Search Results for author:
Found 19 papers, 12 papers with code
GEB+: A benchmark for generic event boundary captioning, grounding and text-based retrieval
Cognitive science has shown that humans perceive videos in terms of events separated by state changes of dominant subjects.
Unified Transformer Tracker for Object Tracking
Although UniTrack \cite{wang2021different} demonstrates that a shared appearance model with multiple heads can be used to tackle individual tracking tasks, it fails to exploit the large-scale tracking datasets for training and performs poorly on single object tracking.
Revitalize Region Feature for Democratizing Video-Language Pre-training
Recent dominant methods for video-language pre-training (VLP) learn transferable representations from the raw pixels in an end-to-end manner to achieve advanced performance on downstream video-language tasks.
All in One: Exploring Unified Video-Language Pre-training
In this work, we for the first time introduce an end-to-end video-language model, namely \textit{all-in-one Transformer}, that embeds raw video and textual signals into joint representations using a unified backbone architecture.
Ranked #1 on
TGIF-Action
on TGIF-QA
AssistQ: Affordance-centric Question-driven Task Completion for Egocentric Assistant
In this paper, we define a new task called Affordance-centric Question-driven Task Completion, where the AI assistant should learn from instructional videos and scripts to guide the user step-by-step.
Contrastive Learning of Semantic and Visual Representations for Text Tracking
Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video.
Video-Text Pre-training with Learned Regions
Video-Text pre-training aims at learning transferable representations from large-scale video-text pairs via aligning the semantics between visual and textual information.
Object-aware Video-language Pre-training for Retrieval
In this work, we present Object-aware Transformers, an object-centric approach that extends video-language transformer to incorporate object representations.
AssistSR: Task-oriented Question-driven Video Segment Retrieval
In contrast, we present a new task called Task-oriented Question-driven Video Segment Retrieval (TQVSR).
AVA-AVD: Audio-visual Speaker Diarization in the Wild
To create a testbed that can effectively compare diarization methods on videos in the wild, we annotate the speaker diarization labels on the AVA movie dataset and create a new benchmark called AVA-AVD.
MorphMLP: A Self-Attention Free, MLP-Like Backbone for Image and Video
Self-attention has become an integral component of the recent network architectures, e. g., Transformer, that dominate major image and video benchmarks.
Ranked #11 on
Action Recognition
on Something-Something V2
(using extra training data)
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
On Pursuit of Designing Multi-modal Transformer for Video Grounding
Almost all existing video grounding methods fall into two frameworks: 1) Top-down model: It predefines a set of segment candidates and then conducts segment classification and regression.
Deep Motion Prior for Weakly-Supervised Temporal Action Localization
In this paper, we analyze that the motion cues behind the optical flow features are complementary informative.
Optical Flow Estimation
Weakly-supervised Temporal Action Localization
+1
Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection
Active speaker detection (ASD) seeks to detect who is speaking in a visual scene of one or more speakers.
NUS-HLT Report for ActivityNet Challenge 2021 AVA (Speaker)
Active speaker detection (ASD) seeks to detect who is speaking in a visual scene of one or more speakers.
Generic Event Boundary Detection: A Benchmark for Event Segmentation
This paper presents a novel task together with a new benchmark for detecting generic, taxonomy-free event boundaries that segment a whole video into chunks.
Channel Augmented Joint Learning for Visible-Infrared Recognition
This paper introduces a powerful channel augmented joint learning strategy for the visible-infrared recognition problem.
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
We propose to explicitly model the Actor-Context-Actor Relation, which is the relation between two actors based on their interactions with the context.
Ranked #1 on
Action Recognition
on AVA v2.1
