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Found 8 papers, 5 papers with code
Generalizable Entity Grounding via Assistance of Large Language Model
In this work, we propose a novel approach to densely ground visual entities from a long caption.
Text-Driven Image Editing via Learnable Regions
Language has emerged as a natural interface for image editing.
Video Salient Object Detection via Contrastive Features and Attention Modules
Video salient object detection aims to find the most visually distinctive objects in a video.
End-to-end Multi-modal Video Temporal Grounding
Specifically, we adopt RGB images for appearance, optical flow for motion, and depth maps for image structure.
Understanding Synonymous Referring Expressions via Contrastive Features
While prior work usually treats each sentence and attends it to an object separately, we focus on learning a referring expression comprehension model that considers the property in synonymous sentences.
Regularizing Meta-Learning via Gradient Dropout
With the growing attention on learning-to-learn new tasks using only a few examples, meta-learning has been widely used in numerous problems such as few-shot classification, reinforcement learning, and domain generalization.
Referring Expression Object Segmentation with Caption-Aware Consistency
To this end, we propose an end-to-end trainable comprehension network that consists of the language and visual encoders to extract feature representations from both domains.
Ranked #19 on
Referring Expression Segmentation
on RefCOCO testB
Unseen Object Segmentation in Videos via Transferable Representations
The entire process is decomposed into two tasks: 1) solving a submodular function for selecting object-like segments, and 2) learning a CNN model with a transferable module for adapting seen categories in the source domain to the unseen target video.
