Source-linked AI summary
Semantic Grouping Network for Video Captioning
Hobin Ryu, Sunghun Kang, Haeyong Kang, Chang D. Yoo
TL;DR
Video captioning must represent redundant frames while using both visual content and the evolving caption context. SGN groups frames around discriminating phrases from the partially decoded caption, uses Contrastive Attention loss for alignment, and outperforms runner-up methods on two benchmarks.
Problem
Consecutive frames often contain repetitive information, while prior video encoding methods mainly use visual input and give limited attention to the partially decoded caption.
Method
SGN filters candidate phrases, aligns surviving phrases with relevant video frames to form semantic groups, and uses these groups for caption decoding with Contrastive Attention loss.
Results
2.1%p and 2.4%p CIDEr-D margins over runner-up methods were achieved on MSVD and MSR-VTT, respectively.
Takeaways & Limitations
SGN produces explainable semantic groups with distinct meanings and coherent frame membership while dynamically adapting video representations to decoded words.
Takeaways & Limitations
Semantic groups require phrases and corresponding frames to be concrete, distinctive, and semantically aligned.
Abstract
from arXiv · showhide
This paper considers a video caption generating network referred to as Semantic Grouping Network (SGN) that attempts (1) to group video frames with discriminating word phrases of partially decoded caption and then (2) to decode those semantically aligned groups in predicting the next word. As consecutive frames are not likely to provide unique information, prior methods have focused on discarding or merging repetitive information based only on the input video. The SGN learns an algorithm to capture the most discriminating word phrases of the partially decoded caption and a mapping that associates each phrase to the relevant video frames - establishing this mapping allows semantically related frames to be clustered, which reduces redundancy. In contrast to the prior methods, the continuous feedback from decoded words enables the SGN to dynamically update the video representation that adapts to the partially decoded caption. Furthermore, a contrastive attention loss is proposed to facilitate accurate alignment between a word phrase and video frames without manual annotations. The SGN achieves state-of-the-art performances by outperforming runner-up methods by a margin of 2.1%p and 2.4%p in a CIDEr-D score on MSVD and MSR-VTT datasets, respectively. Extensive experiments demonstrate the effectiveness and interpretability of the SGN.
Introduction
Video captioning must connect visual content with text while handling redundant consecutive frames. SGN addresses this by grouping frames around discriminating phrases from the partially decoded caption and using those groups to adapt next-word prediction.
- Video captioning uses visual encoders and recurrent decoders to generate captions one word at a time from consecutive frames and prior words.
- Consecutive frames often repeat information, making frame-by-frame processing inefficient compared with grouping video information by semantic meaning.
- Prior approaches organize video information into segments, selected frames, or object-level features, but mainly focus on the visual modality.
- Semantic groups should use concrete, distinctive phrases whose associated frames are semantically aligned and nonredundant.
- SGN forms semantic groups by aligning relevant frames with phrases from the partially decoded caption, enabling caption-dependent video encoding and next-word prediction.
- SGN introduces Contrastive Attention loss to penalize groups containing unrelated frames without requiring manual alignment annotations.
Related Works
Related work reduces video redundancy through temporal or visual information units and explores links between visual and textual modalities. SGN extends these directions by using partially decoded caption phrases to discover a video hierarchy and align frames semantically.
- Encoding Video into Information Units: Earlier captioning methods encode videos into hierarchical abstractions, consecutive events, or sampled informative frames to reduce repetitive information.
- Multi-Modal Reasoning: Multimodal methods connect visual concepts, word embeddings, collocations, memories, or visual contexts to model visual-textual dependencies.
- Semantic Grouping Network: SGN combines visual encoding, phrase-based semantic grouping, and decoding, while training additionally uses a negative video for Contrastive Attention loss.
- Multi-Modal Reasoning: SGN uses partially decoded caption phrases to discover a hierarchical video structure by associating frames with semantically related phrases.
- Supervision for Attention: Attention supervision in video captioning includes temporal, spatial, and regional mechanisms, with some methods using explicit human-perception signals.
