Source-linked AI summary
TF-Blender: Temporal Feature Blender for Video Object Detection
Yiming Cui, Liqi Yan, Zhiwen Cao, Dongfang Liu
TL;DR
Video object detection is challenged by feature degradation from camera jitter, fast motion, and appearance variation, while prior post-processing approaches are not end-to-end. TF-Blender models temporal relations, adjusts neighboring features, and blends them into stronger representations; it improves recent state-of-the-art methods by more than 0.7% mAP on ImageNet VID and 1.5% mAP on YouTube-VIS.
Problem
Video object detection must handle feature degradation from camera jitter or fast motion, while prior temporal post-processing is not end-to-end and cannot improve weak single-frame predictions.
Method
TF-Blender combines temporal relation, feature adjustment, and feature blender modules to model temporal-spatial relations and strengthen neighboring-frame features for detection.
Results
More than 0.7% mAP on ImageNet VID and 1.5% mAP on YouTube-VIS are reported absolute gains for recent state-of-the-art methods.
Takeaways & Limitations
TF-Blender is presented as a flexible framework that can be adopted by any learning-based detection network to improve video object detection accuracy.
Takeaways & Limitations
Prior post-processing methods cannot improve detection results when single-frame detection produces weak predictions.
Abstract
from arXiv · showhide
Video objection detection is a challenging task because isolated video frames may encounter appearance deterioration, which introduces great confusion for detection. One of the popular solutions is to exploit the temporal information and enhance per-frame representation through aggregating features from neighboring frames. Despite achieving improvements in detection, existing methods focus on the selection of higher-level video frames for aggregation rather than modeling lower-level temporal relations to increase the feature representation. To address this limitation, we propose a novel solution named TF-Blender,which includes three modules: 1) Temporal relation mod-els the relations between the current frame and its neighboring frames to preserve spatial information. 2). Feature adjustment enriches the representation of every neigh-boring feature map; 3) Feature blender combines outputs from the first two modules and produces stronger features for the later detection tasks. For its simplicity, TF-Blender can be effortlessly plugged into any detection network to improve detection behavior. Extensive evaluations on ImageNet VID and YouTube-VIS benchmarks indicate the performance guarantees of using TF-Blender on recent state-of-the-art methods.
1. Introduction
Video object detection must handle degraded appearances in individual frames, while existing aggregation methods inadequately model temporal relations. TF-Blender addresses this gap by organizing pairwise temporal information and blending neighboring features for stronger representations.
- 1. Introduction: Feature degradation from camera jitter or fast motion makes single-frame detection difficult, even though objects recur across nearby video frames.Post-processing methods assemble detections after independent frame-level inference, so weak single-frame predictions cannot be improved by that assembly.
- 1. Introduction: Optical-flow-based methods propagate temporal features, but their intuitive lumping operation oversimplifies feature aggregation.FGFA and SELSA instead use similarity-based adaptive weights, yet focus on global relations for neighboring frames.
- 1. Introduction: TF-Blender models temporal feature relations and blends valuable neighboring features to increase temporal-spatial representation across frames.Its design reinforces local feature-space similarity and semantic correspondence to improve robustness to appearance variations, shape deformations, and local occlusions.
- 1. Introduction: The framework organizes feature learning between every pair of frames and aggregates features across the whole neighborhood.It uses temporal relation and feature adjustment modules to manage temporal information and preserve spatial information during aggregation.
2. Related Works
Related work addresses video understanding through post-processing, temporal feature aggregation, relation learning, and video instance segmentation. TF-Blender differs by applying general relation learning to feature aggregation and exploiting salient cross-frame correspondences.
- 2. Related Works: Video object detection methods use post-processing techniques such as linking boxes, tubelets, or temporal convolutions to incorporate information across frames.These approaches build on still-image detection or embed temporal information for cross-frame detection.
- 2. Related Works: Temporal feature aggregation methods are categorized as local, global, or combination aggregation, with FGFA and MANet representing local optical-flow-based aggregation.Global methods and other approaches organize temporal features over different ranges of video sequences.
- 2. Related Works: Video instance segmentation extends image segmentation to videos by segmenting and tracking instances across frames.Many methods focus on tracking rather than generating high-quality features for detection, segmentation, and tracking.
- 2. Related Works: Relation-learning methods model relationships between current features and neighbors in tasks including point-cloud analysis, image understanding, and video object detection.RDN and MEGA apply relation learning to video detection through relation distillation or memory-enhanced global-local aggregation.
