---
title: "Computer Vision and Pattern Recognition — papers, page 4"
description: "Computer Vision and Pattern Recognition: 1,029 papers of the Elamur Research library · page 4 of 12 · 226–325, the most cited first — our summary of each…"
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---
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# 👁️ Computer Vision and Pattern Recognition

1,029 papers · page 4 of 12 · 226–325 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

Computer Vision and Pattern Recognition is a subfield of Computer Science in the OpenAlex classification of science. The topics most of its papers here are on: «Multimodal Machine Learning Applications», «Advanced Neural Network Applications», «Handwritten Text Recognition Techniques».

## Papers

[← prev](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/page/3/) [1](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) [2](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/page/2/) [3](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/page/3/) **4** [5](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/page/5/) … [12](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/page/12/) [next →](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/page/5/)

226. [**TVQA: Localized, Compositional Video Question Answering**Jie Lei, Licheng Yu, Mohit Bansal, et al.*2018 · 669 citations*](https://md.research.elamur.ai/free/en/papers/lei-2018-tvqa-localized-compositional-video-question/)
227. [**KILT: a Benchmark for Knowledge Intensive Language Tasks**Fabio Petroni, Aleksandra Piktus, Angela Fan, et al.*2020 · 663 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/)
228. [**Efficient Content-Based Sparse Attention with Routing Transformers**Aurko Roy, Mohammad Saffar, Ashish Vaswani, et al.*2020 · 660 citations*](https://md.research.elamur.ai/free/en/papers/roy-2020-efficient-content-based-sparse-attention/)
229. [**Highway Networks**Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber*2015 · 652 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2015-highway-networks/)
230. [**Visualizing and Understanding Convolutional Networks**Matthew D Zeiler, Rob Fergus*2013 · 649 citations*](https://md.research.elamur.ai/free/en/papers/zeiler-2013-visualizing-understanding-convolutional-networks/)
231. [**CoCa: Contrastive Captioners are Image-Text Foundation Models**Jiahui Yu, Zirui Wang, Vijay Vasudevan, et al.*2022 · 646 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-coca-contrastive-captioners-image-text/)
232. [**Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate**Tian Liang, Zhiwei He, Wenxiang Jiao, et al.*2023 · 643 citations*](https://md.research.elamur.ai/free/en/papers/liang-2023-encouraging-divergent-thinking-large-language/)
233. [**A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)**Alane Suhr, Stephanie Zhou, Ally Zhang, et al.*2019 · 640 citations*](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)
234. [**Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models**Muhammad Maaz, Hanoona Rasheed, Salman Khan, et al.*2023 · 634 citations*](https://md.research.elamur.ai/free/en/papers/maaz-2023-video-chatgpt-towards-detailed-video/)
235. [**A Frustratingly Easy Approach for Entity and Relation Extraction**Zexuan Zhong, Danqi Chen*2020 · 629 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/)
236. [**Benchmarking Large Language Models in Retrieval-Augmented Generation**Jiawei Chen, Hongyu Lin, Xianpei Han, et al.*2023 · 624 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-benchmarking-large-language-models-retrieval/)
237. [**LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding**Yang Xu, Yiheng Xu, Tengchao Lv, et al.*2021 · 623 citations*](https://md.research.elamur.ai/free/en/papers/xu-2021-layoutlmv2-multi-modal-pre-training/)
238. [**Harnessing Deep Neural Networks with Logic Rules**Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, et al.*2016 · 619 citations*](https://md.research.elamur.ai/free/en/papers/hu-2016-harnessing-deep-neural-networks-logic/)
239. [**Scalable Zero-shot Entity Linking with Dense Entity Retrieval**Ledell Wu, Fabio Petroni, Martin Josifoski, et al.*2020 · 618 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-scalable-zero-shot-entity-linking/)
240. [**Object Hallucination in Image Captioning**Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, et al.*2018 · 617 citations*](https://md.research.elamur.ai/free/en/papers/rohrbach-2018-object-hallucination-image-captioning/)
241. [**Video-LLaVA: Learning United Visual Representation by Alignment Before Projection**Bin Lin, Yang Ye, Bin Zhu, et al.*2023 · 614 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-video-llava-learning-united-visual/)
242. [**Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism**Xiangrong Zeng, Daojian Zeng, Shizhu He, et al.*2018 · 607 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2018-extracting-relational-facts-end-end/)
243. [**Show and Tell: A Neural Image Caption Generator**Oriol Vinyals, Alexander Toshev, Samy Bengio, et al.*2014 · 606 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2014-show-tell-neural-image-caption/)
244. [**Event Extraction by Answering (Almost) Natural Questions**Xinya Du, Claire Cardie*2020 · 602 citations*](https://md.research.elamur.ai/free/en/papers/du-2020-event-extraction-answering-almost-natural/)
245. [**RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering**Yingqi Qu, Yuchen Ding, Jing Liu, et al.*2021 · 601 citations*](https://md.research.elamur.ai/free/en/papers/qu-2021-rocketqa-optimized-training-approach-dense/)
