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Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding

This paper explores the task of Temporal Video Grounding (TVG) where, given an untrimmed video and a query sentence, the goal is to recognize and determine temporal boundaries of action instances in the video described by the provided natural language queries. Recent works solve this task by directly encoding the query using large pre-trained language models (PLM). However, isolating the effects of the improved language representations is difficult, as these works also propose improvements in the visual inputs. Furthermore, these PLMs significantly increase the computational cost of training TVG models. Therefore, this paper studies the effects of PLMs in the TVG task and assesses the applicability of NLP parameter-efficient training alternatives based on adapters. We couple popular PLMs with a selection of existing approaches and test different adapters to reduce the impact of the additional parameters. Our results on three challenging datasets show that TVG models could greatly benefit from PLMs when these are fine-tuned for the task and that adapters are an effective alternative to full fine-tuning, even though they are not tailored for our task. Concretely, adapters helped save on computational cost, allowing PLM integration in larger TVG models and delivering results comparable to the state-of-the-art models. Finally, through benchmarking different types of adapters in TVG, our results shed light on what kind of adapters work best for each studied case.


Introduction
Temporal Video Grounding (TVG) is a fundamental task in Computer Vision (CV), where the goal is to have models recognize and determine temporal boundaries of action instances in videos [51,13,12] using queries provided in natural language [9,19]. Over the past few years, interest in this task has grown substantially, which has led to the release of several models that either use propose-andrank techniques or directly predict the starting and ending temporal locations [11,47]. The latter has focused mostly on finding better ways to fuse and compare the representations of the natural language query and the input video, with model sizes increasingly growing in the past few years.
Following trends in other vision-and-language (V&L) tasks, some of the latest models combine vision encoders and pre-trained language models (PLM), which are first pretrained on large-scale unlabeled data and then fine-tuned on the downstream task at hand. In the Temporal Video Grounding task specifically, we find works that directly encode the natural query using such PLMs [37,56], which are later fine-tuned with the rest of the architecture, or that try to project both the query and video into the same embedding space using a Transformer [63].
Despite these approaches undoubtedly bringing performance improvements to TVG, we note that it is difficult to isolate the effects of the improved language representations, as these works also propose new video-language matching approaches and/or use different video encoders, which act as confounding factors. Another drawback is their computational cost for training, as parameter counts grow substantially once pre-trained models are incorporated. For example, TMLGA [47] has about 4.7M parameters, which requires roughly 18.8MB of storage 1 . If we combine it with a large PLM such as GPT3 [3], we would need an extra 700GB of memory to store all parameters.
To address this problem, several parameter-efficient training methods have been recently proposed for both Natural Language Processing (NLP) [20,1,23] and CV models [45,53,64]. Among these approaches, adapters [20,1] and their variations have been particularly effective 2 . In this setting, the idea is to add a small set of parameters to intermediate layers of the PLM, which allows us to attain a performance as high as when fine-tuning but by training only a small set of parameters. Adapters have been successfully combined with vision models for several tasks [53,24,64,68], showing that it is possible to use a few parameters to learn to fuse vision and language representations without losing performance. However, we note that the efforts so far have focused only on image [64] and video [38] classification tasks, or on leveraging pre-trained generative models, for example, by re-casting existing visionand-language tasks as language generation [53].
Furthermore, while the number of trainable parameters is a major concern, we cannot overlook the amount of data TVG models require to be trained. For example, DORi [48] uses two types of visual features: Temporal features to represent the activities, and spatial features to represent humans and objects in each of these selected frames. Using such features led to significant improvements in performance compared to previous proposal-free TVG models. However, it also significantly increased the memory necessary to load each training sample.
In light of this, in this paper, we study the effects of large PLMs in Temporal Video Grounding and investigate the applicability of NLP adapters for a parameter-efficient integration. We couple popular PLM models with a selection of previous work approaches, allowing us to isolate and understand their effects on performance. Concretely, we analyze ExCL [11], TMLGA [47] and DORi [48], three proposal-free TVG models with different levels of complexity. Moreover, as introducing such large architectures substantially increases the computational cost of existing models, we also test several parameter-efficient training alternatives based on adapters. Unlike previous work on adapters for V&L, our training signal is not a regular classification loss and comes from the visual modality, which keeps us from casting our task as language generation.
We conduct thorough experiments on three challenging datasets for TVG, Charades-STA [9], ActivityNet Captions [26] and YouCookII [70,69], thus covering videos containing a large variety of daily activities with varying lengths (between 30 and 755 s) and with different query lengths (between 7.2 to 13.48 words on average). Concretely, we seek to answer the following research questions: RQ1: Does incorporating a PLM improve the performance of existing TVG models?
RQ2: Are adapters an alternative to full finetuning of the PLM parameters within TVG?
RQ3: Is there an adapter that works best for TVG?
RQ4: What is the impact of different PLMs? RQ5: How does the combination of existing TVG models with PLM trained with adapters perform against stateof-the-art models?
Our results offer concrete answers to these questions, indicating, for example, that PLMs can deliver significant performance boosts when combined with existing TVG models, sometimes surpassing strong baselines, especially when using adapters. This result ultimately shows that adapters can be an effective alternative to full fine-tuning in our task with minimum impact in terms of parameter count, sometimes performing better than full fine-tuning while training only 10% of the total number of parameters. Ultimately, the parameter-efficient integration of PLMs into TVG models using adapters delivered results comparable to state-of-theart TVG models.
We believe our results help clarify the role of language encoders in the TVG task, allowing us to quantify to what extent they can improve the grounding capabilities of existing models. Our experiments also highlight the relevance of parameter-efficient training alternatives, which, with the advent of new, deeper Transformer-based models for image and video understanding, are likely to become more relevant in the future.
Our contributions can be summarized as follows: (1) We quantify the impact of pre-trained language models in Temporal Video Grounding models, suggesting that these models can greatly benefit from such integration, especially when these PLMs are fine-tuned for the task, (2) We perform the first work on benchmarking different types of adapters on the task of Temporal Video Grounding, shedding light on what kind of adapters work best for each case, and (3) We offer an empirical demonstration that adapters can reach or surpass the performance of full fine-tuning while updating only about 10% of the parameters in our task.

