Search Results for author:
Found 18 papers, 9 papers with code
Learning Grounded Vision-Language Representation for Versatile Understanding in Untrimmed Videos
TEG learns to adaptively ground the possible event proposals given a set of sentences by estimating the cross-modal distance in a joint semantic space.
SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-Maximization
Matching-based methods, especially those based on space-time memory, are significantly ahead of other solutions in semi-supervised video object segmentation (VOS).
Ranked #4 on
Semi-Supervised Video Object Segmentation
on MOSE
Semantic Segmentation
Semi-Supervised Video Object Segmentation
+1
VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix
Existing vision-language pre-training (VLP) methods primarily rely on paired image-text datasets, which are either annotated by enormous human labors, or crawled from the internet followed by elaborate data cleaning techniques.
DynaMixer: A Vision MLP Architecture with Dynamic Mixing
In contrast with vision transformers and CNNs, the success of MLP-like models shows that simple information fusion operations among tokens and channels can yield a good representation power for deep recognition models.
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds
To this end, MorphNet jointly optimizes two objectives for sample-adaptive poisoning: a reconstruction loss that preserves the visual similarity between benign / poisoned point clouds, and a classification loss that enforces a modern recognition model of point clouds tends to mis-classify the poisoned sample to a pre-specified target category.
VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples
By empowering the temporal robustness of the encoder and modeling the temporal decay of the keys, our VideoMoCo improves MoCo temporally based on contrastive learning.
Ranked #73 on
Action Recognition
on HMDB-51
Learning Modality Interaction for Temporal Sentence Localization and Event Captioning in Videos
Inspired by the fact that there exist cross-modal interactions in the human brain, we propose a novel method for learning pairwise modality interactions in order to better exploit complementary information for each pair of modalities in videos and thus improve performances on both tasks.
Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction
The task of temporally grounding language queries in videos is to temporally localize the best matched video segment corresponding to a given language (sentence).
MRI Reconstruction Using Deep Bayesian Estimation
Purpose: To develop a deep learning-based Bayesian inference for MRI reconstruction.
Controllable Video Captioning with POS Sequence Guidance Based on Gated Fusion Network
In this paper, we propose to guide the video caption generation with Part-of-Speech (POS) information, based on a gated fusion of multiple representations of input videos.
Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training
Abdominal magnetic resonance imaging (MRI) provides a straightforward way of characterizing tissue and locating lesions of patients as in standard diagnosis.
Hierarchical Photo-Scene Encoder for Album Storytelling
In this paper, we propose a novel model with a hierarchical photo-scene encoder and a reconstructor for the task of album storytelling.
Recurrent Fusion Network for Image Captioning
In this paper, in order to exploit the complementary information from multiple encoders, we propose a novel Recurrent Fusion Network (RFNet) for tackling image captioning.
Learning to Guide Decoding for Image Captioning
Recently, much advance has been made in image captioning, and an encoder-decoder framework has achieved outstanding performance for this task.
Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning
We propose a bidirectional proposal method that effectively exploits both past and future contexts to make proposal predictions.
Regularizing RNNs for Caption Generation by Reconstructing The Past with The Present
Recently, caption generation with an encoder-decoder framework has been extensively studied and applied in different domains, such as image captioning, code captioning, and so on.
Real-Time Neural Style Transfer for Videos
More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames.
Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning
Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions.
