Search Results for author:
Found 10 papers, 8 papers with code
Revitalize Region Feature for Democratizing Video-Language Pre-training of Retrieval
Recent dominant methods for video-language pre-training (VLP) learn transferable representations from the raw pixels in an end-to-end manner to achieve advanced performance on downstream video-language retrieval.
All in One: Exploring Unified Video-Language Pre-training
In this work, we for the first time introduce an end-to-end video-language model, namely \textit{all-in-one Transformer}, that embeds raw video and textual signals into joint representations using a unified backbone architecture.
Ranked #6 on
TGIF-Transition
on TGIF-QA
(using extra training data)
Video-Text Pre-training with Learned Regions
Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs via aligning the semantics between visual and textual information.
Object-aware Video-language Pre-training for Retrieval
In this work, we present Object-aware Transformers, an object-centric approach that extends video-language transformer to incorporate object representations.
Ranked #20 on
Zero-Shot Video Retrieval
on DiDeMo
Unsupervised Adaptive Semantic Segmentation with Local Lipschitz Constraint
In the first stage, we propose the local Lipschitzness regularization as the objective function to align different domains by exploiting intra-domain knowledge, which explores a promising direction for non-adversarial adaptive semantic segmentation.
Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval with Partial Query
However, the performance of existing methods suffers in real life since the user is likely to provide an incomplete description of an image, which often leads to results filled with false positives that fit the incomplete description.
Contextual Non-Local Alignment over Full-Scale Representation for Text-Based Person Search
Secondly, a BERT with locality-constrained attention is proposed to obtain representations of descriptions at different scales.
Ranked #15 on
Text based Person Retrieval
on CUHK-PEDES
Learning Smooth Representation for Unsupervised Domain Adaptation
When constructing a deep end-to-end model, to ensure the effectiveness and stability of unsupervised domain adaptation, three critical factors are considered in our proposed optimization strategy, i. e., the sample amount of a target domain, dimension and batchsize of samples.
Ranked #1 on
Domain Adaptation
on SVNH-to-MNIST
Virtual Conditional Generative Adversarial Networks
All the generative paths share the same decoder network while in each path the decoder network is fed with a concatenation of a different pre-computed amplified one-hot vector and the inputted Gaussian noise.
Unsupervised Domain Adaptation with Adversarial Residual Transform Networks
Domain adaptation is widely used in learning problems lacking labels.
