Large Vision-Language Models (LVLMs) show significant strides in general-purpose
multimodal applications such as visual dialogue and embodied navigation. However …

The recent work known as Segment Anything (SA) has made significant strides in pushing
the boundaries of semantic segmentation into the era of foundation models. The impact of …

Diffusion-based methods have shown impressive visual results in the text-to-image domain.
They first learn a latent space using an autoencoder, then run a denoising process on the …

Recent advancements in deep learning have profoundly impacted the field of animal
behavioral research, offering researchers powerful tools for understanding the complexities …

This work proposes an end-to-end neural interactive keypoint detection framework named
Click-Pose, which can significantly reduce more than 10 times labeling costs of 2D keypoint …

Category-agnostic pose estimation (CAPE) aims to predict keypoints for arbitrary categories
given support images with keypoint annotations. Existing approaches match the keypoints …

Abstract Category-Agnostic Pose Estimation (CAPE) aims to detect keypoints of an arbitrary
unseen category in images, based on several provided examples of that category. This is a …

Recently the prompt-based models have become popular across various language and
vision tasks. In this work, we perform few-shot keypoint detection (FSKD) by detecting any …

In this paper we tackle the task of category-agnostic pose estimation (CAPE) which aims to
predict poses for objects of any category with few annotated samples. Previous works either …

Category-agnostic pose estimation (CAPE) aims to predict keypoints for arbitrary classes
given a few support images annotated with keypoints. Existing methods only rely on the …