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].
TrackIME: Enhanced Video Point Tracking via Instance Motion Estimation
Authors: Seong Hyeon Park, Huiwon Jang, Byungwoo Jeon, Sukmin Yun, Paul Hongsuck Seo, Jinwoo Shin
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For example, on the recent TAP-Vid benchmark, our framework consistently improves all baselines, e.g., up to 13.5% improvement on the average Jaccard metric. |
| Researcher Affiliation | Academia | 1KAIST 2Hanyang University ERICA 3Korea University EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper provides a workflow diagram (Figure 1) and mathematical formulations, but no explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The open-source version of Track IME is available at https://github.com/kami93/trackime. |
| Open Datasets | Yes | We evaluate these models on three different datasets, DAVIS [12], Kinetics [34], and RGBStacking [33], each representing different characteristics. |
| Dataset Splits | No | The paper mentions using 'validation' and 'test-dev' sets for the zero-shot benchmark ('In particular, we use the validation and the test-dev sets for the zero-shot benchmark.') but does not specify the splits (e.g., percentages, counts, or explicit splitting methodology) for training, validation, and test datasets needed for reproduction. It refers to established datasets like DAVIS, which have predefined splits, but doesn't explicitly state their use of those splits or how their data was partitioned if custom. |
| Hardware Specification | Yes | Every baseline model and internal module in Track IME (e.g., Segment Anything [1]) is implemented in Py Torch 2.1 [32] compiled for CUDA 11.8, which we run on an NVIDIA RTX 3090 GPU. |
| Software Dependencies | Yes | Every baseline model and internal module in Track IME (e.g., Segment Anything [1]) is implemented in Py Torch 2.1 [32] compiled for CUDA 11.8, which we run on an NVIDIA RTX 3090 GPU. |
| Experiment Setup | Yes | For example, we choose the hyperparameters for each baseline, e.g., progressive inference steps K = 2, and the pruning sizes H0 = W0 = 960 and H1 = W1 = 384 when incorporated with TAPIR [6]. |