Search Results for author:
Found 11 papers, 6 papers with code
DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations
Solving multi-label recognition (MLR) for images in the low-label regime is a challenging task with many real-world applications.
Dynamic Network Quantization for Efficient Video Inference
Deep convolutional networks have recently achieved great success in video recognition, yet their practical realization remains a challenge due to the large amount of computational resources required to achieve robust recognition.
AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition
Specifically, given a video segment, a multi-modal policy network is used to decide what modalities should be used for processing by the recognition model, with the goal of improving both accuracy and efficiency.
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths
Second, to effectively transfer knowledge, we develop a dynamic block swapping method by randomly replacing the blocks in the lower-precision student network with the corresponding blocks in the higher-precision teacher network.
Revisiting Few-shot Activity Detection with Class Similarity Control
In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection based on proposal regression which detects the start and end time of the activities in untrimmed videos.
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Multi-task learning is an open and challenging problem in computer vision.
Ranked #64 on
Semantic Segmentation
on NYU Depth v2
Weakly-supervised Compositional FeatureAggregation for Few-shot Recognition
Learning from a few examples is a challenging task for machine learning.
Domain Agnostic Learning with Disentangled Representations
Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains.
Ranked #3 on
Multi-target Domain Adaptation
on DomainNet
Similarity R-C3D for Few-shot Temporal Activity Detection
In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection which detects the start and end time of the few-shot input activities in an untrimmed video.
TwoStreamVAN: Improving Motion Modeling in Video Generation
Video generation is an inherently challenging task, as it requires modeling realistic temporal dynamics as well as spatial content.
A Temporally-Aware Interpolation Network for Video Frame Inpainting
We propose the first deep learning solution to video frame inpainting, a challenging instance of the general video inpainting problem with applications in video editing, manipulation, and forensics.
