Search Results for author:
Found 6 papers, 3 papers with code
Meta Compositional Referring Expression Segmentation
Then, following a novel meta optimization scheme to optimize the model to obtain good testing performance on the virtual testing sets after training on the virtual training set, our framework can effectively drive the model to better capture semantics and visual representations of individual concepts, and thus obtain robust generalization performance even when handling novel compositions.
Token Boosting for Robust Self-Supervised Visual Transformer Pre-training
Pre-training VTs on such corrupted data can be challenging, especially when we pre-train via the masked autoencoding approach, where both the inputs and masked ``ground truth" targets can potentially be unreliable in this case.
NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions
We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions.
NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions
We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions.
Visual Relation Grounding in Videos
In this paper, we explore a novel task named visual Relation Grounding in Videos (vRGV).
Online Collaborative Learning for Open-Vocabulary Visual Classifiers
Leveraging on the structure of the proposed collaborative learning formulation, we develop an efficient online algorithm that can jointly learn the label embeddings and visual classifiers.
