1 citations · 2 across the 9 of their papers we have counts for
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The Devil is in the EOS: Sequence Training for Detailed Image Captioning
Abdelrahman Mohamed, Yova Kementchedjhieva
Despite significant advances in vision-language models (VLMs), image captioning often suffers from a lack of detail, with base models producing short, generic captions. This limita…
iLRM: An Iterative Large 3D Reconstruction Model
Gyeongjin Kang, Seungtae Nam, Seungkwon Yang +4
Feed-forward 3D modeling has emerged as a promising approach for rapid and high-quality 3D reconstruction. In particular, directly generating explicit 3D representations, such as 3…
LLMs Can Compensate for Deficiencies in Visual Representations
Sho Takishita, Jay Gala, Abdelrahman Mohamed +2
Many vision-language models (VLMs) that prove very effective at a range of multimodal task, build on CLIP-based vision encoders, which are known to have various limitations. We inv…
fCOP: Focal Length Estimation from Category-level Object Priors
Xinyue Zhang, Jiaqi Yang, Xiangting Meng +2
In the realm of computer vision, the perception and reconstruction of the 3D world through vision signals heavily rely on camera intrinsic parameters, which have long been a subjec…
Violet: A Vision-Language Model for Arabic Image Captioning with Gemini Decoder
Abdelrahman Mohamed, Fakhraddin Alwajih, El Moatez Billah Nagoudi +2
Although image captioning has a vast array of applications, it has not reached its full potential in languages other than English. Arabic, for instance, although the native languag…
D3Former: Debiased Dual Distilled Transformer for Incremental Learning
Abdelrahman Mohamed, Rushali Grandhe, K J Joseph +2
In class incremental learning (CIL) setting, groups of classes are introduced to a model in each learning phase. The goal is to learn a unified model performant on all the classes…