Search Results for author:
Found 17 papers, 4 papers with code
MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction
We present Mesh Pre-Training (MPT), a new pre-training framework that leverages 3D mesh data such as MoCap data for human pose and mesh reconstruction from a single image.
LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling
In this work, we explore a unified VidL framework LAVENDER, where Masked Language Modeling (MLM) is used as the common interface for all pre-training and downstream tasks.
Cross-modal Representation Learning for Zero-shot Action Recognition
The model design provides a natural mechanism for visual and semantic representations to be learned in a shared knowledge space, whereby it encourages the learned visual embedding to be discriminative and more semantically consistent.
Ranked #2 on
Zero-Shot Action Recognition
on ActivityNet
Multi-Mode Spatial Signal Processor with Rainbow-like Fast Beam Training and Wideband Communications using True-Time-Delay Arrays
Initial access in millimeter-wave (mmW) wireless is critical toward successful realization of the fifth-generation (5G) wireless networks and beyond.
Wideband Beamforming with Rainbow Beam Training using Reconfigurable True-Time-Delay Arrays for Millimeter-Wave Wireless
The decadal research in integrated true-time-delay arrays have seen organic growth enabling realization of wideband beamformers for large arrays with wide aperture widths.
SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning
Based on this model architecture, we show that video captioning can benefit significantly from more densely sampled video frames as opposed to previous successes with sparsely sampled video frames for video-and-language understanding tasks (e. g., video question answering).
Mutual Information Continuity-constrained Estimator
Accordingly, we propose Mutual Information Continuity-constrained Estimator (MICE).
A 4-Element 800MHz-BW 29mW True-Time-Delay Spatial Signal Processor Enabling Fast Beam-Training with Data Communications
In this work, we demonstrate a true-time-delay (TTD) array with digitally reconfigurable delay elements enabling both fast beam-training at the receiver with wideband data communications.
VA-RED$^2$: Video Adaptive Redundancy Reduction
An inherent property of real-world videos is the high correlation of information across frames which can translate into redundancy in either temporal or spatial feature maps of the models, or both.
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
Temporal modelling is the key for efficient video action recognition.
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
Specifically, given a video frame, a policy network is used to decide what input resolution should be used for processing by the action recognition model, with the goal of improving both accuracy and efficiency.
True-Time-Delay Arrays for Fast Beam Training in Wideband Millimeter-Wave Systems
We also propose a suitable algorithm that requires a single pilot to achieve high-accuracy estimation of angle of arrival.
Video Instance Segmentation Tracking With a Modified VAE Architecture
We propose a modified variational autoencoder (VAE) architecture built on top of Mask R-CNN for instance-level video segmentation and tracking.
A Prior-Less Method for Multi-Face Tracking in Unconstrained Videos
This paper presents a prior-less method for tracking and clustering an unknown number of human faces and maintaining their individual identities in unconstrained videos.
Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Images
Quantitative relevance of results, according to non-expert similarity, as well as localized image regions, are also significantly improved.
Distributed Bundle Adjustment
The runtime of our implementation scales linearly with the number of observed points.
Adaptive As-Natural-As-Possible Image Stitching
Computing the warp is fully automated and uses a combination of local homography and global similarity transformations, both of which are estimated with respect to the target.
