Search Results for author:
Found 13 papers, 4 papers with code
Voila-A: Aligning Vision-Language Models with User's Gaze Attention
In this paper, we introduce gaze information, feasibly collected by AR or VR devices, as a proxy for human attention to guide VLMs and propose a novel approach, Voila-A, for gaze alignment to enhance the interpretability and effectiveness of these models in real-world applications.
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain.
GroundNLQ @ Ego4D Natural Language Queries Challenge 2023
Motivated by this, we leverage a two-stage pre-training strategy to train egocentric feature extractors and the grounding model on video narrations, and further fine-tune the model on annotated data.
Two-shot Video Object Segmentation
The underlying idea is to generate pseudo labels for unlabeled frames during training and to optimize the model on the combination of labeled and pseudo-labeled data.
An Efficient COarse-to-fiNE Alignment Framework @ Ego4D Natural Language Queries Challenge 2022
This technical report describes the CONE approach for Ego4D Natural Language Queries (NLQ) Challenge in ECCV 2022.
HORIZON: High-Resolution Semantically Controlled Panorama Synthesis
Panorama synthesis endeavors to craft captivating 360-degree visual landscapes, immersing users in the heart of virtual worlds.
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding
This paper tackles an emerging and challenging problem of long video temporal grounding~(VTG) that localizes video moments related to a natural language (NL) query.
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention
A key feature of the multi-label setting is that images often have multiple labels, which typically refer to different regions of the image.
Control Image Captioning Spatially and Temporally
Moreover, the controllability and explainability of LoopCAG are validated by analyzing spatial and temporal sensitivity during the generation process.
Ranked #1 on
Image Captioning
on Localized Narratives
CETransformer: Casual Effect Estimation via Transformer Based Representation Learning
Treatment effect estimation, which refers to the estimation of causal effects and aims to measure the strength of the causal relationship, is of great importance in many fields but is a challenging problem in practice.
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning
While the use of class names has already been explored in previous work, our approach differs in two key aspects.
Few-shot Image Classification with Multi-Facet Prototypes
The aim of few-shot learning (FSL) is to learn how to recognize image categories from a small number of training examples.
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
When the scaling factor is set to zero, it is equivalent to removing the corresponding filter.
