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Found 18 papers, 3 papers with code
SAUGE: Taming SAM for Uncertainty-Aligned Multi-Granularity Edge Detection
Edge labels are typically at various granularity levels owing to the varying preferences of annotators, thus handling the subjectivity of per-pixel labels has been a focal point for edge detection.
TechCoach: Towards Technical Keypoint-Aware Descriptive Action Coaching
To bridge this gap, we investigate a new task termed Descriptive Action Coaching (DAC) which requires a model to provide detailed commentary on what is done well and what can be improved beyond a quality score from an action execution.
SynopGround: A Large-Scale Dataset for Multi-Paragraph Video Grounding from TV Dramas and Synopses
Based on the dataset, we further introduce a more complex setting of video grounding dubbed Multi-Paragraph Video Grounding (MPVG), which takes as input multiple paragraphs and a long video for grounding each paragraph query to its temporal interval.
Progressive Pretext Task Learning for Human Trajectory Prediction
In the final stage, the model aims to address the entire future trajectory task by taking full advantage of the knowledge from previous stages.
Ranked #3 on
Trajectory Prediction
on Stanford Drone
Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels
This paper focuses on open-ended video question answering, which aims to find the correct answers from a large answer set in response to a video-related question.
Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding
Different from previous weakly-supervised grounding frameworks based on multiple instance learning or reconstruction learning for two-stage candidate ranking, we propose a novel siamese learning framework that jointly learns the cross-modal feature alignment and temporal coordinate regression without timestamp labels to achieve concise one-stage localization for WSVPG.
Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model
Universal image restoration is a practical and potential computer vision task for real-world applications.
Collaborative Static and Dynamic Vision-Language Streams for Spatio-Temporal Video Grounding
The static stream performs cross-modal understanding in a single frame and learns to attend to the target object spatially according to intra-frame visual cues like object appearances.
STVGFormer: Spatio-Temporal Video Grounding with Static-Dynamic Cross-Modal Understanding
The static branch performs cross-modal understanding in a single frame and learns to localize the target object spatially according to intra-frame visual cues like object appearances.
Ranked #2 on
Spatio-Temporal Video Grounding
on HC-STVG2
Action-guided 3D Human Motion Prediction
The ability of forecasting future human motion is important for human-machine interaction systems to understand human behaviors and make interaction.
Augmented 2D-TAN: A Two-stage Approach for Human-centric Spatio-Temporal Video Grounding
We propose an effective two-stage approach to tackle the problem of language-based Human-centric Spatio-Temporal Video Grounding (HC-STVG) task.
Predictive Feature Learning for Future Segmentation Prediction
In the latent feature learned by the autoencoder, global structures are enhanced and local details are suppressed so that it is more predictive.
Improving Fast Segmentation With Teacher-student Learning
Recently, segmentation neural networks have been significantly improved by demonstrating very promising accuracies on public benchmarks.
Deep Bilinear Learning for RGB-D Action Recognition
In this paper, we focus on exploring modality-temporal mutual information for RGB-D action recognition.
Early action prediction by soft regression
Our formulation of soft regression framework 1) overcomes a usual assumption in existing early action prediction systems that the progress level of on-going sequence is given in the testing stage; and 2) presents a theoretical framework to better resolve the ambiguity and uncertainty of subsequences at early performing stage.
Ranked #80 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Latent Embeddings for Collective Activity Recognition
Rather than simply recognizing the action of a person individually, collective activity recognition aims to find out what a group of people is acting in a collective scene.
Jointly learning heterogeneous features for rgb-d activity recognition
The proposed model formed in a unified framework is capable of: 1) jointly mining a set of subspaces with the same dimensionality to exploit latent shared features across different feature channels, 2) meanwhile, quantifying the shared and feature-specific components of features in the subspaces, and 3) transferring feature-specific intermediate transforms (i-transforms) for learning fusion of heterogeneous features across datasets.
Ranked #8 on
Skeleton Based Action Recognition
on SYSU 3D
