7 accepted papers
"Generalizability and few-shot learning are key challenges in Neural Radiance Fields (NeRF), often due to the lack of a holistic understanding in pixel-level rendering. We introduce CaesarNeRF, an end-to-end approach that leverages scene-level CAlibratEd SemAntic Representation along with pixel-leve…
Low-shot image classification where training images are limited or inaccessible has benefited from recent progress on pre-trained vision-language (VL) models with strong generalizability e.g. CLIP. Prompt learning methods built with VL models generate text features from the class names that only hav…
We introduce SEAS using ShapE-Aligned Supervision to enhance appearance-based person re-identification. When recognizing an individual's identity existing methods primarily rely on appearance which can be influenced by the background environment due to a lack of body shape awareness. Although some m…
This paper addresses the challenge of reconstructing a scene with a neural radiance field (NeRF) for robot vision and scene understanding using multiple modalities. Researchers have introduced the use of NeRF to represent an object for synthesizing and rendering novel views of complex scenes by opti…
There is often a resemblance in the sentiment expressed in social media posts (text) and their accompanying images. In this paper, We leverage this sentiment congruence for self-supervised representation learning for sentiment analysis. By teaching the model to pair an image with its corresponding s…
When learning to sketch, beginners start with simple and flexible shapes, and then gradually strive for more complex and accurate ones in the subsequent training sessions. In this paper, we design a ``""shape curriculum'' for learning continuous Signed Distance Function (SDF) on shapes, namely Curri…
Most current state-of-the-art connectome reconstruction pipelines have two major steps: initial pixel-based segmentation with affinity prediction and watershed transform, and refined segmentation by merging over-segmented regions. These methods rely only on local context and are typically agnostic t…