Search Results for author:
Found 25 papers, 11 papers with code
G4: Grounding-guided Goal-oriented Dialogues Generation with Multiple Documents
Goal-oriented dialogues generation grounded in multiple documents(MultiDoc2Dial) is a challenging and realistic task.
Hybrid Relation Guided Set Matching for Few-shot Action Recognition
To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric.
Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency
In this work, we aim to learn representations by leveraging more abundant information in untrimmed videos.
TCTrack: Temporal Contexts for Aerial Tracking
Temporal contexts among consecutive frames are far from being fully utilized in existing visual trackers.
Does QA-based intermediate training help fine-tuning language models for text classification?
Fine-tuning pre-trained language models for downstream tasks has become a norm for NLP.
Discovery-and-Selection: Towards Optimal Multiple Instance Learning for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) is a challenging task that requires simultaneously learn object classifiers and estimate object locations under the supervision of image category labels.
Multiple Instance Learning
Weakly Supervised Object Detection
TAda! Temporally-Adaptive Convolutions for Video Understanding
This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modelling complex temporal dynamics in videos.
Ranked #31 on
Action Recognition
on Something-Something V2
(using extra training data)
ParamCrop: Parametric Cubic Cropping for Video Contrastive Learning
The visualizations show that ParamCrop adaptively controls the center distance and the IoU between two augmented views, and the learned change in the disparity along the training process is beneficial to learning a strong representation.
Support-Set Based Cross-Supervision for Video Grounding
The contrastive objective aims to learn effective representations by contrastive learning, while the caption objective can train a powerful video encoder supervised by texts.
Exploring Stronger Feature for Temporal Action Localization
Temporal action localization aims to localize starting and ending time with action category.
OadTR: Online Action Detection with Transformers
Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure.
Proposal Relation Network for Temporal Action Detection
We calculate the detection results by assigning the proposals with corresponding classification results.
Ranked #1 on
Temporal Action Localization
on ActivityNet-1.3
Weakly-Supervised Temporal Action Localization Through Local-Global Background Modeling
Then our proposed Local-Global Background Modeling Network (LGBM-Net) is trained to localize instances by using only video-level labels based on Multi-Instance Learning (MIL).
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Relation Modeling in Spatio-Temporal Action Localization
This paper presents our solution to the AVA-Kinetics Crossover Challenge of ActivityNet workshop at CVPR 2021.
Ranked #1 on
Spatio-Temporal Action Localization
on AVA-Kinetics
(using extra training data)
A Stronger Baseline for Ego-Centric Action Detection
This technical report analyzes an egocentric video action detection method we used in the 2021 EPIC-KITCHENS-100 competition hosted in CVPR2021 Workshop.
Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition
In this paper, we present empirical results for training a stronger video vision transformer on the EPIC-KITCHENS-100 Action Recognition dataset.
Self-Supervised Learning for Semi-Supervised Temporal Action Proposal
In this paper, we focus on applying the power of self-supervised methods to improve semi-supervised action proposal generation.
Ranked #11 on
Temporal Action Localization
on ActivityNet-1.3
Self-supervised Motion Learning from Static Images
We furthermore introduce a static mask in pseudo motions to create local motion patterns, which forces the model to additionally locate notable motion areas for the correct classification. We demonstrate that MoSI can discover regions with large motion even without fine-tuning on the downstream datasets.
Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw
With the constrained jigsaw puzzles, instead of solving them directly, which could still be extremely hard, we carefully design four surrogate tasks that are more solvable but meanwhile still ensure that the learned representation is sensitive to spatiotemporal continuity at both the local and global levels.
Tuning the quantumness of simple Bose systems: A universal phase diagram
We present a comprehensive theoretical study of the phase diagram of a system of many Bose particles interacting with a two-body central potential of the so-called Lennard-Jones form.
Statistical Mechanics
Multi-Level Temporal Pyramid Network for Action Detection
By this means, the proposed MLTPN can learn rich and discriminative features for different action instances with different durations.
Less is More: Rejecting Unreliable Reviews for Product Question Answering
In the literature, PQA is formulated as a retrieval problem with the goal to search for the most relevant reviews to answer a given product question.
CBR-Net: Cascade Boundary Refinement Network for Action Detection: Submission to ActivityNet Challenge 2020 (Task 1)
In this report, we present our solution for the task of temporal action localization (detection) (task 1) in ActivityNet Challenge 2020.
Temporal Fusion Network for Temporal Action Localization:Submission to ActivityNet Challenge 2020 (Task E)
This technical report analyzes a temporal action localization method we used in the HACS competition which is hosted in Activitynet Challenge 2020. The goal of our task is to locate the start time and end time of the action in the untrimmed video, and predict action category. Firstly, we utilize the video-level feature information to train multiple video-level action classification models.
TACNet: Transition-Aware Context Network for Spatio-Temporal Action Detection
In this paper, we define these ambiguous samples as "transitional states", and propose a Transition-Aware Context Network (TACNet) to distinguish transitional states.
