Search Results for author:
Found 5 papers, 2 papers with code
SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentation
In-context segmentation aims at segmenting novel images using a few labeled example images, termed as "in-context examples", exploring content similarities between examples and the target.
To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning
Existing visual instruction tuning methods typically prompt large language models with textual descriptions to generate instruction-following data.
Ranked #32 on
Visual Question Answering
on MM-Vet
Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding
Detection Hub further achieves SoTA performance on UODB benchmark with wide variety of datasets.
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition
To this end, we introduce AdaViT, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition.
A Multimodal Framework for Video Ads Understanding
There is a growing trend in placing video advertisements on social platforms for online marketing, which demands automatic approaches to understand the contents of advertisements effectively.
