Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning
Anonymous Author(s)
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Abstract
Recent incremental learning for action recognition usually stores representativevideos to mitigate catastrophic forgetting.
However, only a few bulky videoscan be stored due to the limited memory.
To address this problem, we propose FrameMaker , a memory-efficient video class-incremental learning approachthat learns to produce a condensed frame for each selected video.
Specifically,
FrameMaker is mainly composed of two crucial components: Frame Condensing and Instance-Specific Prompt .
The former is to reduce the memory cost bypreserving only one condensed frame instead of the whole video, while the latter aims to compensate the lost spatio-temporal details in the Frame Condensingstage.
By this means, FrameMaker enables a remarkable reduction in memorybut keep enough information that can be applied to following incremental tasks.
Experimental results on multiple challenging benchmarks, i.e ., HMDB51, UCF101and Something-Something V2, demonstrate that FrameMaker can achieve betterperformance to recent advanced methods while consuming only 20% memory.
Additionally, under the same memory consumption conditions, FrameMaker significantly outperforms existing state-of-the-arts by a convincing margin.
The sourcecode and models will be released when this manuscript is public.
Introduction
Training video action recognition models with all classes in a single fine-tuning stage have beenwidely studied [6, 12, 26, 31, 33, 46, 47, 57] in recent years.
However, in many realistic scenarios,limited by privacy matters or technologies, the different classes can only be presented in sequence,where the previously trained classes are either unavailable or partially available in limited memory.
Naively training models will overfit currently available data and cause performance deterioration onpreviously seen classes, which is named catastrophic forgetting [35].
Class-incremental learning is amachine learning paradigm to fight this challenge when fine-tuning a single model in a sequence ofindependent classes.
A branch of class-incremental learning methods [5, 7, 9, 11, 18, 30, 40, 56] has achieved remarkableperformances in the image domain by re-training a portion of past examples.
Meanwhile, someexisting video incremental learning methods [37, 50] have demonstrated that storing more pastexamples can improve the ability to mitigate forgetting.
Nevertheless, videos are informationredundant and require a high memory load to be saved, hence it is impractical to store a good dealof training videos for each class.
Therefore, herding strategy [40] is adopted to select a small set ofrepresentative videos for the exemplar memory [37, 50].
vCLIMB [50] tries to down-sample framesfor each video to reduce the memory consumption, and imposes a temporal-consistency regularizationfor better performance.
Despite the remarkable performance, these methods still demand to conserve
[Figure]
⋯
TCD
[Figure]
Video Exemplars
[Figure] ⋯ [Figure] multiple ( i.e. , 8-16) frames for each representative video, leading to non-negligible memory overhead,which limits their further real-world applications.
To this end, we present FrameMaker, a memory-efficient video class-incremental learning approach.
Concretely, the proposed FrameMaker contains two components, Frame Condensing and InstanceSpecific Prompt.
In Frame Condensing, FrameMaker assigns a learnable weight to each frame, andthen these weights are adjusted to make the weighted sum frame i.e ., the condensed frame, share thesame embedding features with the original video clip.
Obviously, the collapsed temporal dimensionand mixed spatial cues by Frame Condensing may damage the accuracy of action recognition.
Tocompensate the missing spatio-temporal details, inspired by Task-Specific Prompt [32], we furtherpropose Instance-Specific Prompt, which learns a set of parameters for each condensed frame tomake minor modifications to the pixels.
In this way, the memory required to store a single videocan be remarkably compressed.
An intuitive comparison is given in Figure 1(a).
On the premise ofcomparable performance with TCD [37], FrameMaker significantly reduces memory overhead by80%.
Moreover, the comparison curve displayed in Figure 1(b) further indicates that FrameMakerachieves a consistent performance improvement over recent TCD with the same memory consumption.
In summary, we make the following contributions: • We present a memory-efficient video class-incremental learning method FrameMaker, which significantly reduces the memory consumption of example videos by integrating multiple frames into one condensed frame.
• For the first time, we show a novel perspective of Prompt, i.e., Instance-Specific Prompt, and
demonstrate that it can embed effective spatio-temporal interactions into learned condensed frames for video class-incremental learning. • Experimental results reveal that FrameMaker consistently outperforms recent state-of-the-art methods with the same memory overhead on three challenging benchmarks (HMDB51,UCF101, and Something-Something V2).
Related Works2.1 Class-Incremental Learning
In image domain , the early class-incremental learning approaches [1, 23, 29, 58] update the modelbased on the importance of parameters to selectively preserve or erase the learned knowledge.
Nevertheless, these methods have been demonstrated the poor generalization performance undermore challenging class-incremental settings as shown in [19, 49].
The knowledge distillation [17]based methods preserve the learned knowledge by mimicking the trained models from the past tasks. The implementation ideas include but are not limited to the gradient attention maps [9], the softmaxfunction [7], the sigmoid functions [29, 40, 56] and the l 2 -normalizations [18]. The memory-basedmethods store a small number of old examples per class in limit memory [40] or training generative models [36, 41] for replay while training new tasks. The architecture-based methods design specificcomponents for different tasks to retain the knowledge of previous classes.
In video domain , classincremental learning for action recognition is still an underexplored area.
The existing solutionsare designed for better temporal constraints, such as decomposing the spatio-temporal features [59],exploiting time-channel importance maps [37], and applying temporal consistency regularization [50].
Notably, both [37] and [50] have shown that more stored examples in memory can effectivelyencourage the performance.
However, FrameMaker attempts to minimize the stored frames for eachvideo by a pretty maneuver, which can provide decent performance with slight storage requirements.
2.2 Prompt-Based Learning
Prompting is an emerging transfer learning technique in natural language processing (NLP)
[32],which adapts the language model to downstream tasks by a fixed function.
Since the design ofprompting functions is challenging, the learnable prompts [25, 27] are proposed to learn task-specificknowledge parameters for the input.
Recently, in the visual domain, some works have also emergedto adapt the pre-trained models to new tasks by adding prompt tokens [21] or pixels [3] in the dataspace.
Besides, DualPrompt [55] and L2P [54] get rid of the rehearsal buffer in continual learning bylearning a set of task-specific parameters.
In this paper, we draw inspiration from prompting, andpresent an Instance-Specific Prompt to replenish the collapsed spatio-temporal cues for condensedframes.
2.3 Action Recognition
The approaches designed for action recognition task can be grouped into three categories: 2D CNN,3D CNN and Transformer-based methods.
Among them, the computation cost of 2D CNN-basedmethods is more friendly, since the standard 2D CNNs [16, 44] are performed on each frame, and thetemporal dynamics are aggregated by optical-flow [43, 51], frame difference [22, 28, 53], dynamicweights [20, 34] or temporal shift operation [31], etc.
On the other hand, 3D CNN [6, 10, 13, 14,39, 46–48, 52, 57] and Transformer-based [2, 4, 12, 26, 33] can achieve remarkable accuracy inaction recognition with expensive computation cost, since modeling spatio-temporal features jointlyintroduces both parameters and calculating operations.
In this work, we follow the settings in [37, 59],and employ the Temporal Shit Module (TSM) [31] based on 2D CNN for efficient experiments.
Method
This section presents the general framework of FrameMaker as shown in Figure 2, which mainlyconsists of two crucial components: Frame Condensing and Instance-Specific Prompt.
Firstly, Frame
Condensing is designed to integrate multiple frames into one frame for efficient storage.
Thenwe introduce Instance-Specific Prompt to compensate the collapsed temporal information mixedspatial knowledge.
Finally, the specific class-incremental training details for subsequent tasks with
Condensed Frames are given.
3.1 Problem formulation
The purpose of video class-incremental learning is to train a model F (; Θ)
=
h ( f (; θ ); ξ ) parameterized by Θ as the tasks {T 1 , T 2 , · · · , T K } arrive in a sequence, where f (; θ ) is the feature extractorand h (; ξ ) is the classification head.
Each task T K has its specific dataset D k
= { ( d ki , y ki ) ,
y ki ∈ L k }whose labels belong to a predefined label set L k and have not appeared in previous tasks.
Thememory-based methods [37, 40, 50] have been proved to be effective in preventing catastrophicforgetting.
Specifically, after the incremental training step k , a memory bank M k will be establishedfor the dataset D k in this task to store the representative exemplars or features.
