Source-linked AI summary
Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning
Junchao Zhang, Yuxin Peng
TL;DR
Video captioning needs representations that preserve salient objects and their detailed temporal dynamics, because global or insufficiently discriminative representations limit fine-grained descriptions. OA-BTG uses bidirectional temporal graphs and object-aware aggregation, and achieves state-of-the-art BLEU@4, METEOR and CIDEr performance on MSVD and MSR-VTT.
Problem
Video captioning needs to capture salient objects with detailed temporal dynamics and represent them discriminatively for accurate, fine-grained descriptions.
Method
OA-BTG combines forward and backward temporal graphs for object trajectories with learnable VLAD aggregation over object trajectories and global frame sequences, using hierarchical attention across objects.
Results
OA-BTG achieves state-of-the-art BLEU@4, METEOR and CIDEr performance on the MSVD and MSR-VTT datasets.
Takeaways & Limitations
The approach captures complementary object trajectories and combines fine local object information with global context for video captioning.
Takeaways & Limitations
The authors identify constructing more effective graphs for relations among object instances and modeling interactions between backward temporal sequences end-to-end as future work.
Abstract
from arXiv · showhide
Video captioning aims to automatically generate natural language descriptions of video content, which has drawn a lot of attention recent years. Generating accurate and fine-grained captions needs to not only understand the global content of video, but also capture the detailed object information. Meanwhile, video representations have great impact on the quality of generated captions. Thus, it is important for video captioning to capture salient objects with their detailed temporal dynamics, and represent them using discriminative spatio-temporal representations. In this paper, we propose a new video captioning approach based on object-aware aggregation with bidirectional temporal graph (OA-BTG), which captures detailed temporal dynamics for salient objects in video, and learns discriminative spatio-temporal representations by performing object-aware local feature aggregation on detected object regions. The main novelties and advantages are: (1) Bidirectional temporal graph: A bidirectional temporal graph is constructed along and reversely along the temporal order, which provides complementary ways to capture the temporal trajectories for each salient object. (2) Object-aware aggregation: Learnable VLAD (Vector of Locally Aggregated Descriptors) models are constructed on object temporal trajectories and global frame sequence, which performs object-aware aggregation to learn discriminative representations. A hierarchical attention mechanism is also developed to distinguish different contributions of multiple objects. Experiments on two widely-used datasets demonstrate our OA-BTG achieves state-of-the-art performance in terms of BLEU@4, METEOR and CIDEr metrics.
1. Introduction
Video captioning must model both global video content and detailed temporal dynamics of salient object instances. OA-BTG addresses these needs with bidirectional temporal graphs and object-aware aggregation, achieving state-of-the-art results on two datasets.
- Motivation: Accurate captions for complex videos require tracking salient object instances and their detailed temporal dynamics.The motivating example includes a boy, basketball, and basket whose evolving relations contribute to the caption.
- Motivation: Global frame or salient-region methods do not discriminate among specific object instances, limiting their ability to capture each object's temporal evolution.
- Approach: OA-BTG constructs bidirectional temporal graphs that follow and reverse temporal order to obtain complementary trajectories for salient objects and global context.
- Approach: Learnable VLAD models aggregate object trajectories and global frame sequences, while hierarchical attention distinguishes the contributions of different object instances.
- Results: OA-BTG achieves state-of-the-art BLEU@4, METEOR and CIDEr performance on the MSVD and MSR-VTT datasets.
2. Related Works
Earlier video-captioning methods progressed from templates and pooled frame features toward temporal modeling and multimodal inputs. OA-BTG instead emphasizes bidirectional object trajectories and object-aware representation learning using visual features.
- Earlier Methods: Template-based methods predict concepts before generating sentences from predefined templates, limiting sentence diversity through template and concept dependence.
- Temporal Modeling: Encoder-decoder and temporal-modeling methods address the loss of frame order information through temporal attention and LSTM-based long-term structure learning.
- OA-BTG: OA-BTG distinguishes object instances across frames, constructs bidirectional temporal graphs, and learns representations on their temporal trajectories.
- OA-BTG: Unlike multimodal approaches, OA-BTG uses only visual features while focusing on object temporal evolution and object-aware feature aggregation.
3. Object-aware Aggregation with Bidirectional Temporal Graph
OA-BTG tracks salient object instances and global frames in both temporal directions, then aggregates their local features into discriminative spatio-temporal representations for captioning.
- Bidirectional Temporal Graph: The bidirectional temporal graph constructs forward and backward trajectories for salient object instances and global frames.Forward and reverse temporal organization provide complementary representations of video dynamics.
- Bidirectional Temporal Graph: Object regions are matched across frames using similarity based on visual appearance, overlap, and relative area.The similarity score averages appearance, intersection-over-union, and area-size terms.
- Bidirectional Temporal Graph: Nearest-neighbor alignment groups regions into forward and backward object trajectories anchored at the first and last frames.The two trajectory sets are organized along and reversely along temporal order.
- Object-aware Aggregation: Two learnable VLAD models aggregate local features from object-region sequences and global-frame sequences into discriminative representations.Local convolutional feature maps are organized according to the bidirectional temporal sequences before VLAD encoding.
- Object-aware Aggregation: C-GRU modules learn soft VLAD assignments from local features, with shared parameters across object-region sequence groups.The hidden state represents learned assignments to K learnable cluster centers.
