diff --git a/README.md b/README.md index 07c1885..f7fe5c7 100644 --- a/README.md +++ b/README.md @@ -134,6 +134,12 @@ python demo/demo_automatic.py --chunk_size 4 \ 2. [Running DEVA with detections to reproduce the benchmark results.](docs/EVALUATION.md) 3. [Training the DEVA model.](docs/TRAINING.md) +## Limitation + +- On closed-set data, DEVA most likely does not work as well as end-to-end approaches. Joint training is (for now) still a better idea when you have enough target data. +- Positive detections are amplified temporally due to propagation. Having a detector with a lower false positive rate (i.e., a higher threshold) helps. +- If new objects are coming in and out all the time (e.g., in driving scenes), we will keep a lot of objects in the memory bank which unfortunately increases the false positive rate. Decreasing `max_missed_detection_count` might help since we delete objects from memory more eagerly. +