Source-linked AI summary
Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization
Krishna Kumar Singh, Yong Jae Lee
TL;DR
Weakly-supervised localization methods often identify only the most discriminative object or action parts, leaving the full relevant extent underlocalized. Hide-and-Seek randomly hides patches or frames during training so networks learn from alternative parts, while full inputs are used at testing. The framework improves localization over state-of-the-art methods on ILSVRC and extends to temporal action localization.
Problem
Existing weakly-supervised methods often localize only the most discriminative parts instead of the entire relevant object or action extent.
Method
Hide-and-Seek randomly hides image patches or video frames during training, forcing networks to learn multiple relevant parts while using unhidden inputs during testing.
Results
The method achieves improved localization accuracy over state-of-the-art methods, including 3.78 and 1.40 percentage-point gains over AlexNet-GAP on GT-known Loc and Top-1 Loc.
Takeaways & Limitations
Hide-and-Seek provides a weakly-supervised localization framework applicable across object localization and temporal action localization without changing the network architecture.
Takeaways & Limitations
The method assumes that, in expectation, a hidden patch’s output equals that of an average-valued patch.
Abstract
from arXiv · showhide
We propose `Hide-and-Seek', a weakly-supervised framework that aims to improve object localization in images and action localization in videos. Most existing weakly-supervised methods localize only the most discriminative parts of an object rather than all relevant parts, which leads to suboptimal performance. Our key idea is to hide patches in a training image randomly, forcing the network to seek other relevant parts when the most discriminative part is hidden. Our approach only needs to modify the input image and can work with any network designed for object localization. During testing, we do not need to hide any patches. Our Hide-and-Seek approach obtains superior performance compared to previous methods for weakly-supervised object localization on the ILSVRC dataset. We also demonstrate that our framework can be easily extended to weakly-supervised action localization.
1. Introduction
Weakly-supervised localization reduces annotation demands but often captures only the most discriminative object regions. Hide-and-Seek addresses this by randomly hiding training patches, encouraging networks to learn multiple relevant parts and extending the idea to actions.
- Weakly-supervised learning uses cheaper labels than fully supervised learning, enabling training from more broadly available visual data.
- Existing methods often localize only the most discriminative part rather than the entire object extent.This limitation arises from intra-category variation or reliance on classification objectives.
- The method changes only the input image, avoids external data or architectural modifications, and leaves patches unhidden during testing.The authors address the resulting train-test distribution discrepancy by setting hidden pixels to the data mean.
- Hide-and-Seek randomly hides image patches during training, forcing the model to seek other relevant object parts when salient regions are unavailable.Different patches are hidden across epochs, exposing the network to varied portions of each image.
- Hide-and-Seek generalizes across networks and tasks, including weakly-supervised object localization and temporal action localization.For actions, randomly hidden frame sequences encourage learning relevant frames for an action.
2. Related Work
Prior weakly-supervised localization methods commonly rely on discriminative regions, classification activations, masking, or related regularization techniques. Hide-and-Seek instead hides contiguous image regions or video frames during training and applies the framework to full-extent action localization using video-level labels.
- Weakly-supervised object localization: Weakly-supervised object localization reduces reliance on expensive bounding-box annotations by learning from cheaper image-level labels.
- Weakly-supervised object localization: Many object-localization methods mine discriminative features or patches but fail to cover the object’s full spatial extent.Hide-and-Seek counters this tendency by randomly hiding image patches during training.
- Masking pixels or activations: Masking-based localization methods typically infer regions by measuring classification-performance drops after masking, whereas Hide-and-Seek masks during training.
- Masking pixels or activations: Dropout randomly removes layer units to prevent overfitting, while Hide-and-Seek removes contiguous image regions or video frames to improve localization.The paper reports significantly better localizations than dropout in experiments.
- Action localization: Weakly-supervised action localization methods may use scripts, ordered action lists, weakly labeled videos, or video-level labels; Hide-and-Seek focuses on the full action extent using a single video-level label.