Related Work
Temporal Video Grounding The task of recognizing and determining temporal boundaries of action instances in videos [51,13,12] using queries provided in natural language [9,19]. Work on this task can be divided into two main approaches. On the one hand, we find techniques based on proposal generation, where the idea is to, given a query, output a set of candidate clips which could later be ranked [32,10]. Further research on this approach has mostly focused on reducing the number of proposals by producing query-guided or query-dependent approaches [5,6,58], or on creating maps that can cover diverse video moments with different lengths [65]. Zhang et al. [63] adopted a Transformer-based multi-modal model (MSAT) which is pre-trained for this setting. More recently, models have incorporated contrastive losses to improve perfor-mance further. This is the case of both CPL [67] and MNM [56], which also incorporate Transformer-based components in their pipelines.
The second line of approaches has instead proposed to directly predict the start and end locations using regressions or classifications through the video span [11,47]. Work on this line of research has focused on improving the performance by modelling label uncertainty [47], adding spatial features [48] or improving the text-to-video matching strategies [36,61]. For example, CPN [66] adopted an adhoc graph-based technique. Other approaches, like [36], have focused on exploiting local and global features for better performance. CPNet [29] recently proposed a pyramidlike approach where the model progressively replenishes the temporal contexts and refines the location of the queried activity by enlarging the temporal receptive fields. Finally, two recent approaches, VSLNet [62] and BCPN [37] have proposed to cast the task as visual question answering. Parameter-efficient model training As machine learning models continue to grow, updating their parameters efficiently is becoming increasingly important. One key idea in this context has been to only update newly-added parameters [45,46]. In particular, with the advent of large pre-trained Transformer-based models in Natural Language Processing (NLP) such as BERT [8] and GPT-2 [44], this idea has led to the development of the so-called adapters [20,1,40,50], sub-networks with small parameters that are inserted after every attention and feed-forward layer in a given model. We also find a variety of promptbased approaches, which add trainable parameters into the model inputs [31,27,14]. Alternative techniques, such as sparsely updating a small number of parameters of the model [2,15,54], or low-rank factorization for the weights to be updated [34,22,21] have also been proposed recently. Finally, [16,35] have proposed combining some of the above mentioned techniques to propose a unified parameterefficient training framework. Though we build our study on top of NLP adapters, we deviate from previous work as our approach incorporates the visual modality.
The idea of only updating newly-added parameters has also been proposed in the CV community, with some work predating the advent of large Transformer language models [45,46]. More recently, we find work combining pretrained language models with multi-modal inputs. For example, Tsimpoukelli et al. [55] trained a vision encoder to represent each image as a sequence of continuous embeddings, such that a frozen language model prompted with this prefix generates the appropriate image caption. Yang et al. [59] showed that it is possible to perform zero-shot video question answering by leveraging frozen bidirectional language models. More recently, work by Sung et al. [53] cast multiple video-and-language tasks as text generation, and combine NLP adapters with pre-trained encoder-decoders  Figure 1. Illustration of our proposed approach to incorporate pretrained language encoders and adapters into existing Temporal Video Grounding pipelines. Light orange color refers to trainable parameters, and blue color refers to frozen ones.
such as BART [28] with existing image encoders such as CLIP [43]. The latter has also lately been the target of several studies [24,64,68] that extend parameter-efficient techniques for this model. Though our approach follows a similar trend, combining NLP adapters with multi-modal inputs, we note that their study in the context of video-and-language remains relatively unexplored [38]. Furthermore, in contrast to prior work, which leverages primarily generative models, our interest lies in a grounding task where the training signal comes from the visual modality, which keeps us from casting our task as language generation.

