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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |