Datasets:
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type string to null
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2014, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id string | forum string | venue_label string | year int64 | api string | invitation string | title string | abstract string | keywords null | authorids list | venue string | venueid string | pdf string | tcdate int64 | reviews list | comments list | decisions list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
zpo9TpUXuU | zpo9TpUXuU | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | PhysReaction: Physically Plausible Real-Time Humanoid Reaction Synthesis via Forward Dynamics Guided 4D Imitation | Humanoid Reaction Synthesis is pivotal for creating highly interactive and empathetic robots that can seamlessly integrate into human environments, enhancing the way we live, work, and communicate. However, it is difficult to learn the diverse interaction patterns of multiple humans and generate physically plausible re... | null | [
"~Yunze_Liu2",
"~Changxi_Chen1",
"~Chenjing_Ding1",
"~Li_Yi2"
] | MM2024 Oral | acmmm.org/ACMMM/2024/Conference | /pdf/49c77cc7b9a37979289a59ddf8420c9685717749.pdf | 1,711,791,067,608 | [
{
"id": "8OeFZ49g2o",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission1338/-/Official_Review",
"replyto": "zpo9TpUXuU",
"tcdate": 1716577570921,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission1338/Reviewer_6s6n"
],
"content": {
"summary": "The paper intro... | [] | [
{
"id": "Bb4Hx0WwWA",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission1338/-/Meta_Review",
"replyto": "zpo9TpUXuU",
"tcdate": 1720475705472,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission1338/Area_Chair_7Wf5"
],
"content": {
"metareview": "This article h... |
zoMT1Czqv3 | zoMT1Czqv3 | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | Domain Generalization-Aware Uncertainty Introspective Learning for 3D Point Clouds Segmentation | Domain generalization 3D segmentation aims to learn the point clouds with unknown distributions. Feature augmentation has been proven to be effective for domain generalization. However, each point of the 3D segmentation scene contains uncertainty in the target domain, which affects model generalization. This paper prop... | null | [
"~Pei_He1",
"~Licheng_Jiao2",
"~Lingling_Li1",
"~Xu_Liu5",
"~Fang_Liu5",
"~Wenping_Ma3",
"~Shuyuan_Yang1",
"~Ronghua_Shang1"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/a6bbdc114aa0f100554a8617119a390f7cebb50d.pdf | 1,712,586,186,368 | [
{
"id": "MvoDAF0RU9",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission4799/-/Official_Review",
"replyto": "zoMT1Czqv3",
"tcdate": 1716618219756,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission4799/Reviewer_x2sV"
],
"content": {
"summary": "This paper addr... | [] | [
{
"id": "7BPh3lJpSF",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission4799/-/Meta_Review",
"replyto": "zoMT1Czqv3",
"tcdate": 1720843426846,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission4799/Area_Chair_ZzEZ"
],
"content": {
"metareview": "This work prop... |
zctvc3QQr2 | zctvc3QQr2 | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | Color4E: Event Demosaicing for Full-color Event Guided Image Deblurring | Neuromorphic event sensors are novel visual cameras that feature high-speed illumination-variation sensing and have found widespread application in guiding frame-based imaging enhancement. This paper focuses on color restoration in the event-guided image deblurring task, we fuse blurry images with mosaic color events i... | null | [
"~Yi_Ma8",
"~Peiqi_Duan1",
"~Yuchen_Hong1",
"~Chu_Zhou1",
"~Yu_Zhang28",
"~Jimmy_Ren2",
"~Boxin_Shi3"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/219df6aa3f8696c7637b917026676589530cddf9.pdf | 1,712,313,759,287 | [
{
"id": "kvR2nYW33I",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2324/-/Official_Review",
