Search Results for author:
Found 18 papers, 8 papers with code
Superior and Pragmatic Talking Face Generation with Teacher-Student Framework
Talking face generation technology creates talking videos from arbitrary appearance and motion signal, with the "arbitrary" offering ease of use but also introducing challenges in practical applications.
RLIPv2: Fast Scaling of Relational Language-Image Pre-training
In this paper, we propose RLIPv2, a fast converging model that enables the scaling of relational pre-training to large-scale pseudo-labelled scene graph data.
Ranked #1 on
Zero-Shot Human-Object Interaction Detection
on HICO-DET
(using extra training data)
ViM: Vision Middleware for Unified Downstream Transferring
ViM consists of a zoo of lightweight plug-in modules, each of which is independently learned on a midstream dataset with a shared frozen backbone.
VoP: Text-Video Co-operative Prompt Tuning for Cross-Modal Retrieval
Many recent studies leverage the pre-trained CLIP for text-video cross-modal retrieval by tuning the backbone with additional heavy modules, which not only brings huge computational burdens with much more parameters, but also leads to the knowledge forgetting from upstream models.
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new categories.
RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection
The task of Human-Object Interaction (HOI) detection targets fine-grained visual parsing of humans interacting with their environment, enabling a broad range of applications.
Ranked #16 on
Human-Object Interaction Detection
on HICO-DET
Rethinking Supervised Pre-training for Better Downstream Transferring
Though for most cases, the pre-training stage is conducted based on supervised methods, recent works on self-supervised pre-training have shown powerful transferability and even outperform supervised pre-training on multiple downstream tasks.
NGC: A Unified Framework for Learning with Open-World Noisy Data
The existence of noisy data is prevalent in both the training and testing phases of machine learning systems, which inevitably leads to the degradation of model performance.
Ranked #18 on
Image Classification
on mini WebVision 1.0
Exploring Stronger Feature for Temporal Action Localization
Temporal action localization aims to localize starting and ending time with action category.
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 Learning
Weakly-supervised Temporal Action Localization
+1
Proposal Relation Network for Temporal Action Detection
We calculate the detection results by assigning the proposals with corresponding classification results.
Ranked #2 on
Temporal Action Localization
on ActivityNet-1.3
(using extra training data)
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 #4 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 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.
Incremental Learning on Growing Graphs
To tackle this problem, we introduce incremental graph learning (IGL), a general framework to formulate the learning on growing graphs in an incremental manner, where traditional graph learning method could be deployed as a basic model.
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.
DHGNN: Dynamic Hypergraph Neural Networks
Then hypergraph convolution is introduced to encode high-order data relations in a hypergraph structure.