Proposed Approach
Consider a video V ∈ V, represented as a sequence of frames such that V = {v t } with t = 1, . . . , l. Each video in V is annotated with a natural language passage S ∈ S, where S is a sequence of words S = {s j } with j = 1, . . . , m, which describes what is happening at a certain period of time in the video. This interval is formally defined by t s and t e , the starting and ending points of the annotations in time, respectively. The goal of the temporal video grounding task is to predict t s and t e given the video V and the text query S.

Temporal Video Grounding Models
For this study, we selected three models from the literature: ExCL [11], TMLGA [47] and DORi [48]. We focused on proposal-free models, which generally offer better performance, and we were careful only to consider works that used word embeddings. ExCL was the first proposal-free model for TVG; TMLGA improved on it by handling video annotation uncertainty, and later DORi incorporated spatial features.  [20]. The adapter layers are introduced after the multi-head attention and feed-forward layers. Light orange color refers to trainable parameters, and blue color refers to frozen ones.
As shown in Figure 1 (a), these models can be summarized into three main parts: A sentence encoder, a video encoder, and a localization module, which combines information from both modalities and predicts the start and end points of the segment in the video described by the query. While these models heavily explore information from the video input, they make relatively simple use of the textual query, mainly processing pre-trained word embeddings, such as GloVe [39], through a recurrent neural network.
To better understand the role of improved language representations on the performance in the TVG task, we study the effects of PLMs by incorporating them as-is into a selection of existing models from the literature, therefore effectively isolating their impact on the performance across a range of settings. More concretely, we replace the noncontextualized word embeddings with a PLM, as shown in Figure 1 (b). Our modification was guided by considering each TVG model architecture. We tested several ways to incorporate PLMs, such as entirely replacing the text encoder block with the PLM, where most failed to deliver any performance improvement or even a performance comparable with the original models. For example, removing DORi's text encoder and inputting BERT's last layer token representation into the localization layer hurt DORi's performance by almost 2% in mIoU. Therefore, we decided to replace only the word embeddings.

