References

https://github.com/ayaabdelsalam91/Input-Cell-Attention
https://github.com/pranoyr/cnn-lstm

Introduction

As deep learning models become more commonplace, there is a rising need to accurately interpret them. One way to do this involves the usage of different types of saliency methods. While saliency has been well explored on single sample data in vision and language, the applicability of these meth- ods in analyzing sequential or time-series data remains relatively unexplored. In our work, we look at tackling this problem of interpretability of sequential data, employing saliency approaches. In particular, we focus on interpreting real world videos, which poses its own set of challenges. Our results show a heavy computation load, demon- strating the need for more efficient algorithms for computing sequential saliency.

Problem Definition and Setup

We formulate our approach as a binary classification prob- lem and choose the two classes ‘walking’ and ‘running’ from the KTH action recognition Dataset (Schuldt et al., 2004). All classes in the KTH dataset have the same individ- uals performing different actions in a similar background. We argue that this leads to the classifier relying more heavily on causal features to make accurate decisions. In contrast choosing two classes such as ’playing badminton’ vs ’play- ing the piano’ could result in the classifier relying on spuri- ous features (background). ’Walking and ’running’ in partic- ular have high visual similarity but have different temporal characteristics. Combining these arguments we hypothesize that by choosing these two specific classes the saliency maps will closely resemble the optical flow. As optical flow can be easily calculated this provides a good baseline for the qualitative evaluation of the generated saliency maps.

Dataset

File structure

.
├── data
│   ├── raw_dataset
│   │   ├── kth_data (copy the walking and running data here)
│   │   │   ├── running 
│   │   │   ├── walking
│   ├── kth
│   │   ├── annotations 
│   │   ├── image_data
│   │   │   ├── running
│   │   │   ├── walking
│   ├── kth_trimmed
│   │   ├── running 
│   │   ├── walking
├── model
├── utils
└── other .py files

Data generation

Download

• The dataset can be downloaded from . We are using only the walking and running classes. Copy it to the raw_data folder or follow the following steps.
  mkdir data
cd data
mkdir kth
mkdir raw_dataset cd raw_dataset
wget http://www.nada.kth.se/cvap/actions/walking.zip
wget http://www.nada.kth.se/cvap/actions/running.zip

Trimming the videos

• Run the following command from the root directory
  python3 utils/trim_videos.py

Data preperation

• Run the followig command
  ./utils/generate_data.sh

Download pre processed data

wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1sMn_BGhqmGdgZ0JKlodBwVB0Z5ppNqi_' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1sMn_BGhqmGdgZ0JKlodBwVB0Z5ppNqi_" -O data.zip && rm -rf /tmp/cookies.txt
unzip data.zip

Models

The pretrained models can be downloaded from .

Problem Definition and Setup

We formulate our approach as a binary classification prob- lem and choose the two classes ‘walking’ and ‘running’ from the KTH action recognition Dataset (Schuldt et al., 2004). All classes in the KTH dataset have the same individ- uals performing different actions in a similar background. We argue that this leads to the classifier relying more heavily on causal features to make accurate decisions. In contrast choosing two classes such as ’playing badminton’ vs ’play- ing the piano’ could result in the classifier relying on spuri- ous features (background). ’Walking and ’running’ in partic- ular have high visual similarity but have different temporal characteristics. Combining these arguments we hypothesize that by choosing these two specific classes the saliency maps will closely resemble the optical flow. As optical flow can be easily calculated this provides a good baseline for the qualitative evaluation of the generated saliency maps.

Dataset

File structure

.
├── data
│   ├── raw_dataset
│   │   ├── kth_data (copy the walking and running data here)
│   │   │   ├── running 
│   │   │   ├── walking
│   ├── kth
│   │   ├── annotations 
│   │   ├── image_data
│   │   │   ├── running
│   │   │   ├── walking
│   ├── kth_trimmed
│   │   ├── running 
│   │   ├── walking
├── model
├── utils
└── other .py files

Data generation

Download

• The dataset can be downloaded from . We are using only the walking and running classes.
• Copy the data inside the raw_data folder. Refer file structure above.

Trimming the videos

• Run the following command from the root directory
  python3 utils/trim_videos.py

Data preperation

• Run the followig command
  ./utils/generate_data.sh

How to run code

Train

python3 main.py --use_cuda --gpu 0 --batch_size 8 --n_epochs 50 --num_workers 0 --annotation_path ./data/kth/annotation/kth1.json --video_path ./data/kth/image_data/ --dataset kth --sample_size 150 --lr_rate 1e-4 --n_classes 2

Test for video classification output

python3 inference.py --annotation_path ./data/kth/annotation/kth1.json --dataset kth --model cnnlstm --n_classes 2 --resume_path <model_path>

Get frame Saliency

If you want to use TSR, set a flag to true in frame_saliency.py
python3 frame_saliency.py --annotation_path ./data/kth/annotation/kth1.json --dataset kth --model cnnlstm --n_classes 2 --resume_path <model_path>

Get feature temporal Saliency

python3 feature_saliency.py --annotation_path ./data/kth/annotation/kth1.json --dataset kth --model cnnlstm --n_classes 2 --resume_path <model_path>

Results

Temporal saliency

Feature saliency