"replyto": "zctvc3QQr2",
"tcdate": 1716600881994,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2324/Reviewer_FRT9"
],
"content": {
"summary": "In this paper, ... | [] | [
{
"id": "UfvLodMNVx",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2324/-/Meta_Review",
"replyto": "zctvc3QQr2",
"tcdate": 1719584238746,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2324/Area_Chair_NqnK"
],
"content": {
"metareview": "The paper prop... |
zXpQ50fcOb | zXpQ50fcOb | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | Attribute-Driven Multimodal Hierarchical Prompts for Image Aesthetic Quality Assessment | Image Aesthetic Quality Assessment (IAQA) aims to simulate users' visual perception to judge the aesthetic quality of images. In social media, users' aesthetic experiences are often reflected in their textual comments regarding the aesthetic attributes of images. To fully explore the attribute information perceived by ... | null | [
"~Hancheng_Zhu1",
"~Ju_Shi1",
"~Zhiwen_Shao1",
"~Rui_Yao1",
"~Yong_Zhou3",
"~Jiaqi_Zhao2",
"~Leida_Li3"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/e651bbcae4b2b168aa385230cb88ac310db854c3.pdf | 1,712,459,390,860 | [
{
"id": "OV7XaMBoLD",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2873/-/Official_Review",
"replyto": "zXpQ50fcOb",
"tcdate": 1716452830272,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2873/Reviewer_oXhd"
],
"content": {
"summary": "This paper prop... | [] | [
{
"id": "drF3zgSvgc",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2873/-/Meta_Review",
"replyto": "zXpQ50fcOb",
"tcdate": 1719588995823,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2873/Area_Chair_r8Xq"
],
"content": {
"metareview": "The reviewers ... |
zVgZfHRM3g | zVgZfHRM3g | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | Asymmetric Event-Guided Video Super-Resolution | Event cameras are novel bio-inspired cameras that record asynchronous events with high temporal resolution and dynamic range. Leveraging the auxiliary temporal information recorded by event cameras holds great promise for the task of video super-resolution (VSR). However, existing event-guided VSR methods assume that t... | null | [
"~Zeyu_Xiao1",
"~Dachun_Kai1",
"~Yueyi_Zhang2",
"~Xiaoyan_Sun1",
"~Zhiwei_Xiong1"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/0b0f7ea18396d9caacbb62126c298ed8b61903e7.pdf | 1,712,499,923,333 | [
{
"id": "nOEQtGd6jA",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission3531/-/Official_Review",
"replyto": "zVgZfHRM3g",
"tcdate": 1716631566047,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission3531/Reviewer_HjqY"
],
"content": {
"summary": "The paper intro... | [] | [
{
"id": "epVFlQRUGl",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission3531/-/Meta_Review",
"replyto": "zVgZfHRM3g",
"tcdate": 1719834321675,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission3531/Area_Chair_dzaj"
],
"content": {
"metareview": "This paper got... |
zTLZvVdgUt | zTLZvVdgUt | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | DP-RAE: A Dual-Phase Merging Reversible Adversarial Example for Image Privacy Protection | In digital security, Reversible Adversarial Examples (RAE) blend adversarial attacks with Reversible Data Hiding (RDH) within images to thwart unauthorized access. Traditional RAE methods, however, compromise attack efficiency for the sake of perturbation concealment, diminishing the protective capacity of valuable per... | null | [
"~Xia_Du2",
"~Jiajie_Zhu1",
"~Jizhe_Zhou1",
"~Chi-Man_Pun1",
"~Qizhen_Xu1",
"~Xiaoyuan_Liu5"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/eec222133eeed934f22476820284e2835c386191.pdf | 1,712,484,531,756 | [
{
"id": "pBizVDGoMY",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission3254/-/Official_Review",
"replyto": "zTLZvVdgUt",
"tcdate": 1716649926364,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission3254/Reviewer_Sg93"
],
"content": {
