Update README.md

README.md

@@ -45,7 +45,20 @@ Dataset statistics for each dimension in the MMLA benchmark. #C, #U, #Train, #Va
 | **Communication Behavior**| Anno-MI (client)      | 3      | 4,713   | 3,123      | 461      | 1,128     | 10.8            | YouTube & Vimeo   | 8.2 / 600.0                   | 16.3 / 266.0                    | **English** |
 |                           | Anno-MI (therapist)   | 4      | 4,773   | 3,161      | 472      | 1,139     | 12.1            |                   | 9.1 / 1316.1                  | 17.9 / 205.0                    |              |
 
-#### 2.2 License
+
+#### 2.2 Collection Timeline
+- **MIntRec**: Released in 2022/10.
+- **MIntRec2.0**: Released in 2024/01.
+- **MELD**: Collected from TV series (released in 2019/05).
+- **UR-FUNNY-v2**: Collected from publicly available TED talks (released in 2019/11).
+- **MUStARD**: Collected from TV series (released in 2019/07).
+- **MELD-DA**: Dialogue act annotations added to MELD in 2020/07.
+- **IEMOCAP-DA**: Dialogue act annotations added to IEMOCAP (released in 2020/07).
+- **MOSI**: Collected from YouTube opinion videos (released in 2016/06).
+- **IEMOCAP**: Collected from scripted improvisational acting (released in 2008/12).
+- **Anno-MI**: Collected from publicly available YouTube and Vimeo videos (released in 2023/03).
+- 
+#### 2.3 License
 
 This benchmark uses nine datasets, each of which is employed strictly in accordance with its official license and exclusively for academic research purposes. We fully respect the datasets’ copyright policies, license requirements, and ethical standards. For those datasets whose licenses explicitly permit redistribution, we release the original video data (e.g., [MIntRec](https://github.com/thuiar/MIntRec), [MIntRec2.0](https://github.com/thuiar/MIntRec2.0), [MELD](https://github.com/declare-lab/MELD), [UR-FUNNY-v2](https://github.com/ROC-HCI/UR-FUNNY), [MUStARD](https://github.com/soujanyaporia/MUStARD), [MELD-DA](https://github.com/sahatulika15/EMOTyDA), [CH-SIMS v2.0](https://github.com/thuiar/ch-sims-v2), and [Anno-MI](https://github.com/uccollab/AnnoMI). For datasets that restrict video redistribution, users should obtain the videos directly from their official repositories (e.g., [MOSI](https://github.com/matsuolab/CMU-MultimodalSDK), [IEMOCAP and IEMOCAP-DA](https://sail.usc.edu/iemocap). In compliance with all relevant licenses, we also provide the original textual data unchanged, together with the specific dataset splits used in our experiments. This approach ensures reproducibility and academic transparency while strictly adhering to copyright obligations and protecting the privacy of individuals featured in the videos.
 
@@ -96,7 +109,63 @@ This benchmark uses nine datasets, each of which is employed strictly in accorda
 |  19  |    Qwen2-1.5B (SFT)    | 64.00 | LLM  |
 |  20  |    Qwen2-0.5B (SFT)    | 62.80 | LLM  |
 
