Source-linked AI summary
Reconstruction Network for Video Captioning
Bairui Wang, Lin Ma, Wei Zhang, Wei Liu
TL;DR
Video captioning must describe complex visual sequences while capturing temporal information, but prior encoder-decoder models mainly use the video-to-sentence direction. RecNet adds reconstructors that model sentence-to-video information, and benchmark experiments report significant gains in caption accuracy.
Problem
Video captioning requires modeling sophisticated visual content and temporal dynamics, while conventional encoder-decoder systems omit sentence-to-video information.
Method
RecNet combines an encoder-decoder with global and local reconstructors that reproduce video representations from decoder hidden states, jointly trained with generation and reconstruction losses.
Results
Extensive benchmark results show that the proposed reconstructor addresses backward flow and achieves significant gains in video captioning accuracy.
Takeaways & Limitations
Bidirectional cues between captions and video content can boost encoder-decoder models for video captioning.
Takeaways & Limitations
On short MSR-VTT clips, the performance gap between global and local reconstructors is not significant because frame representations have few differences.
Abstract
from arXiv · showhide
In this paper, the problem of describing visual contents of a video sequence with natural language is addressed. Unlike previous video captioning work mainly exploiting the cues of video contents to make a language description, we propose a reconstruction network (RecNet) with a novel encoder-decoder-reconstructor architecture, which leverages both the forward (video to sentence) and backward (sentence to video) flows for video captioning. Specifically, the encoder-decoder makes use of the forward flow to produce the sentence description based on the encoded video semantic features. Two types of reconstructors are customized to employ the backward flow and reproduce the video features based on the hidden state sequence generated by the decoder. The generation loss yielded by the encoder-decoder and the reconstruction loss introduced by the reconstructor are jointly drawn into training the proposed RecNet in an end-to-end fashion. Experimental results on benchmark datasets demonstrate that the proposed reconstructor can boost the encoder-decoder models and leads to significant gains in video caption accuracy.
1. Introduction
Video captioning must model rich spatial content and temporal dynamics, while conventional encoder-decoder systems use only video-to-sentence information. RecNet adds sentence-to-video reconstruction through global and local reconstructors, and benchmark results show significant captioning gains.
- Video captioning is harder than image captioning because videos contain sophisticated objects, scenes, and actions.
- Captioning systems must capture temporal dynamics in addition to spatial content modeling.
- Conventional encoder-decoder models use the forward video-to-sentence flow but omit sentence-to-video information.
- RecNet uses an encoder-decoder-reconstructor architecture to exploit both forward and backward flows for video captioning.
- Two reconstructors restore the video's global and local structures, respectively.
- Extensive benchmark results indicate that the reconstructor addresses backward flow and achieves significant gains in video captioning.
2. Related Work
Video captioning research includes template-based and sequence-learning approaches, while dual learning reconstructs source information from target representations. RecNet applies bidirectional video-sentence modeling to sequence-based video captioning.
- Template-based methods generate video descriptions by associating detected visual words with predefined grammatical templates.
- Sequence-learning methods directly generate more syntactically flexible descriptions from visual inputs.
- Simple mean pooling in some sequence-learning methods does not adequately address video temporal dynamics.
- RecNet extends conventional sequence learning by using both video-to-sentence and sentence-to-video flows.
- Dual learning reconstructs source information from target-side hidden states and has been used in neural machine translation.
3. Architecture
RecNet extends video captioning with an encoder-decoder-reconstructor architecture that uses forward video-to-sentence and backward sentence-to-video flows. Its reconstructors recover global or frame-level video representations from decoder hidden states, and joint losses train the model end to end.
- Architecture: The encoder uses Inception-V4 to encode video frames into sequential semantic representations, while an LSTM decoder generates captions word by word.Temporal attention helps the decoder select key video frames or elements during caption generation.
- Architecture: RecNet adds a reconstructor to the encoder-decoder architecture, using decoder hidden states to reproduce the encoded video feature sequence.The reconstructor targets sequential frame representations rather than directly reconstructing high-dimensional video frames.
- Reconstructing Global Structure: The global reconstructor mean-pools decoder hidden states into a caption representation and combines it with each hidden state to recover the whole video's mean-pooled feature representation.The recovered global structure is compared with the video's mean-pooled frame features using Euclidean distance.
- Reconstructing Local Structure: The local reconstructor selectively attends to decoder hidden states and reconstructs video feature representations frame by frame to preserve temporal dynamics.Its dynamically generated context replaces the global mean representation as an input to the reconstruction process.
- Training: Training combines forward video-to-sentence likelihood with backward sentence-to-video reconstruction loss, using λ to trade off the encoder-decoder and reconstructor objectives.The encoder-decoder is first trained with forward likelihood, after which the reconstructor is introduced for joint training and encoder-decoder fine-tuning.
4. Experimental Results
Experiments on MSR-VTT and MSVD evaluate RecNet with standard captioning metrics, architectural variants, reconstruction settings, and qualitative examples. Adding reconstructors generally improves encoder-decoder captioning, while local reconstruction is slightly stronger than global reconstruction on MSR-VTT.
- Experimental setup: MSR-VTT experiments compare video captioning models using BLEU-4, METEOR, ROUGE-L, and CIDEr.MSR-VTT-10K contains 10K clips from 20 categories, with 20 captions per clip and 200K clip-sentence pairs.
- Encoder-decoder comparison: With the same VGG19 encoder, SA-LSTM achieves 35.6 BLEU-4 and 25.4 METEOR, exceeding MP-LSTM's 34.8 and 24.7.SA-LSTM uses attention to aggregate frame features, whereas MP-LSTM uses mean pooling.
- Reconstruction gains: Adding either global or local reconstruction to SA-LSTM provides significant gains across all reported captioning metrics.The results are interpreted as evidence that backward-flow information encourages more semantic and video-consistent captions.
- Trade-off parameter: λ > 0 improves BLEU-4, but overly large λ deteriorates captioning performance by misbalancing forward likelihood and backward reconstruction losses.The authors set λ to 0.2 for RecNetglobal and 0.1 for RecNetlocal.
- Reconstructor comparison: RecNetlocal performs slightly better than RecNetglobal on MSR-VTT because attention models local temporal dynamics, whereas mean pooling misses them.The performance gap is not significant, possibly because short clips have similar frame representations and complicated video-sentence relationships.
- Cross-dataset and qualitative results: On MSVD, RecNetlocal and RecNetglobal rank first and second across all metrics, while both reconstructors improve the original S2VT.Qualitative MSR-VTT examples likewise show RecNet captions adding more accurate content such as makeup, faces, fighting, and table tennis.
5. Conclusions
RecNet uses an encoder-decoder-reconstructor architecture to exploit bidirectional cues between video content and natural-language descriptions. Its global and local reconstructors jointly model forward likelihood and backward reconstruction, with benchmark results showing superior caption accuracy over existing encoder-decoder models.
- RecNet exploits bidirectional cues between natural-language descriptions and video content through an encoder-decoder-reconstructor architecture.
- Two reconstructors reproduce the global and local structures of the input video, respectively.
- Forward likelihood and backward reconstruction losses are jointly modeled to train RecNet.
- Experimental results on benchmark datasets support RecNet's superiority over existing encoder-decoder models in video caption accuracy.