13 citations · 26 across the 8 of their papers we have counts for
8 papers
LoSA: Long-Short-range Adapter for Scaling End-to-End Temporal Action Localization
Akshita Gupta, Gaurav Mittal, Ahmed Magooda +3
Temporal Action Localization (TAL) involves localizing and classifying action snippets in an untrimmed video. The emergence of large video foundation models has led RGB-only video…
Rethinking Multimodal Content Moderation from an Asymmetric Angle with Mixed-modality
Jialin Yuan, Ye Yu, Gaurav Mittal +3
There is a rapidly growing need for multimodal content moderation (CM) as more and more content on social media is multimodal in nature. Existing unimodal CM systems may fail to ca…
BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation
Ye Yu, Jialin Yuan, Gaurav Mittal +2
Video Object Segmentation (VOS) is fundamental to video understanding. Transformer-based methods show significant performance improvement on semi-supervised VOS. However, existing…
GateHUB: Gated History Unit with Background Suppression for Online Action Detection
Junwen Chen, Gaurav Mittal, Ye Yu +2
Online action detection is the task of predicting the action as soon as it happens in a streaming video. A major challenge is that the model does not have access to the future and…
MUSE: Feature Self-Distillation with Mutual Information and Self-Information
Yu Gong, Ye Yu, Gaurav Mittal +2
We present a novel information-theoretic approach to introduce dependency among features of a deep convolutional neural network (CNN). The core idea of our proposed method, called…
Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation
Jay Patravali, Gaurav Mittal, Ye Yu +2
We present MetaUVFS as the first Unsupervised Meta-learning algorithm for Video Few-Shot action recognition. MetaUVFS leverages over 550K unlabeled videos to train a two-stream 2D…
Revisiting Dynamic Convolution via Matrix Decomposition
Yunsheng Li, Yinpeng Chen, Xiyang Dai +7
Recent research in dynamic convolution shows substantial performance boost for efficient CNNs, due to the adaptive aggregation of K static convolution kernels. It has two limitatio…
Stronger NAS with Weaker Predictors
Junru Wu, Xiyang Dai, Dongdong Chen +7
Neural Architecture Search (NAS) often trains and evaluates a large number of architectures. Recent predictor-based NAS approaches attempt to alleviate such heavy computation costs…