-## 4. Acknowledgements 
+
+## 4. Data Integrity
+
+All files included in the MMLA benchmark are verified using SHA-256 checksums. Please ensure the integrity of the files using the following checksums:
+
+| File Path | SHA256 Hash |
+|-----------|-------------|
+| `/MMLA-Datasets/AnnoMi-client/test.tsv` | `d555c7131bc54cb61424d421c7a3ec117fa5587c1d4027dd8501321a5d1abc09` |
+| `/MMLA-Datasets/AnnoMi-client/dev.tsv` | `4695dc4e1c360cac53ecd0386b82d10fdda9414ad1d559c0a9491a8981657acd` |
+| `/MMLA-Datasets/AnnoMi-client/train.tsv` | `8e1104e7d4e42952d0e615c22ee7ea08c03d9b7d07807ba6f4fd4b41d08fed89` |
+| `/MMLA-Datasets/AnnoMi-client/AnnoMI-client_video.tar.gz` | `597d9b8c1a701a89c3f6b18e4a451c21b6699a670a83225b7bce5212f5abdfe0` |
+| `/MMLA-Datasets/AnnoMi-therapist/dev.tsv` | `bde3ae0e4f16e2249ac94245802b1e5053df3c9d4864f8a889347fe492364767` |
+| `/MMLA-Datasets/AnnoMi-therapist/test.tsv` | `0ef6ceeba7dfff9f3201b263aecdb6636b6dd39c5eec220c91a328b5dd23e9d5` |
+| `/MMLA-Datasets/AnnoMi-therapist/train.tsv` | `fd0a4741bd3fb32014318f0bd0fbc464a87a9e267163fcac9618707fedca12b2` |
+| `/MMLA-Datasets/AnnoMi-therapist/AnnoMi-therapist_video.tar.gz` | `767ce57ad55078001cdd616d642f78d3b0433d9ebcbc14db1608408a54c9fa10` |
+| `/MMLA-Datasets/CH-SIMSv2.0/test.tsv` | `40afae5245b1060e8bb5162e8cc4f17f294a43b51a9e01e5bbd64d1f5ebcb6d7` |
+| `/MMLA-Datasets/CH-SIMSv2.0/dev.tsv` | `47dfac9ca8d77868ed644b8cd9536fa403f9d6f81e26796cd882e39d2cc14608` |
+| `/MMLA-Datasets/CH-SIMSv2.0/train.tsv` | `96350a9e35d62dc63035256e09f033f84aa670f6bf1c06e38daef85d39bde7d7` |
+| `/MMLA-Datasets/CH-SIMSv2.0/Ch-simsv2_video.tar.gz` | `e2817c4841a74f9e73eed6cf3196442ff0245f999bdfc5f975dcf18e66348f1e` |
+| `/MMLA-Datasets/IEMOCAP-DA/dev.tsv` | `67d357fee50c9b009f9cdc81738e1f45562e0a7f193f6f100320e1881d2b2c8c` |
+| `/MMLA-Datasets/IEMOCAP-DA/test.tsv` | `050d27887bec3714f8f0c323594c3c287fa9a5c006f94de0fa09565ba0251773` |
+| `/MMLA-Datasets/IEMOCAP-DA/train.tsv` | `823b37fa045aa6aad694d94ad134e23b92491cd6c5d742ed6e9d9456b433608b` |
+| `/MMLA-Datasets/IEMOCAP/dev.tsv` | `b6b0bbe1f49dc1f20c4121ac8f943b2d85722c95bb0988946282a496c0c1094d` |
+| `/MMLA-Datasets/IEMOCAP/test.tsv` | `7ab10d9c126e037e8c6be1ddf6487d57e9132b2e238286a6a9cccce029760581` |
+| `/MMLA-Datasets/IEMOCAP/train.tsv` | `a0017547086721147ed1191e8b7d5da42f795c4070687cffcff001d8827b81d8` |
+| `/MMLA-Datasets/MELD-DA/test.tsv` | `b25f4396f30a8d591224ec8074cc4ebfd5727f22fa816ab46cdb455dc22ee854` |
+| `/MMLA-Datasets/MELD-DA/dev.tsv` | `4fcc28d139ac933df8e8a288f2d17e010d5e013c70722485a834a7b843536351` |
+| `/MMLA-Datasets/MELD-DA/train.tsv` | `045642a0abaa9d9d9ea5f7ade96a09dd856311c9a375dea1839616688240ec71` |
+| `/MMLA-Datasets/MELD-DA/MELD-DA_video.tar.gz` | `92154bb5d2cf9d8dc229d5fe7ce65519ee7525487f4f42ca7acdf79e48c69707` |
