develop a bi-level semantic alignment loss, which not only forces all generated images to be consistent with the semantic distribution of all categories belonging to the target dataset in an adversarial learning manner, but also requires each generated image to match the semantic description of its category name. In addition, we further cast an existing image quality scoring model into a quality assurance loss to preserve the quality of the generated image. By leveraging these two loss functions, we can obtain an unbiased image generation model by simply fine-tuning a pre-trained diffusion model using only all category names in the target dataset as input. Experimental results confirm that, compared with the manually labeled dataset or other synthetic datasets, the utilization of our generated unbiased datasets leads to stable generalization capacity enhancement of different backbone networks across various tasks, especially in tasks where the manually labeled samples are scarce.