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Jointly Modeling Embedding and Translation to Bridge Video and Language
Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui
TL;DR
Video description generation requires aligning natural-language sentences with visual content beyond locally predicting the next word, because sentence-level semantics can remain incorrect. LSTM-E jointly learns an LSTM generator and visual-semantic embedding using CNN video representations; on YouTube2Text, it achieves the best reported sentence-generation performance and outperforms state-of-the-art methods on SVO prediction.
Problem
Existing video-description methods mainly optimize next-word probabilities locally, leaving the relationship between entire-sentence semantics and video content insufficiently exploited.
Method
LSTM-E jointly learns a CNN-based video representation, an LSTM sentence generator, and a visual-semantic embedding model using relevance and coherence objectives.
Results
LSTM-E achieves the best reported sentence-generation performance and outperforms the current state-of-the-art on both SVO prediction and sentence generation.
Takeaways & Limitations
The framework bridges video content and natural language by jointly measuring global sentence relevance and local word coherence.
Takeaways & Limitations
Further gains may require better recurrent representations of video’s temporal sequence and sufficient labeled video-sentence pairs for training a deeper RNN.
Abstract
from arXiv · showhide
Automatically describing video content with natural language is a fundamental challenge of multimedia. Recurrent Neural Networks (RNN), which models sequence dynamics, has attracted increasing attention on visual interpretation. However, most existing approaches generate a word locally with given previous words and the visual content, while the relationship between sentence semantics and visual content is not holistically exploited. As a result, the generated sentences may be contextually correct but the semantics (e.g., subjects, verbs or objects) are not true. This paper presents a novel unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visual-semantic embedding. The former aims to locally maximize the probability of generating the next word given previous words and visual content, while the latter is to create a visual-semantic embedding space for enforcing the relationship between the semantics of the entire sentence and visual content. Our proposed LSTM-E consists of three components: a 2-D and/or 3-D deep convolutional neural networks for learning powerful video representation, a deep RNN for generating sentences, and a joint embedding model for exploring the relationships between visual content and sentence semantics. The experiments on YouTube2Text dataset show that our proposed LSTM-E achieves to-date the best reported performance in generating natural sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. We also demonstrate that LSTM-E is superior in predicting Subject-Verb-Object (SVO) triplets to several state-of-the-art techniques.
1. Introduction
Video description generation must connect visual objects, spatio-temporal relationships, and natural-language dynamics, while existing local word-generation methods may miss sentence-level semantic alignment. LSTM-E addresses this with jointly learned language and visual-semantic components and outperforms prior methods on sentence generation and SVO prediction.
- Video description generation aims to produce natural sentences that capture informative video dynamics and support applications including editing, indexing, search, and sharing.
- Existing approaches optimize the next word locally, so generated sentences may be contextually correct while misidentifying subjects, verbs, or objects.
- The framework uses 2-D and/or 3-D CNN features, an LSTM sentence generator, and a visual-semantic embedding model trained jointly.
- LSTM-E jointly models contextual word relationships and the relationship between entire-sentence semantics and video content.
- On Youtube2Text, LSTM-E outperforms state-of-the-art methods in both sentence generation and Subject-Verb-Object triplet prediction.
2. RELATED WORK
Prior visual-to-sentence methods either construct sentences from predefined fragments or learn visual-text distributions, but many emphasize word-level coherence without fully modeling whole-sentence relevance. LSTM-E jointly exploits both relationships.
- One translation direction decomposes sentences into predefined grammatical parts such as subject, verb, and object, then aligns fragments with visual content.
- Another direction learns a common visual-text space with topic models or neural networks to generate sentences more flexibly.
- Prior video methods use LSTM, optical flow, 3-D convolution, or temporal attention to model video sequences and generate descriptions.
- LSTM-E differs by jointly exploiting word-level coherence and whole-sentence relevance between visual content and sentence semantics.
3. Video Description Generation
The paper formulates video description as generating a sentence that is both coherent in language and relevant to video content. It models these complementary requirements through coherence and relevance losses combined in a joint objective.
- A good video description must organize words coherently while keeping the entire sentence relevant to the video.
- The problem represents a video by visual features and a sentence by word features plus an integrated sentence-level feature.The sentence-level representation is constructed from word vectors and binary TF weights.
- The energy loss combines relevance and coherence losses as E(V, S) = (1 −λ) × Er(v, s) + λ × Ec(v, W).Er measures video–sentence relevance, while Ec estimates contextual relationships among generated words.
- A visual-semantic embedding measures relevance by mapping video and sentence representations into a shared low-dimensional space.Transformation matrices project the video and sentence into the common embedding before their distance is evaluated.
- Minimizing the relevance and coherence losses is intended to make generated sentences better aligned with video semantics and smoother in context.The two losses are jointly modeled in a deep recurrent neural network.
4. Joint Modeling Embedding and Translation
LSTM-E jointly trains an LSTM sequence model and a visual-semantic embedding so that video-conditioned sentences capture both word-level coherence and sentence-level relevance. Its architecture uses convolutional video features, embedding-based inputs, and recurrent word prediction.
- LSTM-E translates video representations into sentences while simultaneously minimizing relevance and coherence losses.The model’s parameters include the LSTM and the video and sentence transformations.
