Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Real-Time Object Tracking via Meta-Learning: Efficient Model Adaptation and One-Shot Channel Pruning
Authors: Ilchae Jung, Kihyun You, Hyeonwoo Noh, Minsu Cho, Bohyung Han11205-11212
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation on the standard datasets demonstrates its outstanding accuracy and speed compared to the state-of-the-art methods. |
| Researcher Affiliation | Academia | Ilchae Jung,1,2 Kihyun You,1 Hyeonwoo Noh,1,2 Minsu Cho,1 Bohyung Han2 1Computer Vision Lab., POSTECH, Korea 2Computer Vision Lab. ECE, & ASRI, Seoul National University, Korea EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Meta-Learning for Fast Adaptation |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We pretrain Meta RTT and Meta RTT+Prune on Image Net Vid (Russakovsky et al. 2015), which contains more than 3,000 videos with 30 object classes labeled for video object detection. [...] We pretrain Meta RTT+COCO on Image Net-Vid and the augmented version of COCO (Lin et al. 2014). |
| Dataset Splits | Yes | We randomly select 6 frames from a single video to construct an episode, and use the ๏ฌrst frame for Dinit, the last frame for Dstd test and the remaining frames for Don. |
| Hardware Specification | Yes | Our algorithm is implemented in Py Torch with 3.60 GHz Intel Core I7-6850k and NVIDIA Titan Xp Pascal GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number for this or any other software dependency. |
| Experiment Setup | Yes | We set Kinit and Kon to 5 throughout our experiment. The meta-parameters are optimized over 40K simulated episodes using ADAM with ๏ฌxed learning rate 10 4. [...] We optimize the network by ADAM for 30K iterations with learning rate 5 10 5. |