"summary": "This research p... | [] | [
{
"id": "ta7XwYaR5m",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission3254/-/Meta_Review",
"replyto": "zTLZvVdgUt",
"tcdate": 1720603229862,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission3254/Senior_Area_Chairs"
],
"content": {
"metareview": "All the rev... |
zQvFY3Mlrk | zQvFY3Mlrk | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | Model-Based Non-Independent Distortion Cost Design for Effective JPEG Steganography | Recent achievements have shown that model-based steganographic schemes hold promise for better security than heuristic-based ones, as they can provide theoretical guarantees on secure steganography under a given statistical model. However, it remains a challenge to exploit the correlations between DCT coefficients for ... | null | [
"~Yuanfeng_Pan1",
"~Wenkang_Su1",
"~Jiangqun_Ni1",
"~Qingliang_Liu3",
"~Yulin_Zhang5",
"~Donghua_Jiang1"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/a20d28b518a5d3c0c2b1cb503023472e9e1a60ea.pdf | 1,712,655,288,912 | [
{
"id": "6OgfV1KAfE",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission5560/-/Official_Review",
"replyto": "zQvFY3Mlrk",
"tcdate": 1716568325293,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission5560/Reviewer_MuSm"
],
"content": {
"summary": "This paper intr... | [] | [
{
"id": "Yc9IIRO3pn",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission5560/-/Meta_Review",
"replyto": "zQvFY3Mlrk",
"tcdate": 1720007209037,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission5560/Area_Chair_y4yz"
],
"content": {
"metareview": "As pointed out... |
zGoCaP7NyR | zGoCaP7NyR | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-Visual Class Incremental Tasks | Class-incremental learning poses a significant challenge under an exemplar-free constraint, leading to catastrophic forgetting and sub-par incremental accuracy. Previous attempts have focused primarily on single-modality tasks, such as image classification or audio event classification. However, in the context of Audio... | null | [
"~Xianghu_Yue1",
"~Xueyi_Zhang2",
"~Yiming_Chen5",
"~Chengwei_Zhang4",
"~Mingrui_Lao1",
"~Huiping_Zhuang2",
"~Xinyuan_Qian2",
"~Haizhou_Li3"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/4786565a0f9ee5b8a7063f1358051cb5261f24d8.pdf | 1,712,594,018,563 | [
{
"id": "dlCHzbR1L8",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission5001/-/Official_Review",
"replyto": "zGoCaP7NyR",
"tcdate": 1718115684698,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission5001/Reviewer_HWFc"
],
"content": {
"summary": "The authors stu... | [] | [
{
"id": "455UwqHCJk",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission5001/-/Meta_Review",
"replyto": "zGoCaP7NyR",
"tcdate": 1719831428882,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission5001/Area_Chair_jJow"
],
"content": {
"metareview": "All four revie... |
zFtyfdfNky | zFtyfdfNky | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | Generating Multimodal Metaphorical Features for Meme Understanding | Understanding a meme is a challenging task, due to the metaphorical information contained in the meme that requires intricate interpretation to grasp its intended meaning fully. In previous works, attempts have been made to facilitate computational understanding of memes through introducing human-annotated metaphors as... | null | [
"~Bo_Xu17",
"~Junzhe_Zheng1",
"~Jiayuan_He1",
"~Yuxuan_Sun6",
"~Hongfei_Lin3",
"~Liang_Zhao12",
"~Feng_Xia1"
] | MM2024 Oral | acmmm.org/ACMMM/2024/Conference | /pdf/a388bbd5e3251f51a9ba99b62342a07c3446dafc.pdf | 1,712,321,501,444 | [
{
"id": "yTa65GWqyt",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2352/-/Official_Review",
"replyto": "zFtyfdfNky",
"tcdate": 1716639465570,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2352/Reviewer_SEUH"
],
"content": {
"summary": "This paper intr... | [] | [
{
