TempMe: Video Temporal Token Merging for Efficient Text-Video Retrieval

Question 5: Metric Suggestion. Presenting an overall sum@R can be unclear for readers. R@1 is generally a more meaningful metric than sum@R and would offer a clearer understanding of the model's performance.

Answer: Thanks for the suggestion. We agree that R@1 directly indicates the model's top-1 retrieval accuracy. To improve clarity, we have included R@1 in all experimental tables in the Experiments section of our revised paper. Regarding the use of R-Sum, it is the sum of R@1, R@5, and R@10. Metrics like R@5 and R@10 are particularly valuable in practical applications where users may prefer having multiple retrieval options for further selection. Thus, R-Sum provides a comprehensive evaluation of the model's retrieval performance across different ranks.

Question 6: If the memory usage of TempMe proves to be more efficient compared to previous PEFT or full finetuning methods, I would consider increasing my score.

Answer: We sincerely appreciate your feedback and are glad to address your concern regarding memory usage. In Table 5, we evaluate TempMe against previous PEFT methods (see our answer to Question 2). Compared to previous PEFT TVR methods such as VoP and DGL, our TempMe requires significantly less memory during training and inference, validating the efficiency of our memory optimization. Specifically, Our TempMe achieves a significant 1.4% R@1 improvement with only 65.6% training memory usage. Additionally, in Table 5 of our revised paper, we present results for full fine-tuning. Compared to CLIP4Clip, TempMe achieves a significant 3.6% R@1 improvement with only 75.2% training memory usage. These results confirm that TempMe offers a more memory-efficient solution.

Table 5. Memory usage comparisons of PEFT methods on MSRVTT with CLIP-ViT-32

Question 3: Limited Novelty. The intra-clip merging process is highly similar to the original token merging approach [3], which limits the novelty of this method. In fact, the flowchart for intra-clip merging in Figure 3 closely resembles the diagram in Token Merging (ToMe), making it difficult to discern substantial differences. Essentially, the only distinct contribution is the cross-clip merging mechanism.

Answer: Thank you for your feedback. (1) To clarify, ToMe and our TempMe address different domains and challenges. ToMe lacks mechanisms to reduce temporal redundancy or fine-tune temporal modeling across frames, both of which are critical in text-video retrieval. In contrast, our innovative framework holistically integrates multiple proposed components, achieving state-of-the-art results in both accuracy and efficiency. (2) While we acknowledge that intra-clip merging is inspired by ToMe, its integration into the ClipMe Block serves a distinct and complementary purpose to novel cross-clip merging. As depicted in Figures 5b and 5c of our manuscript, intra-clip merging further optimizes token reduction, complementing cross-clip merging to enhance efficiency without sacrificing accuracy. Unlike ToMe, which focuses solely on spatial redundancy within a single image, our ClipMe Block combines cross-clip merging and intra-clip merging to enhance temporal modeling and reduce temporal redundancy.

Question 4.1: Efficiency and Early Layer Merging. Performing token merging in earlier layers of CLIP could theoretically reduce both GFLOPS and memory usage, though it might harm performance metrics like R@1. TempMe, however, only applies merging in the last three layers with a 12-6-3-1 merging pattern.

Answer: Thank you for your insightful comments. (1) As shown in Table 3, we agree that early layer merging can reduce GFLOPs and training memory usage. However, it may lead to higher inference memory usage due to limitations in Attn Capacity (see our answer to Question 2). Specifically, early layer merging’s Attn Capacity is 171 at layer 3, potentially increasing inference memory usage. (2) Additionally, Figure 5a in our manuscript shows that merging at earlier layers leads to noticeable performance degradation. This is likely because early layers in the CLIP image encoder capture low-level features, which are critical for frame-specific details but inadequate for modeling temporal relationships. In contrast, merging in later layers takes advantage of more semantic features that are better suited for learning spatio-temporal relationships.

Table 3. Comparisons with early layer merging on MSRVTT with CLIP-ViT-32

Question 4.2: A potential improvement could be to increase the input frames and match GFLOPS with previous PEFT methods, potentially enhancing R@1 without excessive memory usage. Additionally, it would be beneficial to provide memory usage details for CLIP-ViT-32 to understand whether further optimization is feasible.

Answer: Thank you for your insightful suggestions. For fair comparisons, our TempMe is consistent with existing PEFT TVR methods in sampling 12 frames on MSRVTT in Table 2 of our manuscript. To explore the impact of longer input frames, we conduct additional experiments in Table 4. Increasing the input frames to 18 significantly improves performance by leveraging more temporal information. However, this comes at the cost of higher GFLOPs and memory usage, as each GPU needs to process 32x18 frames. Compared to previous PEFT TVR methods like VoP and DGL, our TempMe with 18 frames achieves a significant improvement of 3.2% R@1 and 6.1% R-sum under comparable inference memory usage. These results highlight the effectiveness of our TempMe in utilizing memory resources efficiently while achieving superior performance. These experiments will be included in the final version.