Adapters
A concern when integrating large PLMs with existing TVG models is the alarming number of parameters added. While some TVG models are pretty small, such as TMLGA with about 4M parameters, recent models are exponentially increasing in size and using larger amounts of training data.
For example, DORi has about 10M parameters, more than double than TMLGA. Combining these models even with reasonably-sized PLMs, such as BERT, increases the number of trainable parameters to over 120M. Also, the more sophisticated visual features are used, the larger the training data becomes. For example, the visual features for TMLGA are about 2.1MB per video in the ActivityNet dataset, while for DORi, they are about 82MB, over 39 times larger.
To alleviate this issue, we also investigate several parameter-efficient training alternatives based on adapters to incorporate these PLMs into the existing model pipelines with reduced computational cost, as shown in Figure 1 (c).
Adapters are sub-networks with a small number of parameters that can be inserted in different locations of a transformer-based language model. Figure 2 illustrates how the traditional bottleneck adapter proposed by Houlsby et al. [20] is introduced to a transformer layer in a language model. When training a model to perform a new task, only the parameters in the adapter module are updated, leading to a massive computational cost reduction while achieving comparable performance to full fine-tuning in many downstream tasks, such as textual entailment recognition, sentiment analysis, question answering, among others [20,16].
We note that some of the adapters we consider in our study were designed for specific purposes in NLP, such as the INVERSE adapter, which was proposed to learn language-specific information. Our decision to still include such adapters in our experimental framework is motivated by their relative success in other vision-and-language tasks, as studied by previous work [18,24,53]. Moreover, as our training signal comes from the visual modality, our approach differs from existing work in this context. Therefore, we were interested in offering empirical evidence to further understand these adapters' role in multi-modal scenarios.

Datasets
Charades-STA: Built upon the Charades dataset [52], it provides time-based annotations using a pre-defined set of activity classes and general video descriptions. We use the pre-defined train and test sets containing 12,408 and 3,720 moment-query pairs, respectively. Videos are 31 seconds long on average and have a maximum duration of 194 seconds, with 2.4 moments on average, each being 8.2 seconds long on average and described using 7.2 words on average. ActivityNet Captions: Introduced by Krishna et al. [26] and initially constructed for dense video captioning, consists of 20k YouTube videos with an average length of 120 seconds and a maximum duration of 755 seconds. The videos contain 3.65 temporally localized time intervals and sentence descriptions on average, where descriptions have on average 13.48 words. Following previous works, we report the performance on the combined validation sets.
YouCookII: Consists of 2,000 long untrimmed videos from 89 cooking recipes obtained from YouTube by Zhou et al. [70,69]. Each step for cooking these dishes was annotated with temporal boundaries and aligned with the corresponding section of the recipe. The average video length is 316 seconds and a maximum duration of 755 seconds. Regarding relevant moment segments, each video has 7.73 moments on average, with each segment being 19.63 seconds long and described using 8.7 words on average.