- Decoder: Forward and backward prediction scores are fused at each decoding step to exploit their complementary temporal information.The fused scores determine the generated word.
4.1. Datasets and Evaluation Metrics
OA-BTG is evaluated on MSVD and MSR-VTT using BLEU@4, METEOR, and CIDEr, with metric scores computed through the Microsoft COCO evaluation server.
- Datasets: The experiments use the MSVD and MSR-VTT video-captioning datasets.MSVD contains open-domain video clips, while MSR-VTT is a larger benchmark with diverse categories and 200,000 clip-sentence pairs.
- Datasets: MSVD includes 1,970 YouTube video clips and approximately 8,000 English descriptions.The dataset split uses 1,200 clips for training, 100 for validation, and 670 for testing.
- Datasets: MSR-VTT contains 10,000 video clips, 200,000 clip-sentence pairs, and 20 video categories.Each video clip is annotated with 20 natural-language sentences.
- Evaluation Metrics: Evaluation uses BLEU@4, METEOR, and CIDEr metrics.BLEU@4 measures four-gram overlap, METEOR uses unigram precision and recall with similar-word matching, and CIDEr is voting-based.
4.2. Experimental Settings
The experimental setup samples video frames and object regions, extracts local features with ResNet-200, and trains the captioning model with specified sequence, VLAD, optimization, and decoding settings.
- Input Features: Each input video is represented using 40 sampled frames and five detected object regions per frame.Mask R-CNN detects objects, and object regions are cropped before feature extraction.
- Input Features: ResNet-200 extracts local features from object crops and global frames using a 7 × 7 × 2048 res5c feature map.Mask R-CNN uses a ResNet-101 backbone pretrained on Microsoft COCO.
- Caption Processing: Training filters captions longer than 16 words and zero-pads shorter sentences.BOS and EOS tags are added, unseen vocabulary items become UNK tags, and encoder-decoder hidden units are set to 512.
- VLAD Settings: Trainable VLAD models use 64 cluster centers on MSVD and 128 on MSR-VTT.The larger MSR-VTT setting reflects its larger scale and more diverse video content.
- Optimization and Decoding: Adam optimization uses a fixed learning rate of 1 × 10^-4 and batch size 16, while testing uses beam search with beam size 5.Decoder dropout is 0.5 and gradient clipping is applied to [−10, 10].
4.3. Comparisons with State-of-the-art Methods
OA-BTG outperforms compared state-of-the-art methods on MSVD and MSR-VTT, using object-region trajectories and bidirectional temporal modeling to retain object-aware semantics and temporal dynamics.
- Comparison with Prior Methods: OA-BTG differs from spatial-temporal attention methods by modeling detailed temporal trajectories for individual object instances.Its trajectory extraction operates on object regions rather than only global frames or undifferentiated salient regions.
- Comparison with Prior Methods: Compared with TSA-ED, OA-BTG preserves object-aware information while capturing object semantics and temporal dynamics.TSA-ED extracts dense point trajectories but loses object-aware information according to the paper.
- Comparison with Prior Methods: Compared with SeqVLAD, OA-BTG aggregates object regions and constructs bidirectional temporal graphs for each object instance.The paper identifies these two design choices as reasons for its higher performance than SeqVLAD.
4.4. Ablation Study
Ablations show that object-aware aggregation improves over global-frame VLAD, bidirectional temporal graphs improve over single-directional variants, and combining both yields the full OA-BTG benefit.
- Object-aware Aggregation: Object-aware aggregation improves performance over a baseline using learnable VLAD only on global frame sequences.The object-aware variants use either forward or backward temporal graphs.
- Object-aware Aggregation: 0.6%, 1.3%, and 1.7% are the MSVD BLEU@4, METEOR, and CIDEr gains for OA with Backward TG over the baseline.On MSR-VTT, the corresponding gains are 1.2%, 1.0%, and 3.0%.
- Bidirectional Temporal Graph: 3.25%, 1.15%, and 5.45% are OA-BTG’s average MSVD improvements in BLEU@4, METEOR, and CIDEr over single-directional temporal-graph variants.The paper also reports improvements on MSR-VTT.
- Overall Ablation: The full OA-BTG configuration combines object-aware aggregation with bidirectional temporal graphs.Its comparison with the baseline is presented as verifying the overall effectiveness of the approach.
4.5. Qualitative Analysis
The qualitative analysis reports successful OA-BTG captions that capture objects, correct semantics, and detailed actions through object temporal trajectories.
- OA-BTG improves captioning by capturing objects and their detailed temporal information.
- Modeling each object’s temporal trajectory enables accurate depiction of actions.
- OA-BTG expresses correct semantics while generating fine-grained descriptions of detailed actions.
5. Conclusion
OA-BTG learns discriminative spatio-temporal representations by aggregating object-aware local information and modeling object trajectories in two temporal directions. Its integrated design captures spatial and temporal cues, while future work will address richer object relations and backward-sequence interactions.
- OA-BTG aggregates fine local information from object-aware regions and frames to learn discriminative spatio-temporal representations.
- Bidirectional temporal graphs capture each object instance’s temporal trajectory in two complementary directions.
- The combined bidirectional graph and object-aware aggregation capture crucial spatial and temporal cues simultaneously, boosting performance.
- Future work will construct more effective graphs for relations among object instances and model backward-sequence interactions end-to-end.