3. Approach
Hide-and-Seek trains weakly supervised localizers by randomly hiding image patches, forcing attention beyond the most discriminative object parts. The method also addresses activation-distribution mismatch and extends the same masking idea to temporal action localization.
- Motivation: Existing weakly-supervised methods often localize only the most discriminative object parts instead of the object’s full spatial extent.
- Image localization: During training, Hide-and-Seek randomly hides input-image patches so the network must learn multiple relevant object parts.Different patches are hidden across images and training epochs, preventing reliance on one consistently visible discriminative region.
- Image localization: Patches are hidden only during training; full images are used at test time after the network has learned to focus on multiple relevant parts.
- Activation handling: Hidden pixels are replaced with the dataset mean RGB vector to make activations for hidden or partially hidden patches match expected fully visible-patch outputs.The method assumes that, in expectation, a patch’s output equals the output of an average-valued patch.
- Action localization: For action localization, continuous video-frame segments are randomly hidden during training, and thresholded one-dimensional CAMs provide action start and end times.The framework is presented as independent of network architecture and demonstrated with image and video localization tasks.
4. Experiments
Experiments evaluate Hide-and-Seek for weakly supervised object localization on ILSVRC and action localization on THUMOS, including quantitative, qualitative, and ablation studies. Randomly hiding contiguous image patches or video frames improves localization, with gains across architectures, pooling choices, and comparisons against baselines.
- Datasets and evaluation metrics: The evaluation uses ILSVRC validation data for object localization and THUMOS 2014 validation data for temporal action localization.Object localization uses Top-1 Loc and GT-known Loc; action localization uses mAP at varying temporal IoU thresholds.
- Object localization quantitative results: Hide-and-Seek models outperform full-image AlexNet-GAP and GoogLeNet-GAP baselines across tested patch sizes for localization.Patches are hidden with 0.5 probability during training; the approach can reduce classification accuracy while substantially improving localization.
- Object localization quantitative results: The mixed-patch model achieves the best Top-1 Loc, while an ensemble improves over AlexNet-GAP by 5.24% and 4.15% for GT-known Loc and Top-1 Loc.The ensemble also exceeds a five-model AlexNet-GAP ensemble by 3.23% and 1.82% on those metrics.
- Object localization quantitative results: 3.78% and 1.40% points improve AlexNet GT-known Loc and Top-1 Loc, while GoogLeNet gains 1.88% and 1.61% points, respectively.These gains result from changing only the input image, without changing the network architecture.
- Object localization qualitative results: Qualitative CAMs show Hide-and-Seek localizing multiple object parts, including bodies and tails, rather than only highly discriminative regions.This contrasts with AlexNet-GAP, which often focuses on animal faces and misses tails.
- Further analysis of Hide-and-Seek: Contiguous patch hiding outperforms dropout for localization, and Hide-and-Seek makes max pooling competitive with, and slightly better than, average pooling.The pooling comparison uses patch size 56; the authors attribute the slight max-pooling improvement to robustness to noise.
- Further analysis of Hide-and-Seek: Hiding probability creates a trade-off: higher probabilities reduce Top-1 Loc through lower classification accuracy, while lower probabilities reduce GT-known Loc by exposing more discriminative content.GT-known Loc remains almost unchanged as hiding probability increases, whereas Top-1 Loc decreases substantially.
- Action localization results: Video-HaS consistently outperforms full-video training for action localization on THUMOS validation data.Randomly hiding frame segments during action-classification training is reported to force attention toward more relevant frames.
5. Conclusion
Hide-and-Seek improves object localization in images and temporal action localization in videos by randomly hiding patches or frames during training, forcing attention to multiple relevant parts.
- Hide-and-Seek is a weakly-supervised framework for object localization in images and temporal action localization in videos.
- Randomly hiding patches or frames during training forces the network to focus on multiple relevant parts of an object or action.
- Extensive experiments showed improved localization accuracy over state-of-the-art methods.