Implementation Details
Temporal Video Grounding Models: Our implementation for TMLGA and DORi is built on top of the original code released by the authors, while we use our own implementation of ExCL. To represent the video input, we follow the original implementations as close as possible. For TMLGA and DORi, we use the I3D features released with the respective papers. These I3D features have size 1024 and are obtained by average-pooling on the last layer, before the classifier head in a I3D network, where the inputs are the raw video frames of dimension 256 × 256, at 25fps. Rodriguez et al. used the I3D pre-trained model trained on Kinetics for ActivityNet and YouCookII released by [4], and the one pre-trained on Charades for Charades-STA. For our implementation of ExCL, we use the features released by the DORi paper, which follows the same protocol, but extracted at 25 fps instead of the original 5 fps 3 . Specifically for DORi, we also use the spatial features (i.e., human and object features) released with the paper.
Adapters: We use the adapters' implementation provided by the adapter-transformers library [41], using the default configurations. In particular, for the Invertible adapters (INVERSE), we only tested the "PfeifferInvConfig", following the configuration proposed in the original paper [42].
Training: Our experiments were performed on a 40-GB NVIDIA A100 GPU. Models were trained using ADAM [25] with a step-based learning rate scheduler. When finetuning the PLMs, we used a scheduler with linear warmup for the PLM parameters, while keeping the learning rate fixed for the rest of the parameters. For more details on the batch size, learning rate and weight decay, we refer the readers to the supplementary material. Evaluation follows previous work, based on two widely used metrics [9], namely the Recall at various thresholds of the temporal Intersection over Union (tIoU or R@α) measuring the percentage of predictions that have tIoU with ground truth larger than certain α, and the mean averaged tIoU (mIoU). We use α threshold values of 0.3, 0.5 and 0.7.

Effect of adding BERT to existing models
To provide an answer for RQ1, we replace the noncontextualized word embeddings in our selected TVG models with BERT, which can be regarded as the current most widely-studied PLM [49,60,7,30]. We compare the original model performance with the performance when finetuning the PLM along with the TVG model training (finetuning), and when freezing the PLM ( ), training only the parameters of the TVG model. To ensure our implementations were correct, we also ran experiments with the original models (ours), achieving performance close to what has been reported in their respective papers. Table 1 shows the results when combining BERT and adapters with the chosen models for TVG. When introducing BERT to the models, we can see that full model fine-tuning leads to an average improvement of 1.38% and 1.24% in mIoU for ExCL and TMLGA, respectively. This result shows that the chosen TVG models can benefit from using PLMs. However, doing so leads to an addition of over 100M parameters to be tuned, greatly increasing the computational cost to train these models. Such an increase is troublesome as we ran out of memory when trying to finetune BERT along with DORi.
One alternative to save on this computational cost is to freeze the PLM when training the TVG model. We can see that this strategy leads to an overall improvement in mIoU of 0.40%, 0.52%, and 0.08% for ExCL, TMLGA, and DORi, respectively, when compared to the original model performance. This improvement is substantially smaller than when fine-tuning the PLM, with cases where using the frozen BERT leads to worse results than when using GloVe, such as with ExCL and DORi on YouCookII. Furthermore, we can see that the models benefited the least from the frozen PLM when tested on the Charades-STA dataset. This result could be related to the fact that the queries in Charades-STA are straightforward, so the word embeddings could already be enough to perform well. Nevertheless, the overall results indicate that fine-tuning is es- sential to getting the full potential of the PLM in our task.

Adapters as an alternative to PLM fine-tuning
Although adapters have shown to be an effective way to deploy PLMs in a parameter-efficient manner in several NLP tasks, to the best of our knowledge, there is currently no evidence to suggest whether or not they could bring similar benefits to the TVG task. Therefore, to answer RQ2, we seek to investigate if using adapters can be an effective alternative to full fine-tuning of the PLM models in the task of TVG. We tested the adapters mentioned in Section 3.2 for all three TVG models with BERT. Our best results are shown in Table 1 (Adapter). We refer the readers to the supplementary material for details.
Our results show that using adapters led to an overall improvement in mIoU of 0.70%, 1.71% and 1.72% for ExCL, TMLGA, and DORi, respectively, over the models' original performance. While the improvement from the adapters for ExCL is smaller than when doing full fine-tuning, adapters led to better performance with TMLGA. More importantly, adapters allowed a significant performance improvement for DORi, as training with adapters requires updating only 16% of the parameters required for full fine-tuning. Furthermore, we can see that in some cases, using adapters leads to better performance than full fine-tuning, such as with ExCL and TMLGA on ActivityNet Captions and ExCL on YouCookII. In summary, these results indicate that despite not being tailored for the task of TVG, the adapters covered in this work can be an efficient alternative to the full fine-tuning of PLM models.

Choice of adapter for TVG
After confirming the effectiveness of adapters in our setting, we were then naturally interested in studying whether  38.92 or not there is a specific type of adapter that works best to solve our task. To provide an answer for RQ3, we rank the performance of each adapter for each dataset and model, and focus on cases that deliver the best and the worst per- formance, also comparing to our setting with a frozen PLM.
The results of our analysis are summarized in Figure 3. We can see that no single adapter can consistently offer the best performance. However, it is possible to see that the bottleneck adapters, such as the HOULSBY, PFEIFFER, and INVERSE are among the best performing ones.
When controlling our results for each dataset, however, it is possible to see that the PREFIX and the INVERSE adapters worked well on Charades-STA, while the PFEIF-FER and HOULSBY adapters worked best for ActivityNet and YouCookII, respectively. We surmise this indicates that the adapter choice is likely more related to the data used to train than to the choice of the model itself. Controlling by model also shows interesting, distinctive patterns. For example, while the PREFIX adapter worked well for TMLGA on Charades-STA and DORi on ActivityNet, it performed poorly with ExCL on ActivityNet and YouCookII.
Another general pattern we could identify is that both the COMPACTER and the PARALLEL adapters did not work well overall, with the latter struggling to deliver good performance in all cases. This regularity of results in delivering bad performance contrasts with our above analysis regarding the best adapters. We think the adapter size (in the number of additional parameters) may play a role in this matter. It is interesting to note that COMPACTER is the adapter with the smallest number of added parameters (0.06M), while the PARALLEL is the second adapter with the largest number of added parameters (7.1M). It might be that the COMPACTER is too small to learn useful information for TVG.
Furthermore, the hyper-parameter search with the PRE-FIX and the PARALLEL adapters was substantially more challenging than with the other adapters. In particular, when training using the PARALLEL adapter, there were many instances where we faced gradient explosions, as with DORi on Charades-STA, or the model converged to poor performance, as with all three models on YouCookII.
Finally, we believe another factor that may affect performance is the location where each adapter is inserted in the PLM architecture. All of the methods tested in this work adapt to specific parts of the transformer layer (e.g., attention heads, feed-forward layers, key/value weights, etc.), except for the PARALLEL adapter, which adapts the whole transformer layer. This observation might indicate that adapting specific parts of the transformer layer is more beneficial for our task.

Impact of different PLMs
After the release of BERT, several pre-trained language encoder variations have been proposed. When tested on extensive NLP benchmarks, some of these models, such as RoBERTa [33], and DeBERTa [17] have proven to be able consistently further improve performance. In this context, we are interested in studying if such performance improvements also translate to better performance in our task in an attempt to provide an answer for RQ4.
Thus, we study the performance of BERT, RoBERTa, and DeBERTa on two datasets, Charades-STA and Activ-ityNet. We do this using the TMLGA model as a pivot, which we chose because, in our experiments with BERT, it offered a good compromise in terms of performance improvements versus computational cost. Our dataset selection for this study is motivated by Charades-STA and Ac-tivityNet having similar video contents but with queries that differ in complexity, i.e., queries in Charades-STA are much simpler than in ActivityNet. We can verify this difference by observing the vocabulary size (748 vs. 9,744 tokens) and length of the natural queries (7.2 vs. 13.48 words per query) and several metrics to measure the grade level of a piece of text 4 (third to fourth vs. fifth to sixth grade level).
The results of our study using different PLMs can be seen in Table 2. We did not include results with PRE-FIX adapters on DeBERTa due to an implementation error on the adapter-transformers library 5 . Furthermore, we also note that in our experiments with the PARALLEL adapter, all the models saturated and reached the same performance on ActivityNet, without learning correctly. Table 3. Comparison between the best performing TVG model with DeBERTa and current state-of-the-art methods in TVG. Results for all compared methods were taken from their respective papers. The best results for each combination of method type (i.e., proposal-free or proposal-based) and dataset are indicated in bold, while the second best results are underlined.

Method
Charades-STA ActivityNet Looking at the results, we can first see that for all three PLMs, using adapters led to an overall performance improvement compared to the frozen PLM. Moreover, we can see that PFEIFFER and the INVERSE adapters seem to provide the best results in both datasets and all PLMs.
Furthermore, while DeBERTa performed the best for Ac-tivityNet, as expected from its performance in NLP downstream tasks, it performed the worst for Charades-STA. We believe this result could be due to the simplicity of the queries in Charades-STA, which might not require all the additional information DeBERTa encodes compared to BERT. It is worth noting that these results were obtained using the first version of DeBERTa, which uses the same type of tokenizer as BERT. We also tested the newer version of DeBERTa, which uses a sentencepiece-based tokenizer and incorporates other model improvements. However, this model performed much worse than the first version on our task showing that using sophisticated tokenizers does not necessarily improve results with simple text queries.
On the other hand, ActivityNet has longer and more complex queries. Our results indicate that in such cases, our task might benefit from using better PLMs. Nevertheless, the best results were achieved when using adapters.

Comparison against state-of-the-art
We finally tackle RQ5 and compare our best-performing models against a selection of approaches from previous work. We achieved our best performance for the Charades-STA dataset by using DORi with DeBERTa+PFEIFFER; for the ActivityNet dataset, DORi with DeBERTa+INVERSE. Considering that the models used in this study are proposal-free, we mainly compare to proposal-free methods, i.e., VSLNet [62], CPN [66], CPNet [29] and BCPN [37]. Nevertheless, we are also interested in observing how our modifications perform against proposal-based methods, such as MS-2D-TAN [65], MSAT [63], CPL [67], and MNM [56]. We note that out of the proposal-free methods, only BCPN uses a PLM (BERT). As for the proposal-based methods, only MS-2D-TAN does not use any form of Transformers in its architecture, while MNM is the only one to use a finetuned PLM (DistilBERT). Finally, while MSAT and CPL use Transformers in their architecture to encode visual and textual information, they do not use PLMs.
The performance summary is shown in Table 3. For this analysis, we only consider two alpha bands for the thresholds as most proposal-based models do not report results at the alpha 0.3.
First, we can see that the best-performing methods are proposal-free, with BCPN achieving the best performance in most of the considered metrics. Furthermore, looking at the mean tIoU, we can see that DORi already performs well against the other methods. Replacing the GloVe embeddings with DeBERTa and using adapters for training provides a significant performance boost, delivering results equivalent to state-of-the-art models.
Moreover, it is interesting to see that while proposalbased methods such as CPL and MNM performed well against all methods on ActivityNet, they could not outperform methods without Transformers on the Charades-STA dataset. In contrast, DORi with DeBERTa and adapters achieved a more balanced performance among both datasets, showing a clear advantage against these methods on the Charades-STA dataset.
Therefore, our results show how TVG models can greatly benefit from adequately incorporating PLMs and making use of parameter-efficient techniques, performing well on datasets with different complexity levels in terms of queries, and achieving results comparable to state-of-the-art methods.

Conclusions
This paper studied the effects of PLMs in the TVG task and assessed the applicability of NLP parameter-efficient training alternatives based on adapters. We coupled BERT, RoBERTa, and DeBERTa, with a selection of previous work approaches, i.e., ExCL, TMLGA, and DORi, and tested different adapters to reduce the impact of the additional parameters. Our results showed that incorporating PLMs greatly improved the performance of TVG models, especially when doing fine-tuning. Moreover, we verified that adapters are an effective alternative to full fine-tuning, even though they were not tailored for our task. They helped save on computational cost, allowing improvements for larger TVG models, such as DORi, and also delivered results comparable to state-of-the-art models. Finally, we observed that while PARALLEL adapters seem to struggle to learn in this task, bottleneck adapters such as HOULSBY and PFEIFFER tend to perform well across all tested TVG models and datasets.