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Found 16 papers, 6 papers with code
SportQA: A Benchmark for Sports Understanding in Large Language Models
A deep understanding of sports, a field rich in strategic and dynamic content, is crucial for advancing Natural Language Processing (NLP).
Advanced Volleyball Stats for All Levels: Automatic Setting Tactic Detection and Classification with a Single Camera
Our frameworks combine setting ball trajectory recognition with a novel set trajectory classifier to generate comprehensive and advanced statistical data.
Graph Encoding and Neural Network Approaches for Volleyball Analytics: From Game Outcome to Individual Play Predictions
Our results show that the use of GNNs with our graph encoding yields a much more advanced analysis of the data, which noticeably improves prediction results overall.
VREN: Volleyball Rally Dataset with Expression Notation Language
The second goal is to introduce a volleyball descriptive language to fully describe the rally processes in the games and apply the language to our dataset.
Auto-Encoder based Co-Training Multi-View Representation Learning
As we known, auto-encoder is a method of deep learning, which can learn the latent feature of raw data by reconstructing the input, and based on this, we propose a novel algorithm called Auto-encoder based Co-training Multi-View Learning (ACMVL), which utilizes both complementarity and consistency and finds a joint latent feature representation of multiple views.
Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss
But not all of this information is useful for classification tasks.
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.
MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment
In this paper, we present Moment Alignment Network (MAN), a novel framework that unifies the candidate moment encoding and temporal structural reasoning in a single-shot feed-forward network.
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation
Vision-language navigation (VLN) is the task of navigating an embodied agent to carry out natural language instructions inside real 3D environments.
Ranked #2 on
Vision-Language Navigation
on Room2Room
Dynamic Temporal Pyramid Network: A Closer Look at Multi-Scale Modeling for Activity Detection
(3) We further exploit the temporal context of activities by appropriately fusing multi-scale feature maps, and demonstrate that both local and global temporal contexts are important.
S3D: Single Shot multi-Span Detector via Fully 3D Convolutional Networks
In this paper, we present a novel Single Shot multi-Span Detector for temporal activity detection in long, untrimmed videos using a simple end-to-end fully three-dimensional convolutional (Conv3D) network.
No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling
Though impressive results have been achieved in visual captioning, the task of generating abstract stories from photo streams is still a little-tapped problem.
Ranked #13 on
Visual Storytelling
on VIST
Watch, Listen, and Describe: Globally and Locally Aligned Cross-Modal Attentions for Video Captioning
Furthermore, for the first time, we validate the superior performance of the deep audio features on the video captioning task.
Video Captioning via Hierarchical Reinforcement Learning
Video captioning is the task of automatically generating a textual description of the actions in a video.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Deep Reinforcement Learning for Visual Object Tracking in Videos
In this paper we introduce a fully end-to-end approach for visual tracking in videos that learns to predict the bounding box locations of a target object at every frame.
Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer
That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.