"id": "HniG8P5ym4",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2352/-/Meta_Review",
"replyto": "zFtyfdfNky",
"tcdate": 1719661979882,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2352/Area_Chair_cfHN"
],
"content": {
"metareview": "The manuscript... |
zD9m7YE8Gj | zD9m7YE8Gj | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | Sampling to Distill: Knowledge Transfer from Open-World Data | Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance student models using only the pre-trained teacher network without original training data. Most of the existing DFKD methods rely heavily on additional generation modules to synthesize the substitution data resulting in high comp... | null | [
"~Yuzheng_Wang1",
"~Zhaoyu_Chen1",
"~Jie_Zhang14",
"~Dingkang_Yang1",
"~Zuhao_Ge1",
"~Yang_Liu86",
"~Siao_Liu1",
"~Yunquan_Sun2",
"~Wenqiang_Zhang1",
"~Lizhe_Qi1"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/645eaba9ad1a74f5e3cd955c989d7fc30b99b82e.pdf | 1,711,699,557,841 | [
{
"id": "xj3Ga890nF",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission1260/-/Official_Review",
"replyto": "zD9m7YE8Gj",
"tcdate": 1717818710457,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission1260/Reviewer_aeKW"
],
"content": {
"summary": "The paper \"Sam... | [] | [
{
"id": "BRfZ4QhqdE",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission1260/-/Meta_Review",
"replyto": "zD9m7YE8Gj",
"tcdate": 1719811551758,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission1260/Area_Chair_3QQg"
],
"content": {
"metareview": "Thanks for aut... |
z9nEV02Ujx | z9nEV02Ujx | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | Geometry-Guided Diffusion Model with Masked Transformer for Robust Multi-View 3D Human Pose Estimation | Recent research on Diffusion Models and Transformers has brought significant advancements to 3D Human Pose Estimation (HPE). Nonetheless, existing methods often fail to concurrently address the issues of accuracy and generalization. In this paper, we propose a **G**eometry-guided D**if**fusion Model with Masked Trans**... | null | [
"~Xinyi_Zhang10",
"~Qinpeng_Cui1",
"~Qiqi_Bao1",
"~Wenming_Yang1",
"~Qingmin_Liao1"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/eb06426b13c9b108a3d7611662078224edb61b67.pdf | 1,712,479,241,727 | [
{
"id": "kFI2rJAKsM",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission3159/-/Official_Review",
"replyto": "z9nEV02Ujx",
"tcdate": 1716703479677,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission3159/Reviewer_ZkuQ"
],
"content": {
"summary": "The authors aim... | [] | [
{
"id": "GSfMfslhsr",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission3159/-/Meta_Review",
"replyto": "z9nEV02Ujx",
"tcdate": 1720189415344,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission3159/Area_Chair_uaRB"
],
"content": {
"metareview": "After consider... |
z8IvMe8gZI | z8IvMe8gZI | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | AdaFPP: Adapt-Focused Bi-Propagating Prototype Learning for Panoramic Activity Recognition | Panoramic Activity Recognition (PAR) aims to identify multi-granul-arity behaviors performed by multiple persons in panoramic scenes, including individual activities, group activities, and global activities. Previous methods 1) heavily rely on manually annotated detection boxes in training and inference, hindering furt... | null | [
"~Meiqi_Cao1",
"~Rui_Yan5",
"~Xiangbo_Shu1",
"~Guangzhao_Dai1",
"~Yazhou_Yao2",
"~Guosen_Xie1"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/28b0ed736b7c729de2a08acf2facebb12bd6a9c2.pdf | 1,712,069,543,032 | [
{
"id": "st9EUk5L9G",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission1785/-/Official_Review",
"replyto": "z8IvMe8gZI",
"tcdate": 1717430215625,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission1785/Reviewer_ARdi"
],
"content": {
"summary": "This paper pres... | [] | [
{
"id": "odBGdyyIUe",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission1785/-/Meta_Review",
