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Found 12 papers, 8 papers with code
Hierarchical Novelty Detection for Visual Object Recognition
The essential ingredients of our methods are confidence-calibrated classifiers, data relabeling, and the leave-one-out strategy for modeling novel classes under the hierarchical taxonomy.
TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection
It consists of two building blocks: first, the encoder network extracts low-resolution spatiotemporal features from an input clip of several consecutive frames, and then the following prediction network decodes the encoded features spatially while aggregating all the temporal information.
Adversarial Background-Aware Loss for Weakly-supervised Temporal Activity Localization
Two triplets of the feature space are considered in our approach: one triplet is used to learn discriminative features for each activity class, and the other one is used to distinguish the features where no activity occurs (i. e. background features) from activity-related features for each video.
Integrating Human Gaze into Attention for Egocentric Activity Recognition
In addition, we model the distribution of gaze fixations using a variational method.
Ranked #2 on
Egocentric Activity Recognition
on EGTEA
Learning Spatial-Temporal Graphs for Active Speaker Detection
We address the problem of active speaker detection through a new framework, called SPELL, that learns long-range multimodal graphs to encode the inter-modal relationship between audio and visual data.
Learning Long-Term Spatial-Temporal Graphs for Active Speaker Detection
Active speaker detection (ASD) in videos with multiple speakers is a challenging task as it requires learning effective audiovisual features and spatial-temporal correlations over long temporal windows.
Ranked #1 on
Node Classification
on AVA
Intel Labs at Ego4D Challenge 2022: A Better Baseline for Audio-Visual Diarization
This report describes our approach for the Audio-Visual Diarization (AVD) task of the Ego4D Challenge 2022.
Unbiased Scene Graph Generation in Videos
The task of dynamic scene graph generation (SGG) from videos is complicated and challenging due to the inherent dynamics of a scene, temporal fluctuation of model predictions, and the long-tailed distribution of the visual relationships in addition to the already existing challenges in image-based SGG.
SViTT: Temporal Learning of Sparse Video-Text Transformers
Do video-text transformers learn to model temporal relationships across frames?
Ranked #4 on
Video Question Answering
on AGQA 2.0 balanced
(Average Accuracy metric)
WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models
The rapid advancement of generative models, facilitating the creation of hyper-realistic images from textual descriptions, has concurrently escalated critical societal concerns such as misinformation.
STHG: Spatial-Temporal Heterogeneous Graph Learning for Advanced Audio-Visual Diarization
This report introduces our novel method named STHG for the Audio-Visual Diarization task of the Ego4D Challenge 2023.
Action Scene Graphs for Long-Form Understanding of Egocentric Videos
We present Egocentric Action Scene Graphs (EASGs), a new representation for long-form understanding of egocentric videos.
