56 citations · 73 across the 7 of their papers we have counts for
7 papers
3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding
Lorenzo Pellegrini, Chenchen Zhu, Fanyi Xiao +7
Continual Learning, also known as Lifelong or Incremental Learning, has recently gained renewed interest among the Artificial Intelligence research community. Recent research effor…
Unified Transformer Tracker for Object Tracking
Fan Ma, Mike Zheng Shou, Linchao Zhu +4
As an important area in computer vision, object tracking has formed two separate communities that respectively study Single Object Tracking (SOT) and Multiple Object Tracking (MOT)…
Auto-X3D: Ultra-Efficient Video Understanding via Finer-Grained Neural Architecture Search
Yifan Jiang, Xinyu Gong, Junru Wu +3
Efficient video architecture is the key to deploying video recognition systems on devices with limited computing resources. Unfortunately, existing video architectures are often co…
Searching for Two-Stream Models in Multivariate Space for Video Recognition
Xinyu Gong, Heng Wang, Zheng Shou +3
Conventional video models rely on a single stream to capture the complex spatial-temporal features. Recent work on two-stream video models, such as SlowFast network and AssembleNet…
Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics
Wuyang Chen, Xinyu Gong, Junru Wu +5
This work targets designing a principled and unified training-free framework for Neural Architecture Search (NAS), with high performance, low cost, and in-depth interpretation. NAS…
Multiscale Vision Transformers
Haoqi Fan, Bo Xiong, Karttikeya Mangalam +4
We present Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models. Multisca…
FP-NAS: Fast Probabilistic Neural Architecture Search
Zhicheng Yan, Xiaoliang Dai, Peizhao Zhang +3
Differential Neural Architecture Search (NAS) requires all layer choices to be held in memory simultaneously; this limits the size of both search space and final architecture. In c…