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Found 14 papers, 5 papers with code
Perception Tokens Enhance Visual Reasoning in Multimodal Language Models
For example, in a depth-related task, an MLM augmented with perception tokens can reason by generating a depth map as tokens, enabling it to solve the problem effectively.
Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)
Understanding Activities of Daily Living (ADLs) is a crucial step for different applications including assistive robots, smart homes, and healthcare.
Ranked #1 on
Few Shot Action Recognition
on MOMA-LRG
(using extra training data)
Few Shot Action Recognition
Fine-Grained Image Classification
+2
Differentially Private Video Activity Recognition
In recent years, differential privacy has seen significant advancements in image classification; however, its application to video activity recognition remains under-explored.
MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing
Video-language models (VLMs), large models pre-trained on numerous but noisy video-text pairs from the internet, have revolutionized activity recognition through their remarkable generalization and open-vocabulary capabilities.
Ranked #2 on
Few Shot Action Recognition
on MOMA-LRG
(using extra training data)
MOMA: Multi-Object Multi-Actor Activity Parsing
This paper introduces Activity Parsing as the overarching task of temporal segmentation and classification of activities, sub-activities, atomic actions, along with an instance-level understanding of actors, objects, and their relationships in videos.
Scalable Differential Privacy With Sparse Network Finetuning
We propose a novel method for privacy-preserving training of deep neural networks leveraging public, out-domain data.
Vision-Based Gait Analysis for Senior Care
As the senior population rapidly increases, it is challenging yet crucial to provide effective long-term care for seniors who live at home or in senior care facilities.
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
We present an unsupervised learning framework for simultaneously training single-view depth prediction and optical flow estimation models using unlabeled video sequences.
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks
We propose a framework that learns a representation transferable across different domains and tasks in a data efficient manner.
Label Efficient Learning of Transferable Representations across Domains and Tasks
We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner.
Graph Distillation for Action Detection with Privileged Modalities
We propose a technique that tackles action detection in multimodal videos under a realistic and challenging condition in which only limited training data and partially observed modalities are available.
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance
One in twenty-five patients admitted to a hospital will suffer from a hospital acquired infection.
Unsupervised Learning of Long-Term Motion Dynamics for Videos
We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos.
Towards Viewpoint Invariant 3D Human Pose Estimation
We propose a viewpoint invariant model for 3D human pose estimation from a single depth image.
Ranked #4 on
Pose Estimation
on ITOP top-view
