Update README.md

README.md

@@ -1,4 +1,62 @@
-# gtad.pth
+# G-TAD
 The official implementation of G-TAD: Sub-Graph Localization for Temporal Action Detection
 
-place holder
+## Update
+30 Mar 2020: THUMOS14 feature is available! Gooogle Drive [Link](https://drive.google.com/drive/folders/10PGPMJ9JaTZ18uakPgl58nu7yuKo8M_k?usp=sharing).
+
+## Overview
+Temporal action detection is a fundamental yet challenging task in video understanding. Video context is a critical cue to effectively detect actions, but current works mainly focus on temporal context, while neglecting semantic context as well as other important context properties. In this work, we propose a graph convolutional network (GCN) model to adaptively incorporate  multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem. Specifically, we formulate video snippets as graph nodes, snippet-snippet correlations as edges, and actions associated with context as target sub-graphs. With graph convolution as the basic operation, we design a GCN block called GCNeXt, which learns the features of each node by aggregating its context and dynamically updates the edges in the graph. To localize each sub-graph, we also design a SGAlign layer to embed each sub-graph into the Euclidean space. Extensive experiments show that G-TAD is capable of finding effective video context without extra supervision and achieves state-of-the-art performance on two detection benchmarks. On ActityNet-1.3, we obtain an average mAP of 34.09%; on THUMOS14, we obtain 40.16% in mAP@0.5, beating all the other one-stage methods.
+
+[Detail](https://sites.google.com/kaust.edu.sa/g-tad), [Video](https://www.youtube.com/watch?v=BlPxnDcykUo), [Arxiv](https://arxiv.org/abs/1911.11462).
+
+## Dependencies 
+* Python == 3.7
+* Pytorch==1.1.0
+* CUDA==10.0.130
+* CUDNN==7.5.1_0
+
+## Installation
+1. Create conda environment
+    ```shell script
+    conda create -f env.yml
+    ```
+2. Install `Align1D2.2.0` 
+    ```shell script
+    cd lib
+    python setup.py install
+    ```
+3. Test `Align1D2.2.0`
+    ```shell script
+    python align.py
+    ```
+## Code Architecture
+
+    gtad                        # this repo
+    ├── data                    # feature and label
+    ├── evaluation              # evaluation code from offical API
+    ├── gtad_lib                # gtad library
+    └── ...
+
+## Train and evaluation
+```shell script
+python gtad.py --mode train
+python gtad.py --mode inference
+python gtad.py --mode detect
+```
+or
+```shell script
+bash gtad.sh | tee log.txt
+```
+## Bibtex
+```text
+@misc{xu2019gtad,
+    title={G-TAD: Sub-Graph Localization for Temporal Action Detection},
+    author={Mengmeng Xu and Chen Zhao and David S. Rojas and Ali Thabet and Bernard Ghanem},
+    year={2019},
+    eprint={1911.11462},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+## Contact
+mengmeng.xu[at]kaust.edu.sa