"replyto": "z8IvMe8gZI",
"tcdate": 1719882710499,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission1785/Area_Chair_48ja"
],
"content": {
"metareview": "1. This paper ... |
z2aWdGeoSL | z2aWdGeoSL | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | Partially Aligned Cross-modal Retrieval via Optimal Transport-based Prototype Alignment Learning | Supervised cross-modal retrieval (CMR) achieves excellent performance thanks to the semantic information provided by its labels, which helps to establish semantic correlations between samples from different modalities. However, in real-world scenarios, there often exists a large amount of unlabeled and unpaired multim... | null | [
"~Junsheng_Wang1",
"~Tiantian_Gong1",
"~Yan_Yan6"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/2471ad6e35e5ad1000b202fae5fbffc99a4887cf.pdf | 1,712,587,696,600 | [
{
"id": "xCtY5r6aBY",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission4857/-/Official_Review",
"replyto": "z2aWdGeoSL",
"tcdate": 1716602123020,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission4857/Reviewer_E2nH"
],
"content": {
"summary": "This study deve... | [] | [
{
"id": "hScVcocL9j",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission4857/-/Meta_Review",
"replyto": "z2aWdGeoSL",
"tcdate": 1719671105878,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission4857/Area_Chair_J5Ut"
],
"content": {
"metareview": "The paper intr... |
z1vuuz86iQ | z1vuuz86iQ | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | TALE: Training-free Cross-domain Image Composition via Adaptive Latent Manipulation and Energy-guided Optimization | We present TALE, a novel training-free framework harnessing the power of text-driven diffusion models to tackle cross-domain image composition task that aims at seamlessly incorporating user-provided objects into a specific visual context regardless of domain disparity. Previous methods often involve either training au... | null | [
"~Kien_T._Pham1",
"~Jingye_Chen2",
"~Qifeng_Chen1"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/5b0b9cc42b610395a6ddd453d2113a44698f3d68.pdf | 1,712,339,307,927 | [
{
"id": "BLmyGjsLIJ",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2425/-/Official_Review",
"replyto": "z1vuuz86iQ",
"tcdate": 1716945107367,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2425/Reviewer_wRkm"
],
"content": {
"summary": "This work intro... | [] | [
{
"id": "YVWnMPKPRl",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2425/-/Meta_Review",
"replyto": "z1vuuz86iQ",
"tcdate": 1720552186619,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2425/Senior_Area_Chairs"
],
"content": {
"metareview": "In this pap... |
z0OEHZbT71 | z0OEHZbT71 | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | SegTalker: Segmentation-based Talking Face Generation with Mask-guided Local Editing | Audio-driven talking face generation aims to synthesize video with lip movements synchronized to input audio. However, current generative techniques face challenges in preserving intricate regional textures (skin, teeth). To address the aforementioned challenges, we propose a novel framework called \textbf{SegTalker} t... | null | [
"~Lingyu_Xiong1",
"~Xize_Cheng1",
"~Jintao_Tan1",
"~Xianjia_Wu1",
"~Xiandong_Li1",
"~Lei_Zhu18",
"~Fei_Ma3",
"~Minglei_Li1",
"~Huang_Xu2",
"~Zhihui_Hu3"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/deb664d1d36e0441c0bb3d7cd1d63948d1e0b256.pdf | 1,712,394,183,766 | [
{
"id": "U2ObjZp7lK",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2594/-/Official_Review",
"replyto": "z0OEHZbT71",
"tcdate": 1716767106064,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2594/Reviewer_zA7n"
],
"content": {
"summary": "The paper aims ... | [] | [
{
"id": "BXwU7H9Bmm",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2594/-/Meta_Review",
"replyto": "z0OEHZbT71",
"tcdate": 1719743022648,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2594/Area_Chair_hMD9"