Table 4. Effect of longer input frames on MSRVTT with CLIP-ViT-32

We sincerely appreciate your valuable and constructive feedback, which has greatly assisted us in enhancing the overall quality of our manuscript.

Question 1.1: Limited Improvement in R@1 for T2V Retrieval. The R@1 improvement over previous methods is relatively small, especially given the advances in MLLM models. In 2023, T2V retrieval methods like HBI and Cap4video on CLIP-ViT-32/16, R@1 have reached around 48/50 (e.g., Cap4Video, HBI).

Answer: Thank you for your observation. It is important to note that methods like HBI and Cap4video perform full fine-tuning, where the entire backbone is trained (as discussed in the Related Work section of our revised paper). In this paper, we focus on parameter-efficient fine-tuning (PEFT) methods, where the backbone remains frozen, and only minimal trainable parameters are used. Compared to previous SOTA methods, our TempMe achieve both superior performance and computational efficiency. Specifically, compared to VoP, we achieve a 1.4% R@1 improvement with only 3.6% trainable parameters and 60% GFLOPs. Similarly, compared to DGL, we achieve a 1.5% R@1 improvement with only 60% trainable parameters and 52% GFLOPs.

Question 1.2: I suspect the limited improvement may stem from TempMe still focusing on video merging within the encoder, without further optimization after obtaining the clip-level representation.

Answer: Thank you for your insightful observation. For fair comparisons, our TempMe is consistent with existing PEFT TVR methods in computing similarities based on clip-level representations without additional optimization. We recognize the potential benefits of further optimization after obtaining the clip-level representation. Due to limited time, we conduct preliminary experiments by incorporating a parameter-free Text-Frame Attention Encoder in [1] to enhance video features (denoted as PFSim). With PFSim, TempMe achieved a 0.5% improvement in R@1 and a 1.2% increase in R-Sum, which validates your suggestion. These results will be included in the final version. Thank you for highlighting this valuable perspective, which we plan to investigate further in our future research.

[1] Jin, P. et al., "DiffusionRet: Generative Text-Video Retrieval with Diffusion Model," ICCV, 2023.

Table 1. Further optimization after backbone on MSRVTT with CLIP-ViT-32

Question 2: Lack of Memory Usage Comparison. Efficiency is not solely about GFLOPS—memory usage for training and inference is also crucial. While TempMe provides training acceleration, memory optimization would be even more beneficial. In Table 5, the authors report memory usage for CLIP-ViT-16, but it would be helpful to see detailed comparisons for CLIP-ViT-32 as well, particularly relative to previous methods.

Answer: Thank you for raising this important point. In Table 2, we presents the memory useage during training and inference for CLIP-ViT-32. For all experiments, memory usage during training and inference are measured on 4 A100 GPUs, each processing a batch size of 32. These results will be included in the final version.

(1) Although our TempMe achieves lower GFLOPs, it exhibits comparable inference memory usage to ToMe. We speculate that this is partially influenced by the quadratic complexity of attention mechanisms, particularly the maximum token length processed (denoted as Attn Capacity). Specifically, ToMe’s Attn Capacity is 50 at layer 1, while our TempMe’s Attn Capacity is 118 at layer 11. Additionally, a similar trend in memory usage is observed in full fine-tuning with CLIP-ViT-16 in Table 5 of our revised paper. Despite comparable inference memory usage, our TempMe achieves a significant performance improvement, with a 3.2% increase in R@1 and a 7.2% increase in R-Sum, demonstrating its overall effectiveness.

(2) Compared to previous PEFT TVR methods such as VoP and DGL, our TempMe requires significantly less memory during training and inference, validating the efficiency of our memory optimization. Specifically, Our TempMe not only improves 1.4% R@1 but also achieves greater efficiency in terms of both model complexity and memory usage.

Table 2. Memory usage comparisons on MSRVTT with CLIP-ViT-32

We sincerely appreciate your time and effort in reviewing our paper. We would like to kindly inquire if our revisions and responses have addressed your concerns. Your further feedback would be invaluable.

Thank you very much for positive and thoughtful comments, which have been instrumental in refining and enhancing the overall quality of our manuscript.