The stored exemplarsfrom previous datasets M 1: k = M 1 (cid:83) M 2 (cid:83) · · · (cid:83) M k will be used in subsequent incremental tasksto mitigate forgetting.
At the incremental step k , the model F (; Θ k ) is trained from F (; Θ k − 1 ) with
D ′ k
= D k (cid:83) M 1:( k − Embedding Feature Framesample ⋮ ⨂ ⨂ ⨂ ⊕ ⊕ V !"
𝐼 ! #" 𝐼 ! $" 𝐼 !%" Frame Condensing
Instance-Specific Prompt 𝐼 !"
𝑃 !"
𝐿 &’( 𝐿 )( 𝐿 )& 𝑓( ;Θ " ) 𝑓( ;Θ " ) 𝑓( ;Θ " ) ⨁ : Pixel-wise Addition ⨂ : Multiplication
GroundTruth 𝑤 !"
# 𝑤 !
$# 𝑤 !
# 𝐿 &’&
Condensed Frame
Prompt (𝑃 !"
+𝐼 !" )
Video Frames h h
Classification Score
3.2 FrameMaker
Frame Condensing.
We follow the standard protocol of video class-incremental methods [37, 50],which are based on memory-replay strategy and knowledge distillation.
Differently, we devotes toreducing the number of stored frames for representative videos to achieve memory-efficient videoclass-incremental learning.
Formally, we first select a subset of video instances V k by herdingstrategy [40] from D k after the incremental step k .
Given a video V ki ∈ V k , we then sample Tframes { I ki 1 , I ki 1 , · · · , I kiT } uniformly and use them to condense a more representative one.
To thisend, we define learnable weights W ki = { w ki 1 , w ki 2 , · · · , w kiT } for the i -th video instance V ki inexemplar set V k .
And the condensed frame can be calculated as:
[Equation]
where C , H and W are the size of channel, height and width of input frames, respectively.
Next,the condensed frame I ki is excepted to empower the same or much similar expressive ability as theoriginal video clip.
Hence, the embedding features of the condensed frame extracted from currentmodel should be consistent with the feature of the original video clip:
[Equation]
where f (; θ k ) is the feature extractor of current model F (; Θ k ) .
This consistency regularization canfoster the condensing weights to exploit memory-efficient expressions of video clips.
To furtherimprove the adaptability of the condensed frames to the current model, we employ cross entropy lossto supervise the classification confidence from the condensed frame:
[Equation]
The total objective function for condensing weights W ki is given by:
[Equation]
With the methods described above, we can learn a condensed frame with effective representation foreach video based on current model.
Instance-Specific Prompt.
However, it is undeniable that when a video is condensed into oneframe, the temporal dynamics and complete spatial information of the original video must be lost tosome extent.
Therefore, an intriguing perspective of prompt, termed as Instance-Specific Prompt, isproposed to replenish missing spatio-temporal cues for the condensed frame.
Specifically, we first construct a learnable prompt P ki ∈ R C × H × W for each exemplar video V ki ,which shares the same spatial resolution as its original clip.
The prompt P ki is then summed with the condensed frame I ki pixel by pixel, and learns to pull the embedding feature of the summed frame( I ki + P ki ) and the video clip V ki together, which is similar to Eq. 2:
[Equation]
Notably, the Eq. 2 for condensing weights is difficult to learn the satisfactory spatio-temporal featuresdue to the collapse of the temporal dimension.
Nevertheless, the Eq. 5 can enrich the representationsof condensing frames by introducing more flexible learnable parameters in the input space.
Besides,the Cross-Entropy loss is also utilized to enhance the semantic perception:
[Equation]
Theoretically, the condensing weights W ki and the prompt parameters P ki can be jointly updatedby L p =
L pf + L pce .
Interestingly, we observe that the flexible prompt P ki leads to the condensingweights W ki being under optimized.
Hence, we give the final training objective function for framecondensing as:
[Equation]
where additional L c f and L cce are added to achieve stronger constraint on W k i for its effective training.α , β , γ and η are the balance weights for each term, and they are empirically set to 1.0 unlessotherwise specified.
3.3 Training
It is worth noting that FrameMaker aims to condense the stored frames for representative videos, andother class-incremental learning steps still follow the standard pipeline.
Specifically, when trainingthe incremental step k , we use the dataset D ′ k
= D k (cid:83) M k − 1 to update the model from F (; Θ k − 1 ) ,where D k is the dataset of the task k which consists of videos belonging to L k , and M k − 1 is memorybank which contains condensed frames generated by FrameMaker.
The training samples from D kand M k − 1 are alternately fed into the current model according to the proportion of their samplenumber.
To further prevent the catastrophic forgetting, we also employ the knowledge distillationmethod proposed in PODNet [11] to transfer the knowledge from the previous model F (; Θ k − 1 ) tocurrent model F (; Θ k ) .
The total objective function for task k is given by:
[Equation]
where L dce and L mce are the Cross Entropy losses for new task data D k and condensed frame exemplars
M k − 1 , respectively.
And L mdist is the knowledge distillation loss [11] for condensed frame exemplars.
Experiments
4.1 Experimental Setup
Datasets and evaluation metrics.
The proposed FrameMaker is evaluated on three standard actionrecognition datasets, UCF101 [45], HMDB51 [24] and Something-Something V2 [15].
HMDB51dataset consists of 6.8K videos belonging to 51 classes from YouTube or other websites.
UCF101dataset contains 13.3K videos from 101 classes.
Something-Something V2 is a crowd-sourced datasetthat includes 220K videos from 174 classes.
For UCF101, the model is trained on 51 classes first, and the remaining 50 classes are divided into5, 10 and 25 tasks.
For HMDB51, we train the base model using videos from 26 classes, and theremaining 25 classes are separated into 5 or 25 groups.
For Something-Something V2, we first train84 classes in the initial stage, and generate the groups of 10 and 5 classes.
To evaluate the performance of class-incremental learning, we infer the test videos from all seencategories after each task, and finally, report the average accuracy of all tasks.
Following [37], twodifferent metrics are reported, i.e., CNN and NME.
CNN refers to training a fully-connected layer forextracted features, which is a standard classification protocol.
NME is proposed by iCaRL [40], whichassigns the labels for test data by comparing the feature embeddings with the mean-of-exemplars. [Table] [Table] [Table]
Implementation details.
TSM [31] is employed as our backbone, and we follow the data preprocessing procedure of TSM.
For UCF101, we train a ResNet-
TSM
for 50 epochs with a batch sizeof 256 from an initial learning rate of 0.04.
For HMDB51 and Something-Something V2, we traina ResNet-50 TSM for 50 epochs with a batch size of 128 from an initial learning rate of 1e-3 and0.04, respectively.
All used networks are first pre-trained on ImageNet [8] for initialization.
Thesesettings are consistent with TCD [37].
We train all models on eight NVIDIA V100 GPUs and use
PyTorch [38] for all our experiments.
4.2 Comparison with State-of-the-art Results
This section, compares our proposed FrameMaker with the existing class incremental learningapproaches under multiple challenging settings on three datasets: UCF101 [45], HMDB51 [24] and
Something-Something V2 [15].
For a fair comparison, we use the same exemplar memory size perclass, model structure and pre-training initialization as the existing methods [37].
Table 1 summarizes the results on UCF101 and HMDB51, which shows that FrameMaker outperformsother methods consistently under different configurations in terms of both CNN and NME scores.
The average accuracy of our FrameMaker surpasses TCD by around 3.0% on UCF101 and 2.0% on
HMDB51 in terms of CNN, respectively.
These results demonstrate that one condensed frame canalso be equipped with effective spatio-temporal representation.
FrameMaker is compared with recent advanced methods on Something-Something V2 in Table 2.
Weshow FrameMaker sets new state-of-the-art performance under both challenging settings.
Although
FrameMaker only uses one condensed frame for future replay, it still achieves decent performance onthe large-scale motion-sensitive dataset.
We speculate that these gains mainly come from two aspects:(1)
The proposed Instance-Specific Prompt effectively rescues the lost spatio-temporal details, whichis also discussed in Section 4.3.
(2) Memory-efficient FrameMaker allows us to store more exemplarvideos with less memory consumption, which greatly alleviates catastrophic forgetting.
4.3 Ablation Study
In this section, we present ablation studies to analyze the properties and the effectiveness of
FrameMaker.
