Diverse Rare Sample Generation with Pretrained GANs

Deep generative models are proficient in generating realistic data but struggle with producing rare samples in low density regions due to their scarcity of training datasets and the mode collapse problem. While recent methods aim to improve the fidelity of generated samples, they often reduce diversity and coverage by ignoring rare and novel samples. This study proposes a novel approach for generating diverse rare samples from high-resolution image datasets with pretrained GANs. Our method employs gradient-based optimization of latent vectors within a multi-objective framework and utilizes normalizing flows for density estimation on the feature space. This enables the generation of diverse rare images, with controllable parameters for rarity, diversity, and similarity to a reference image. We demonstrate the effectiveness of our approach both qualitatively and quantitatively across various datasets and GANs without retraining or fine-tuning the pretrained GANs.

Mutually-Aware Feature Learning for Few-Shot Object Counting

Few-shot object counting has garnered significant attention for its practicality as it aims to count target objects in a query image based on given exemplars without the need for additional training. However, there is a shortcoming in the prevailing extract-and-match approach: query and exemplar features lack interaction during feature extraction since they are extracted unaware of each other and later correlated based on similarity. This can lead to insufficient target awareness of the extracted features, resulting in target confusion in precisely identifying the actual target when multiple class objects coexist. To address this limitation, we propose a novel framework, Mutually-Aware FEAture learning(MAFEA), which encodes query and exemplar features mutually aware of each other from the outset. By encouraging interaction between query and exemplar features throughout the entire pipeline, we can obtain target-aware features that are robust to a multi-category scenario. Furthermore, we introduce a background token to effectively associate the target region of query with exemplars and decouple its background region from them. Our extensive experiments demonstrate that our model reaches a new state-of-the-art performance on the two challenging benchmarks, FSCD-LVIS and FSC-147, with a remarkably reduced degree of the target confusion problem.