- 2. Related Works: TF-Blender proposes a more general feature-aggregation approach that depicts salient correspondences between current and neighboring frames and exploits valuable features.This distinguishes it from methods that use relation learning in narrower aggregation settings.
3. TF-Blender
TF-Blender addresses limitations of conventional temporal feature aggregation by modeling pixel-level temporal relations, adjusting neighboring features, and blending the resulting representations. Its design suppresses irrelevant features, avoids redundant aggregation, and produces stronger features for detection.
- Preliminary and Overall Pipeline: TF-Blender combines temporal relation, feature adjustment, and feature blender modules to organize temporal information for detection.The framework focuses on calculating useful weights and selecting representative neighboring features.
- Preliminary and Overall Pipeline: Conventional aggregation weights neighboring features by similarity to the current frame, but this can preserve irrelevant features and outliers.Global relations ignore local spatial information, creating problems for occlusions and small-scale objects.
- Temporal Relation: The temporal relation module assigns adaptive weights at every feature-map pixel, enhancing regions of interest while suppressing irrelevant features.A feature relation function and masking function generate the adaptive weights for each neighboring frame.
- Feature Adjustment: The feature adjustment module aggregates each neighboring feature with other neighboring features because an unconstrained feature may not be valuable for aggregation.This operation uses the temporal relation module to generate adaptive weights during neighboring-feature aggregation.
- Feature Blender: The feature blender applies ReLU to temporal-relation outputs, softmax across channels to adjusted features, and cosine-distance filtering before element-wise combination.The design removes redundant neighboring features that are very similar to the current feature and avoids ambiguity from blurred or defocused frames.
- Experiments: Table 1 compares recent video object detection models on ImageNet VID using a ResNet-101 backbone, with runtime measured on one RTX 2080Ti GPU.The supplied table caption specifies the evaluation setting but does not provide numerical results.
4. Experiments
Experiments evaluate TF-Blender on video detection and instance segmentation, compare its integration with existing methods, and analyze module, relation-function, object-size, motion-speed, and runtime effects.
- Main Results: Experiments use mAP and evaluate detection methods on ImageNet VID, with training combining ImageNet VID and DET datasets.Compared systems include MEGA, SELSA, FGFA, and RDN.
- Main Results: TF-Blender improves every compared ImageNet VID method using the same backbone, with head-to-head gains around 0.7%-1.5% in accuracy.FGFA has the highest improvement among the compared methods.
- Main Results: TF-Blender helps address weak detection in rare-pose and part-occlusion situations, as illustrated by ImageNet VID examples.Figure 5 presents quantitative examples with and without TF-Blender integration.
- Main Results: On YouTube-VIS with ResNet-50 backbones, TF-Blender achieves competitive results across evaluation metrics and improves MaskTrack R-CNN and SipMask by more than 1.6% AP.The experiments target feature issues including motion blur and defocus.
- Ablation Study: Each TF-Blender component improves detection: temporal relation adds 0.7%, feature adjustment adds 0.3%, and feature blender adds 0.5% over the baseline in separate settings.Combining components yields a full-model absolute gain of 1.5% in video object-detection accuracy.
- Ablation Study: The selected relation function uses fi, fj, fi −fj, and fj −fi, while a three-layer 1×1 convolutional mini-network reaches 79.2% mAP before deeper networks reduce accuracy.Changing the kernel size to 3×3 adds 0.1% detection accuracy.
- Analysis of Object Sizes and Motion Speeds: TF-Blender provides different improvements across motion speeds and object sizes, with the highest detection improvement reported for large objects.The authors relate this to larger feature maps benefiting more from individual aggregation weights.
- Speed-Accuracy Tradeoff: Adding TF-Blender increases runtime within an affordable range while improving accuracy as the number of input frames increases.The reported computational-cost impact is described as negligible.
5. Conclusion
TF-Blender addresses feature degradation in video frames through temporal relation, feature adjustment, and feature blender modules. Integrated with existing detection networks, it improves video object detection accuracy on ImageNet VID and YouTube-VIS.
- TF-Blender combines temporal relation, feature adjustment, and feature blender modules to address feature degradation in video frames.
- The framework is flexible and general enough to be adopted by learning-based detection networks.
- TF-Blender improves video object detection accuracy for current state-of-the-art methods on ImageNet VID and YouTube-VIS.