If not specified, the ablation studies are performed on UCF101 with 10 steps and
[Table]
[Table]
[Table]
HMDB51 with 5 steps.
For comparison with storing all frames, we select 5 exemplar videos for eachclass.
Frame Condensing and Instance-Specific Prompt.
To show the effectiveness of Frame Condensingand Instance-Specific Prompt, in Table 3, we compare them with the following alternative methods toproduce stored frames for future replay: (1) All Frames, which simply saves the whole video.
One Random Frame, which randomly selects one frame for each exemplar video.
(3) One Condensed
Frame, which is generated by our Frame Condensing procedure.
We observe that Frame Condensingwith Instance-Specific Prompt leads to at least on par or better performance than preserving all frameswhile using just one condensed frame under all settings on both datasets.
In particular, InstanceSpecific Prompt yields an improvement of 2.07%, and 0.22% using randomly sampled frames on
UCF101 and HMDB51, respectively, which implies that prompts can always replenish the ignoredsptiao-temporal cues in video frames.
It is also promising to see that one condensed frame withoutprompt can surpass that use of all frames on UCF101 but not on HMDB51. We explain it by thestrong motion bias exhibited in HMDB51. Hence the introduction of Instance-Specific Prompt aimsto compensate for the lost details for condensed frames and reaches comparable results compared tostoring all frames with less storage.
Loss terms.
To preserve the best spatio-temporal information in the condensed frames, the distillationloss L *f and the cross entropy loss L *ce are both applied for Frame Condensing and Instance-Specific
Prompt.
Table 4 presents the results with Frame Condensing from several different combinationsof loss terms.
Note that if both L cf and L cce are discarded, the condensing weights W ki will notbe optimized, resulting in the condensed frame being equivalent to the average frame and poorperformance.
Hence, uniformly condensed frames are semantically inefficient.
L cce provides moredirect semantic supervision, which leads to better accuracy.
The combination of L cf and L cce canbe further improved, which indicates the complementarity between them.
Similar experiments for Instance-Specific Prompt are shown in Table 5.
One noticeable thing is that once introducing prompt,we only need to calculate the final L pf and L pce to realize the joint update of condensing weights W kiand prompt P ki .
However, we observe that the learned weights, in this case, tend to be average, whichindicates that W ki is under-optimized.
We speculate that this is due to the strong plasticity of prompt,which leads to the insufficient optimization of W k i .
To this end, we further employ L c f and L c ce for W ki as the guidance.
The improvement in Table 5 and the visualizations in Figure 3 show that W kireceives effective learning.
The number of frames used for Frame Condensing.
As discussed in Section 3.2, the T frames areuniformly sampled from a representative video for Frame Condensing.
In Table 6, we experimentto show the influence of T in video class-incremental learning.
We observe that the performanceincreases as T increases for both CNN and NME.
However, the performance is saturated when using16 frames, which is in line with the conclusion of existing methods [37, 50], i.e., storing more framesin a video does not necessarily deliver performance improvement.
This phenomenon reveals that
FrameMaker can empower one Condensed Frame to absorb plenty of spatio-temporal features fromoriginal video frames.
Memory budget comparison.
We now compare the memory budget of FramMaker with existingapproaches.
To make a direct comparison, we use the definition of the working memory size interms of stored frames, following [50].
The results are summarized in Table 7.
We can see thatour FrameMaker costs only 1.2Mb memory, which surpasses TCD [37] using 6.0Mb by 0.21% and1.63% in terms of CNN and NME.
FrameMaker saves 80% of memory space with the comparable Memory Per [Table] [Table]
[Table] [Table] [Table] [Table]
[Table]
[Table]
Memory Per Class
[Table]
FrameMaker
[Table]
[Table]
[Table]
[Table]
[Table]
Memory Per Class 1 F
[Table]
[Table]
FrameMaker
[Table]
[Table]
[Figure]
Meanwhile, we further increase the stored videos but keep the same memory withexisting methods.
FrameMaker can still effectively promote performance and shows superior abilitiesto fight catastrophic forgetting.
Our results are about 3% ahead of TCD in both indicators.
Besides,we also attempt to select the same number of videos as the existing methods.
However, we observethat the accuracy of the trained linear classifier, i.e., CNN, is weaker with a small number of videoinstances.