],
"content": {
"metareview": "The paper join... |
yzPsE6aWAk | yzPsE6aWAk | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | InstantAS: Minimum Coverage Sampling for Arbitrary-Size Image Generation | In recent years, diffusion models have dominated the field of image generation with their outstanding generation quality. However, pre-trained large-scale diffusion models are generally trained using fixed-size images, and fail to maintain their performance at different aspect ratios. Existing methods for generating ar... | null | [
"~Changshuo_Wang3",
"~Mingzhe_Yu1",
"~Lei_Wu8",
"~Lei_Meng1",
"~Xiang_Li86",
"~Xiangxu_Meng2"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/c46a147985341aa2467f729c7ba5474949f59c8b.pdf | 1,712,461,630,740 | [
{
"id": "p2K6TiP4Iv",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2925/-/Official_Review",
"replyto": "yzPsE6aWAk",
"tcdate": 1716552671930,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2925/Reviewer_QMcu"
],
"content": {
"summary": "This paper prop... | [] | [
{
"id": "21glR0JQt3",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission2925/-/Meta_Review",
"replyto": "yzPsE6aWAk",
"tcdate": 1719941569833,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission2925/Area_Chair_ehZQ"
],
"content": {
"metareview": "All the review... |
yuCgYQR0A8 | yuCgYQR0A8 | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | SSL: A Self-similarity Loss for Improving Generative Image Super-resolution | Generative adversarial networks (GAN) and generative diffusion models (DM) have been widely used in real-world image super-resolution (Real-ISR) to enhance the image perceptual quality. However, these generative models are prone to generating visual artifacts and false image structures, resulting in unnatural Real-ISR ... | null | [
"~Du_Chen1",
"~Zhengqiang_ZHANG1",
"~Jie_Liang4",
"~Lei_Zhang2"
] | MM2024 Poster | acmmm.org/ACMMM/2024/Conference | /pdf/ca3b3a268082c2a86f3ac8ce0b78feb29a3c6dfb.pdf | 1,710,929,851,078 | [
{
"id": "Lu7Bt15c2U",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission701/-/Official_Review",
"replyto": "yuCgYQR0A8",
"tcdate": 1716579011939,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission701/Reviewer_eWxV"
],
"content": {
"summary": "The authors propo... | [] | [
{
"id": "lKJEmhPhmL",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission701/-/Meta_Review",
"replyto": "yuCgYQR0A8",
"tcdate": 1719739226224,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission701/Area_Chair_Qrdh"
],
"content": {
"metareview": "The final rating... |
ys3V4jiENk | ys3V4jiENk | ACMMM | 2,024 | v2 | acmmm.org/ACMMM/2024/Conference/-/Submission | Hawkeye: Discovering and Grounding Implicit Anomalous Sentiment in Recon-videos via Scene-enhanced Video Large Language Model | In real-world recon-videos such as surveillance and drone reconnaissance videos, commonly used explicit language, acoustic and facial expressions information is often missing. However, these videos are always rich in anomalous sentiments (e.g., criminal tendencies), which urgently requires the implicit scene informatio... | null | [
"~Jianing_Zhao3",
"~Jingjing_Wang4",
"~Yujie_Jin3",
"~Jiamin_Luo1",
"~Guodong_Zhou1"
] | MM2024 Oral | acmmm.org/ACMMM/2024/Conference | /pdf/d1de68947f8b92ee6dd8fde7bc2bb2eae13f74b2.pdf | 1,712,542,803,901 | [
{
"id": "0SdMXc0gbw",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission3804/-/Official_Review",
"replyto": "ys3V4jiENk",
"tcdate": 1716707096229,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission3804/Reviewer_PJXi"
],
"content": {
"summary": "- The paper pro... | [] | [
{
"id": "4hay2jj6A9",
"invitation": "acmmm.org/ACMMM/2024/Conference/Submission3804/-/Meta_Review",
"replyto": "ys3V4jiENk",
"tcdate": 1719359754368,
"signatures": [
"acmmm.org/ACMMM/2024/Conference/Submission3804/Area_Chair_9sJ1"
],
"content": {
"metareview": "The reviewers ... |
End of preview.
