9 accepted papers
Generating editable 3D CAD models from natural language remains challenging, as existing text-to-CAD systems either produce meshes or rely on scarce design-history data. We present NURBGen, the first framework to generate high fidelity 3D CAD models directly from text using Non-Uniform Rational B-Sp
Generating high-fidelity 3D content from text prompts remains a significant challenge in computer vision due to the limited size, diversity, and annotation depth of the existing datasets. To address this, we introduce MARVEL-40M+, an extensive dataset with 40 million text annotations for over 8.9 mi…
This paper addresses key limitations in current Semi-Supervised Object Detection (SSOD) frameworks, focusing on issues related to pseudo-label quality, confidence bias, and inefficient query generation. Traditional methods, including CNN-based and DETR-based architectures, often face challenges such…
Low-light conditions significantly degrade the performance of high-level vision tasks. Existing approaches either enhance low-light images without considering normal illumination scenarios, leading to poor generalization, or are tailored to specific tasks. We propose TorchAdapt, a realtime adaptive…
In this paper we address the limitations of the DETR-based semi-supervised object detection (SSOD) framework particularly focusing on the challenges posed by the quality of object queries. In DETR-based SSOD the one-to-one assignment strategy provides inaccurate pseudo-labels while the one-to-many a…
Prototyping complex computer-aided design (CAD) models in modern softwares can be very time-consuming. This is due to the lack of intelligent systems that can quickly generate simpler intermediate parts. We propose Text2CAD, the first AI framework for generating text-to-parametric CAD models using d…
Extracting useful visual cues for the downstream tasks is especially challenging under low-light vision. Prior works create enhanced representations by either correlating visual quality with machine perception or designing illumination-degrading transformation methods that require pre-training on…
Recent works have shown that unstructured text (documents) from online sources can serve as useful auxiliary information for zero-shot image classification. However, these methods require access to a high-quality source like Wikipedia and are limited to a single source of information. Large Language…
Continual Learning aims to learn a single model on a sequence of tasks without having access to data from previous tasks. The biggest challenge in the domain still remains catastrophic forgetting: a loss in performance on seen classes of earlier tasks. Some existing methods rely on an expensive repl…