Interestingly, NME evaluated by the exemplar class centre yielded fairly or even betterresults, implying that the model suffers only slight forgetting.
We hypothesize that this is caused bythe poor feature diversity that a few condensed frames can provide.
Therefore, the classifier tends tooverfit fixed sample features, resulting in poor generalization.
Visualization of condensing weights and instance-specific prompts.
To intuitively understand our
FrameMaker, we provide visualization of condensing weights, Instance-Specific Prompts, condensedframes and learned prompts in Figure 3.
To save space, we uniformly select the most typical 4 framesfrom T frames for display.
Based on this, we have the following fascinating observations: (1) Forframes with strong scene bias, the learned weights are more uniform, while those with motion biasare not.
For example, the action classification for (a) and (b) in Figure 3 does not depend on a specificframe.
Hence the condensing weight for each frame is roughly the same.
In contrast, stair climbingand hugging in (c) and (d) only occur in some specific frames, and those keyframes are highlightedby condensing weights.
This demonstrates that it is reasonable to condense multiple frames into oneframe since our proposed Frame Condensing can assist the learning of which frame is meaningful forthe action recognition task.
(2) It is difficult for humans to absorb useful knowledge from the learnedprompts shown in Figure 3, and even they seem to be the same.
It should be emphasized that theprompts are different.
We try to share the same prompt for all videos to verify this.
The experiment isconducted on UCF101 with 10 steps, and the performances decrease by 0.83% and 0.78% in terms of CNN and NME, respectively. This fully proves that our Instance-Specific Prompt is to replenish thespecific information for different instances.
[Figure]
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[Figure]
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[Figure]
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[Figure]
H a nd s t a nd
D r
i bb l e
C a t c h
[Figure]
hoo t bo w R i d e ho r s
[Figure]
P ou r
W a l k
H
[Figure]
Per-category and Per-task Analysis.
We depict the task-wise and category-wise accuracy withdifferent forms of stored frames in Figure 4, i.e ., “ All Frames ", “ Frame Condensing without InstanceSpecific Prompt " and “ Frame Condensing with Instance-Specific Prompt ".
As expected, integratingmultiple frames into one condensed frame does undermine performance in almost all cases unless
Instance-Specific Prompt is introduced.
As shown in Figure 4(a) and (b), the gains on incrementaltasks yielded by Instance-Specific Prompt mainly fall in the later tasks, which implies that thespatio-temporal knowledge absorbed by prompt can effectively alleviate forgetting.
Meanwhile, from
Figure 4(c) and (d), we also observe that Frame Condensing can lead to the performance degradationof actions with large temporal and spatial variations or short duration, such as “ CliffDiving ", “ Ridehorse " and “ Hug ", etc.
Therefore, Frame Condensing with only a few parameters is difficult to capturethese complex spatio-temporal interactions.
However, Instance-Specific Prompt helps alleviate thisissue since it recovers the lost spatio-temporal details in Frame Condensing for each instance.
Discussions
Limitations.
Compared with the previous video incremental learning approaches, FrameMakerprovides a simple framework that significantly reduces the amount of memory required for eachexemplar video.
Nevertheless, FrameMaker may fail when the memory budget is highly constrained,which needs to be further explored in future work.
Societal Impacts.
Although FrameMaker only stores condensed frames in memory buffer for experience replay, which still retains some information about the original video.
Therefore, FrameMakermay be used in applications with privacy concerns [42].
Conclusion.
This paper proposes FrameMaker, a memory-efficient approach for video classincremental learning.
It explores how to learn an effective condensed frame with less memoryfor each video that can be applied to the future incremental task.
FrameMaker mainly consists oftwo key components, i.e., Frame Condensing and Instance-Specific Prompt .
The former learns agroup of condensed weights for each video to integrate multiple frames into one condensed frame,while the latter learns to retrieve the collapsed spatio-temporal structure for the condensed frame.
Inthis way, FrameMaker offers better results with only 20% memory consumption compared to recentadvanced methods and sets a new state-of-the-art class-incremental learning performance on multiplechallenging datasets.
Memory-based class-incremental learning is one of the effective methods tofight forgetting.
The efficient utilization of limited space is worth exploring in future work.
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