Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

The size of vision models has grown exponentially over the last few years, especially after
the emergence of Vision Transformer. This has motivated the development of parameter …

Recent advances in detecting arbitrary objects in the real world are trained and evaluated
on object detection datasets with a relatively restricted vocabulary. To facilitate the …

We present T-Rex2, a highly practical model for open-set object detection. Previous open-
set object detection methods relying on text prompts effectively encapsulate the abstract …

Transformers have revolutionized computer vision and natural language processing, but
their high computational complexity limits their application in high-resolution image …

Though impressive performance has been achieved in specific visual realms (eg faces,
dogs, and places), an omni-vision representation generalizing to many natural visual …

Texture analysis is a classical yet challenging task in computer vision for which deep neural
networks are actively being applied. Most approaches are based on building feature …

OCTAVIUS: MITIGATING TASK INTERFERENCE IN MLLMS VIA MOE Page 1 OCTAVIUS:
MITIGATING TASK INTERFERENCE IN MLLMS VIA MOE Zeren Chen1,2*, Ziqin Wang1,3*, Zhen …

Real world data often exhibits a long-tailed and open-ended (ie, with unseen classes)
distribution. A practical recognition system must balance between majority (head) and …

Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting
with visual content with myriad potential downstream tasks. However, even though a list of …