- 4.1. Long Short Term Memory: LSTM uses a memory cell and input, forget, and output gates to control information flow and capture long-term temporal dynamics.The forget gate regulates which information is retained or discarded during recurrent processing.
- 4.2. LSTM with Visual-Semantic Embedding: The LSTM-E inputs combine a mapped video representation initially with word embeddings at subsequent timesteps.The initial video embedding informs the recurrent memory, after which previous-word embeddings drive sequence updates.
- 4.2. LSTM with Visual-Semantic Embedding: At each noninitial timestep, an LSTM output passes through softmax to produce a probability distribution over vocabulary words.Generation continues until the end-sign word is emitted.
- 4.2. LSTM with Visual-Semantic Embedding: The training objective is optimized over video-sentence pairs with stochastic gradient descent and includes regularization terms.The regularizers apply to the video embedding, sentence embedding, softmax layer, and LSTM.
- 4.2. LSTM with Visual-Semantic Embedding: Sentence generation can sample words sequentially or retain top-k sentence candidates at each timestep.These are alternative decoding strategies for extending partial sentences.
5. Experiments
The experiments evaluate LSTM-E on Subject-Verb-Object triplet prediction and natural sentence generation, including analyses of loss tradeoffs and LSTM hidden-layer size.
- The evaluation compares LSTM-E with state-of-the-art methods on SVO triplet prediction and natural sentence generation.
- The experiments also examine the tradeoff parameter between coherence and relevance and the size of the LSTM hidden layer.
5.1. Experimental Setting
Experiments use the YouTube2Text corpus, which contains 1,970 YouTube snippets covering diverse daily activities and roughly 40 English descriptions per video.
- YouTube2Text contains 1,970 YouTube snippets spanning activities such as exercise, music, and cooking.
- The experiments use roughly 40 available English descriptions for each video.
5.2. Performance Comparison
The experiments compare LSTM-E with established video-language models on SVO triplet prediction and sentence generation. LSTM-E improves semantic accuracy and generation quality, with stronger visual representations and combined VGG+C3D features yielding further gains.
- Compared Approaches: LSTM-E is evaluated on SVO triplet prediction and video sentence generation against several non-trivial baseline methods.The compared approaches include CRF, CCA, FGM, JEM, LSTM, Soft-Attention, and S2VT.
- SVO Triplet Prediction: SVO accuracy measures exact Subject-Verb-Object word matching with binary 0-1 loss.For sentence-driven models, SVO triplets are extracted from generated sentences using a dependency parser.
- SVO Triplet Prediction: Jointly modeling sentence semantics and video content significantly improves LSTM on SVO triplet prediction.LSTM-E models with VGG, C3D, and combined VGG+C3D features outperform the LSTM baseline on Subject, Verb, and Object measures.
- Sentence Generation: LSTM-E (Alex) significantly outperforms traditional LSTM in BLEU@N, with larger gains as N increases.BLEU@N measures shared contiguous n-grams up to 4-gram, while METEOR uses unigram precision and recall with synonym and stem matching.
- Sentence Generation: 31.0% METEOR is achieved by LSTM-E (VGG+C3D), exceeding SA by 4.7% and S2VT by 4.0%.LSTM-E (VGG) reaches 29.5% METEOR, and LSTM-E (C3D) reaches 29.9% before feature combination.
- Sentence Generation: LSTM-E (VGG+C3D) generates more coherent sentences and more relevant subject, verb, and object terms than the compared automatic methods.Examples include more precise subjects, the verb “singing,” and objects such as “keyboard” and “motorcycle.”
5.3. Experimental Analysis
The analysis studies how the tradeoff parameter λ and LSTM hidden-layer size affect performance. Performance peaks near λ = 0.7, while larger hidden layers improve both reported metrics through size 512.
- The Tradeoff Parameter λ: Performance curves form ∧ shapes as λ varies from 0.1 to 0.9, with the best performance at about λ = 0.7.The curves use normalized BLEU@N and METEOR values for comparability.
- The Tradeoff Parameter λ: The results support jointly learning the visual-semantic embedding space within the deep recurrent neural network.The tradeoff parameter controls the balance between the competing losses.
- Sentence Generation Examples: Figure 5 compares sampled video frames, outputs from LSTM and two LSTM-E variants, and three randomly selected ground-truth sentences.The LSTM-E variants are LSTM-E (Alex) and LSTM-E (VGG+C3D).
- Hidden Layer Size: Increasing the hidden-layer size from 128 to 256 to 512 improves both BLEU@4 and METEOR.The hidden-layer size is empirically set to 512 because it achieves the best performance.
6. Discussion and Conclusion
The paper proposes LSTM-E, which adds visual-semantic embedding to LSTM learning and outperforms prior state-of-the-art methods on SVO prediction and sentence generation. Future work targets improved temporal video representation and deeper RNNs supported by more labeled video-sentence pairs.
- LSTM-E incorporates visual-semantic embedding into LSTM learning to jointly model local word context and global video-sentence semantics.The embedding space measures relationships between video content and whole-sentence semantics alongside LSTM’s local contextual modeling.
- LSTM-E outperforms the current state-of-the-art model by a significantly large margin on both SVO prediction and sentence generation.
- Better temporal video representation using RNNs is identified as a future research direction.
- Deeper RNNs may further boost video description generation if sufficient labeled video-sentence pairs become available.