Semantic Grouping Network
SGN builds semantic groups by filtering redundant phrases from the partially decoded caption and aligning surviving phrases with relevant video frames. A semantic-attention decoder then uses these groups to predict the next word, while Contrastive Attention loss encourages coherent visual-textual alignment.
- Architecture: SGN uses a Visual Encoder, Phrase Encoder, Semantic Grouping module, and Decoder to construct and exploit semantic groups during caption generation.The Decoder predicts the next word from semantic groups rather than directly attending only to frames.
- Phrase Encoder: Phrase Encoder self-attention models dependencies among previously generated words to produce candidate phrase representations.Each phrase representation is built from the partially decoded caption and its word-attention weights.
- Semantic Grouping: Phrase Suppressor removes similar candidate phrases so the remaining phrases form distinctive, less redundant semantic groups.Phrase similarity is computed from the word-attention matrix, and a threshold-based procedure determines which phrase survives.
- Semantic Grouping: Semantic Aligner assigns frame relevance scores to surviving phrases and aggregates aligned frames into semantic-group representations.Using these groups avoids redundancy from adjacent frames and provides information units with more concrete meanings.
- Decoder: The decoder scores semantic groups by their usefulness for the next word, then generates the word distribution with an LSTM and softmax layer.The method replaces temporal attention over frames with semantic attention over groups.
- Training: Contrastive Attention loss supplements cross-entropy training by comparing input-video frames with negative-video frames to encourage coherent phrase-frame alignment.Negative videos are sampled from videos whose captions do not overlap with the input caption, and the contrastive probability increases when positive relevance exceeds negative relevance.
Experiments
Experiments evaluate SGN on MSVD and MSR-VTT through benchmark comparisons, component ablations, efficiency measurements, and qualitative visualizations. SGN improves captioning performance and produces interpretable semantic groups, while incurring a measured inference-speed reduction.
- Benchmark Results: SGN outperforms runner-up methods by 2.1%p on MSVD and 2.4%p on MSR-VTT in CIDEr-D.CIDEr-D is designed for captioning and is reported as more consistent with human judgment than the other metrics.
- Ablation Study: Adding the semantic aligner, phrase suppressor, and contrastive attention loss generally raises performance across the evaluated measures.The semantic aligner provides a significant increase from the baseline configuration.
- Ablation Study: SGN significantly outperforms its word-based counterpart on all metrics, supporting phrase-based formation of semantic groups.The comparison evaluates SGN with its default phrase-based grouping against a version without the Phrase Encoder and Phrase Suppressor.
- Inference Speed: SGN processes 657 videos per second on MSVD and 203 on MSR-VTT, compared with 865 and 268 for TA, respectively.The repeated grouping process reduces inference speed by about 25%, while latency remains below 10ms.
- Qualitative Results: Semantic groups align caption phrases with relevant frames and emphasize the group useful for predicting the next word.Examples show groups for phrases such as “people are walking” and “walking in the,” with the latter used to predict “street.”
- Qualitative Results: Compared with TA, SGN better identifies action subjects, provides more detailed descriptions, and appears to understand lengthy-scene context better.Examples include selecting “a band is performing” rather than “a man is singing.”
Conclusion
SGN encodes videos into explainable semantic groups built from partially decoded caption phrases and related frames, dynamically adapting video representations as caption words are decoded. It uses contrastive attention for alignment without manual annotations and achieves state-of-the-art CIDEr-D results on MSVD and MSR-VTT.
- Conclusion: SGN encodes videos into semantic groups containing partially decoded caption phrases and related frames.These groups are used to predict the next word and provide coherent, distinct meanings.
- Conclusion: Continuous feedback from decoded words dynamically updates the video representation to adapt to the partially decoded caption.
- Conclusion: Contrastive Attention loss supervises visual-textual alignment within semantic groups without manual annotations.
- Conclusion: 2.1%p and 2.4%p CIDEr-D margins over runner-up methods are reported on MSVD and MSR-VTT, respectively.
- Conclusion: Semantic groups are explainable because their members share coherent semantics and each group has a distinct meaning.