+| `/MMLA-Datasets/MELD/dev.tsv` | `ce677f8162ce901e0cc26f531f1786620cac40b7507fa34664537dadc407d256` |
+| `/MMLA-Datasets/MELD/test.tsv` | `ee0e0a35a8ae73b522f359039cea34e92d0e13283f5f01c4f29795b439a92a69` |
+| `/MMLA-Datasets/MELD/train.tsv` | `063ac8accce2e0da3b45e9cdb077c5374a4cf08f6d62db41438e6e0c52981287` |
+| `/MMLA-Datasets/MELD/MELD_video.tar.gz` | `6ce66e5e0d3054aeaf2f5857106360f3b94c37e099bf2e2b17bc1304ef79361b` |
+| `/MMLA-Datasets/MIntRec/dev.tsv` | `629ab568ec3e1343c83d76b43d7398f7580361370d09162065a6bb1883f2fe9a` |
+| `/MMLA-Datasets/MIntRec/test.tsv` | `adffdc8f061878ad560ee0e0046ba32e6bc9e0332d9e09094cfce0b755fcc2a9` |
+| `/MMLA-Datasets/MIntRec/train.tsv` | `c1bec2ff06712063c7399264d7c06f4cdc125084314e6fa8bdfd94d3f0b42332` |
+| `/MMLA-Datasets/MIntRec/MIntRec_video.tar.gz` | `a756b6ad5f851773b3ae4621e3aa5c33a662bde80b239a6815a8541c30fc6411` |
+| `/MMLA-Datasets/MIntRec2.0/dev.tsv` | `f2f69111d0bd8c26681db0a613a0112f466c667d56a79949ce17ccadd1e6ae37` |
+| `/MMLA-Datasets/MIntRec2.0/test.tsv` | `6aa650afbaf40256afdbb546a9f7253511f3fe8d791a9acc7b6829824455a6ed` |
+| `/MMLA-Datasets/MIntRec2.0/train.tsv` | `e8b8767bd9a4de5833475db2438e63390c9674041a7b8ea39183a74fa4b624ef` |
+| `/MMLA-Datasets/MIntRec2.0/MIntRec2.0_video.tar.gz` | `78bd9ab4a0f9e5768ed2a094524165ecc51926e210a4701a9548d036a68d5e29` |
+| `/MMLA-Datasets/MOSI/dev.tsv` | `bd8ccded8dacb9cb7d37743f54c7e4c7bef391069b67b55c7e0cf4626fadee5f` |
+| `/MMLA-Datasets/MOSI/test.tsv` | `c480fc2cb444d215e5ba3433452db546fd8e638d332ee0f03278158b69375eca` |
+| `/MMLA-Datasets/MOSI/train.tsv` | `f1afe6018ae0b0ab8833da6934c0847f480412ed11c9c22e204a01e8cf75971b` |
+| `/MMLA-Datasets/MUStARD/MUStARD_video.tar.gz` | `8bd863c7ab4c29a710aa3edc0f560361275830a1e98ec41908d51c43e08647c1` |
+| `/MMLA-Datasets/MUStARD/dev.tsv` | `45477e0bda84c3d45ff197734b3943fc30e9f89c0d0cb8c272f0c10d31ee5474` |
+| `/MMLA-Datasets/MUStARD/test.tsv` | `ae248884d42d690700b6ce9930bb12827cd0fbcae200c43aace5a90003ad99e5` |
+| `/MMLA-Datasets/MUStARD/train.tsv` | `4292e07a087978a08552268b6c8405d897ee855af495e7e58ee99863e705eb43` |
+| `/MMLA-Datasets/UR-FUNNY-v2/dev.tsv` | `a82f758ef5d2a65bc41e09e24a616d4654c1565e851cd42c71a575b09282a2d2` |
+| `/MMLA-Datasets/UR-FUNNY-v2/test.tsv` | `6cb9dee9fd55545f46cd079ecb7541981d4c19a76c0ce79d7d874fe73703b63a` |
+| `/MMLA-Datasets/UR-FUNNY-v2/train.tsv` | `8eb91657faa19a2d53cc930c810d2fa3abd8e365c49d27fa6feb68cd95f40fb4` |
+| `/MMLA-Datasets/UR-FUNNY-v2/UR-FUNNYv2_video.tar.gz` | `e5a3962985c8ead5f593db69ab77a9d6702895768bb5871fe8764406358f8cae` |
+
+
+
+
+## 5. Acknowledgements 
 
 For more details, please refer to our [Github repo](https://github.com/thuiar/MMLA). If our work is helpful to your research, please consider citing the following paper: