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].
ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition
Authors: Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on established background and foreground bias protocols, setting a new state-of-the-art and strongly improving combined debiasing performance by over 12% absolute on HMDB51. Furthermore, we identify an issue of background leakage in the existing UCF101 protocol for bias evaluation which provides a shortcut to predict actions and does not provide an accurate measure of the debiasing capability of a model. We address this issue by proposing more fine-grained segmentation boundaries for the actor, where our method also outperforms existing approaches. |
| Researcher Affiliation | Academia | Joseph Fioresi, Ishan Rajendrakumar Dave, Mubarak Shah Center for Research in Computer Vision, University of Central Florida, Orlando, USA EMAIL, EMAIL |
| Pseudocode | No | The paper describes the methodology using prose and mathematical equations but does not include any distinct pseudocode or algorithm blocks. |
| Open Source Code | No | Project Page: https://joefioresi718.github.io/ALBAR_webpage/. This is a project page, not a direct code repository. The paper does not explicitly state that the source code for the described methodology is released or provide a direct link to a code repository. |
| Open Datasets | Yes | We evaluate our method on established background and foreground bias protocols, setting a new state-of-the-art and strongly improving combined debiasing performance by over 12% absolute on HMDB51. ... SCUBA and SCUFO Li et al. (2023) are background and foreground bias evaluation benchmarks for action recognition based on common benchmarks Kinetics400 Carreira & Zisserman (2017), UCF101 Soomro et al. (2012), and HMDB51 Kuehne et al. (2011). |
| Dataset Splits | Yes | UCF101 Soomro et al. (2012) ... has three train/test splits available. Following Li et al. (2023), we utilize only the first (split 1) train/test split for all training and evaluation in this work. ... HMDB51 Kuehne et al. (2011) ... has three potential train/test splits... we only use the first (split 1) train/test split for all training and evaluation in this work. ... Kinetics400 Carreira & Zisserman (2017) ... It has a single dedicated train/val/test split. In this work, we train on the train split and evaluate IID on the test split. ... A standard validation set does not exist for HDMB51 and UCF101. We randomly sample 20% of the respective training sets to use for validation... |
| Hardware Specification | Yes | All experiments are performed on a local computing cluster with access to V100 and A100 GPUs of various memory configurations up to 80GB. |
| Software Dependencies | No | The Py Torch Paszke et al. (2019) library is utilized for all experiments. This mentions PyTorch but does not specify a version number. |
| Experiment Setup | Yes | For all experiments, we use a clip resolution of 224 × 224. We follow Li et al. (2023) and use Kinetics400 Carreira & Zisserman (2017) pretrained Swin-T Liu et al. (2022) with 32 frame clips at a skip rate of 2. We adopt the same common augmentations used in Li et al. (2023): random resized cropping and random horizontal flipping. Our chosen optimizer is AdamW Kingma & Ba (2014); Loshchilov & Hutter (2017) with default parameters β1 = 0.9, β2 = 0.999, and weight decay of 0.01. We follow the linear scaling rule Goyal et al. (2017) with a base learning rate of 1e-4 corresponding to a batch size of 64. For training, we utilize a linear warmup of 5 epochs and a cosine learning rate scheduler. |