auto-research-bench-data
同行评审语料 + 论文 PDF，覆盖五个机器学习会议 2022–2026 年。 用于研究「AI 生成的论文与人类论文有何差异」，特别是图表质量与实验管理两个维度。
数据来自 OpenReview，用官方 API 抓取。
内容
1. 评审元数据（18 个 jsonl，3.11 GB）
每行一篇投稿，字段如下：
| 字段 | 说明 |
|---|---|
forum |
OpenReview 论文 ID，与 PDF 文件名一致，是关联两部分数据的 key |
title / abstract / keywords |
论文元信息 |
venue / venueid |
录用层级。注意层级只在 venue 里（如 ICLR 2024 oral），venueid 对所有录用论文都是 .../Conference |
reviews |
全部 Official_Review，含评分、置信度、正文各字段 |
comments |
作者 rebuttal 与其他 Official_Comment |
decisions |
Decision / Meta_Review |
pdf |
OpenReview 上的相对路径 |
共 73,566 篇投稿、267,117 条评审、711,880 条评论。
2. 论文 PDF（13 个 tar，44.9 GB，8,416 篇）
按 pdf/{会议}_{年份}.tar 组织，解包后是 {会议}_{年份}/{forum}.pdf。
只包含用于本研究的子集：全部 oral + spotlight 论文，加上按 1:2 抽样的被拒论文。 不是全量投稿。
怎么用
from datasets import load_dataset
# 只取需要的年份，不必下载全部
ds = load_dataset("chengwanru/auto-research-bench-data", data_files="ICLR_2024.jsonl")
# 关联 PDF 与评审
import tarfile, json
from huggingface_hub import hf_hub_download
p = hf_hub_download("chengwanru/auto-research-bench-data",
"pdf/ICLR_2024.tar", repo_type="dataset")
with tarfile.open(p) as tf:
pdf = tf.extractfile("ICLR_2024/<forum_id>.pdf").read()
各会议实际覆盖内容差异很大
会议的公开政策不同，不是抓取遗漏：
| 语料 | 评审 | 评分 | rebuttal | decision | |
|---|---|---|---|---|---|
| ICLR 2022–2026 | ✅ | ✅ | ✅ | 88% | ✅ |
| NeurIPS 2023–2025 | ✅ | ✅ | ✅ | 99% | ✅ |
| ICML 2025 | ✅ | ✅ | ❌ | ✅ | ✅ |
| ICML 2023–2024 | ✅ | ❌ | ❌ | ❌ | ❌ |
| ACM MM 2024 | ✅ | ✅ | ✅ | ❌ | ✅ |
| CoRL 2025 | ✅ | ❌ | ❌ | ❌ | ✅ |
被拒论文实际只有 ICLR 有规模。 NeurIPS 三年合计只有 631 篇公开被拒， ICML 2023/2024 一篇都没有。这限制了任何需要负样本的分析。
评分字段名随年份变化：2022–2023 用 recommendation，2024 起用 rating；
2024 及以前取值是 "5: marginally below..." 这样的字符串，2025 起是纯数字。
已知缺口
选中 8,865 篇，实际上传 8,416 篇，**完整率 94.9%**，缺 449 篇。
缺口来自 OpenReview 下载失败，原因是并发拉取大文件超时（ACM MM 单篇可达 50 MB） 以及持续高频请求触发限流。降到单线程重跑能基本消除：ACM MM 从 38% 失败降到 0， ICLR 2024 从 17% 降到 0.5%。已补跑的四批完整率 94.8%–100%，其余批次 89%–95%。
各批次完整率：
| 语料 | 选中 | 已传 | 完整率 |
|---|---|---|---|
| ACMMM_2024 | 174 | 174 | 100% |
| CoRL_2025 | 42 | 42 | 100% |
| ICLR_2024 | 1359 | 1352 | 99.5% |
| ICLR_2022 | 690 | 679 | 98.4% |
| NeurIPS_2024 | 588 | 576 | 98.0% |
| ICLR_2023 | 1113 | 1070 | 96.1% |
| NeurIPS_2025 | 1018 | 968 | 95.1% |
| ICML_2023 | 155 | 147 | 94.8% |
| NeurIPS_2023 | 621 | 580 | 93.4% |
| ICLR_2026 | 672 | 621 | 92.4% |
| ICML_2025 | 319 | 292 | 91.5% |
| ICLR_2025 | 1779 | 1617 | 90.9% |
| ICML_2024 | 335 | 298 | 89.0% |
缺失的论文可以用 jsonl 里的 pdf 字段自行从 OpenReview 补取（需账号）。
NeurIPS 2022 没有 oral/spotlight 层级，未纳入 PDF 子集。
许可与使用
评审内容来自 OpenReview 公开页面。论文 PDF 版权归各自作者所有， ICLR 投稿多数采用 CC BY 4.0，其他会议以各自政策为准。
本数据集仅供学术研究使用。 使用论文内容时请引用原论文。 如果你是某篇论文的作者并希望移除，请通过 HuggingFace 联系。
生成方式
抓取与处理脚本见 github.com/chengwanru/auto-research-bench
（scripts/fetch_openreview.py、scripts/upload_pdfs_to_hf.py）。
- Downloads last month
- 340