Question 1 & Question 3: (Question 1) Although the paper addresses spatial-temporal redundancy in text-video retrieval, there is already substantial work on token merging and pruning in video processing. This overlap may affect the perceived uniqueness of the proposed approach. (Question 3) The authors should clarify how this work distinguishes itself from existing methods for token pruning or merging.

Answer: Thank you for the insightful question. In this work, we focus on Text-Video Retrieval using pretrained CLIP, where each sampled frame is processed as an independent token set. Existing image/video token compression methods are limited to pruning or merging tokens within a single image/video token set, without addressing token compression across multiple sets or incorporating temporal fine-tuning. In contrast, we have explored a practical and feasible path to reach both superior performance and computational efficiency. By fruitfully integrating parameter-efficient fine-tuning and token compression techniques, we propose TempMe and reach state-of-the-art performance. TempMe can progressively merge different frame token sets, and thus minimize spatio-temporal redundancy and enhance temporal modeling across frame. We have clarified this distinction in the Related Work section of our revised manuscript.

Question 2: A few symbols lack adequate definitions, which may hinder readability. For instance, "R_c" in Section 3.2, while defined in Figure 3 as the ratio of kept tokens, should also be described in the text when first introduced for clarity.

Answer: Thank you for pointing out this issue. In Section 3.2 of our revised paper, we have clarified the definition of $R_C$ and $R_I$ by explicitly describing it in the text when it is first mentioned.

Thank you for your acknowledgment of our efforts in addressing your concerns. Although the ICLR scoring system doesn’t include a 7, we’ll happily take your "implicit 7" as a sign of encouragement. We sincerely appreciate your positive and constructive evaluation.

Thank you very much for positive and thoughtful comments, which have been instrumental in refining and enhancing the quality and clarity of our manuscript.

Question 1: Abstract: the abstract is hard to follow and is not clear if the method addressed inference speed-ups or training time speed-ups or both.

Answer: We appreciate your comment on the clarity of the abstract. In the revised abstract, we have refined it for improved clarity and coherence. Specifically, we now emphasize that our method achieves efficiency in both training and inference while maintaining parameter efficiency. We have replaced "inference-efficient" with "training-inference efficient" to precisely convey this dual focus. This updated terminology will also be consistently applied throughout the paper in future revisions to enhance overall clarity and coherence.

Question 2: Overall the quality of the writing can be improved because it's not straightforward to follow the paper, for example in the introduction the transitions between paragraphs are abrupt.

Answer: Thank you for pointing out this issue. In the Introduction section of our revised paper, we have improved the transitions between paragraphs in the Introduction section to ensure smoother flow and better readability. Additionally, we will conduct a comprehensive review of the entire manuscript, using writing tools and feedback from native speakers, to further enhance clarity and readability in the final version.

Question 3: The choice of sampling 12 frames for MSRVTT and 64 frames for ActivityNet seems arbitrary.

Answer: Thank you for your observation. The number of sampled frames (12 for MSRVTT and 64 for ActivityNet) is chosen to align with previous methods like DGL, as mentioned in Lines 303–305 of our manuscript. This choice facilitates a fair comparison of our method with previous methods.

Question 4: Is any part of the method that is specifically designed for text-video retrieval or could it be applied for other text-video tasks as well?

Answer: Thank you for your question. While the primary focus of our method is text-video retrieval (TVR), its design is inherently versatile and can be employed for other text-video tasks. Specifically, we focus on a frame-wise processing paradigm in TVR, where each sampled frame is processed by pretrained CLIP. Our proposed TempMe progressively merges different frames, achieveing both efficiency and performance improvements. This design can be applied for other text-video tasks that utilize a similar frame-wise processing paradigm. As shown in Table 6 and 7 of our manuscript, we successfully apply TempMe to VideoQA task and integrate it with video backbones like UMT, demonstrating its generalizability and broader applicability.

Question 5: [1] shares a similar idea, can you please summarize the similarities and differences between the proposed method and [1]? [1]Yao, Linli, et al. "DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models." arXiv preprint arXiv:2405.20985 (2024).

Answer: Thanks for the supplement. In our revised paper, we have discussed DeCo in the Related Work section. DeCo adopts 2D Adaptive Pooling to downsample vision tokens at the spatial level for MLLMs. In contrast, our TempMe concentrates on temporal-level processing for text-video retrieval, which effectively learns temporal information and efficiently reduces temporal redundancy.

We sincerely appreciate your positive feedback and are delighted that our responses have addressed your concerns. Your inclination towards acceptance is encouraging, and we value your support for our work.

We sincerely appreciate your time and effort in reviewing our paper. We would like to kindly inquire if our revisions and responses have addressed your concerns. Your further feedback would be invaluable.