246. [**A Joint Neural Model for Information Extraction with Global Features**Ying Lin, Heng Ji, Fei Huang, et al.*2020 · 600 citations*](https://md.research.elamur.ai/free/en/papers/lin-2020-joint-neural-model-information-extraction/)
247. [**Text Embeddings by Weakly-Supervised Contrastive Pre-training**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2022 · 594 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/)
248. [**Named Entity Recognition as Dependency Parsing**Juntao Yu, Bernd Bohnet, Massimo Poesio*2020 · 594 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-named-entity-recognition-as-dependency/)
249. [**FNet: Mixing Tokens with Fourier Transforms**James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, et al.*2022 · 593 citations*](https://md.research.elamur.ai/free/en/papers/thorp-2022-fnet-mixing-tokens-fourier-transforms/)
250. [**How Powerful are Graph Neural Networks?**Keyulu Xu, Weihua Hu, Jure Leskovec, et al.*2018 · 591 citations*](https://md.research.elamur.ai/free/en/papers/xu-2018-powerful-graph-neural-networks/)
251. [**A simple neural network module for relational reasoning**Adam Santoro, David Raposo, David G. T. Barrett, et al.*2017 · 590 citations*](https://md.research.elamur.ai/free/en/papers/santoro-2017-simple-neural-network-module-relational/)
252. [**Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks**Yossi Adi, Einat Kermany, Yonatan Belinkov, et al.*2016 · 587 citations*](https://md.research.elamur.ai/free/en/papers/adi-2016-fine-grained-analysis-sentence-embeddings/)
253. [**Instruction-Following Evaluation for Large Language Models**Jeffrey Zhou, Tianjian Lu, Swaroop Mishra, et al.*2023 · 586 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-instruction-following-evaluation-large-language/)
254. [**Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models**Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, et al.*2021 · 582 citations*](https://md.research.elamur.ai/free/en/papers/ni-2021-sentence-t5-scalable-sentence-encoders/)
255. [**QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension**Adams Wei Yu, David Dohan, Minh-Thang Luong, et al.*2018 · 581 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-qanet-combining-local-convolution-global/)
256. [**OCR-free Document Understanding Transformer**Geewook Kim, Teakgyu Hong, Moonbin Yim, et al.*2021 · 580 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-ocr-free-document-understanding-transformer/)
257. [**Dynamic Coattention Networks For Question Answering**Caiming Xiong, Victor Zhong, Richard Socher*2016 · 578 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2016-dynamic-coattention-networks-question-answering/)
258. [**Relation Classification via Multi-Level Attention CNNs**Linlin Wang, Zhu Cao, Gerard de Melo, et al.*2016 · 573 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-relation-classification-multi-level-attention/)
259. [**SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis**Dustin Podell, Zion English, Kyle Lacey, et al.*2023 · 571 citations*](https://md.research.elamur.ai/free/en/papers/podell-2023-sdxl-improving-latent-diffusion-models/)
260. [**A Survey on Recent Advances in Named Entity Recognition from Deep Learning models**Vikas Yadav, Steven Bethard*2018 · 565 citations*](https://md.research.elamur.ai/free/en/papers/yadav-2018-survey-recent-advances-named-entity/)
261. [**Improving Factuality and Reasoning in Language Models through Multiagent Debate**Yilun Du, Shuang Li, Antonio Torralba, et al.*2023 · 564 citations*](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/)
262. [**Plug and Play Language Models: A Simple Approach to Controlled Text Generation**Sumanth Dathathri, Andrea Madotto, Janice Lan, et al.*2019 · 562 citations*](https://md.research.elamur.ai/free/en/papers/dathathri-2019-plug-play-language-models-simple/)
263. [**Collecting Image Annotations Using Amazon's Mechanical Turk**Cyrus Rashtchian, Peter Young, Micah Hodosh, et al.*2010 · 562 citations*](https://md.research.elamur.ai/free/en/papers/rashtchian-2010-collecting-image-annotations-amazon-s/)
264. [**Galactica: A Large Language Model for Science**Ross Taylor, Marcin Kardas, Guillem Cucurull, et al.*2022 · 558 citations*](https://md.research.elamur.ai/free/en/papers/taylor-2022-galactica-large-language-model-science/)
265. [**Simple and Effective Multi-Paragraph Reading Comprehension**Christopher Clark, Matt Gardner*2017 · 556 citations*](https://md.research.elamur.ai/free/en/papers/clark-2017-simple-effective-multi-paragraph-reading/)
266. [**gpt-oss-120b & gpt-oss-20b Model Card**OpenAI: Sandhini Agarwal, Lama Ahmad, Jason Ai, et al.*2025 · 555 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2025-gpt-oss-120b-gpt-oss/)
267. [**Style Transfer Through Back-Translation**Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, et al.*2018 · 554 citations*](https://md.research.elamur.ai/free/en/papers/prabhumoye-2018-style-transfer-through-back-translation/)
268. [**Learning to Compose Neural Networks for Question Answering**Jacob Andreas, Marcus Rohrbach, Trevor Darrell, et al.*2016 · 547 citations*](https://md.research.elamur.ai/free/en/papers/andreas-2016-learning-compose-neural-networks-question/)
269. [**Reducing Transformer Depth on Demand with Structured Dropout**Angela Fan, Edouard Grave, Armand Joulin*2019 · 546 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/)
270. [**Precise Zero-Shot Dense Retrieval without Relevance Labels**Luyu Gao, Xueguang Ma, Jimmy Lin, et al.*2022 · 545 citations*](https://md.research.elamur.ai/free/en/papers/gao-2022-precise-zero-shot-dense-retrieval/)
271. [**Fast and Accurate Entity Recognition with Iterated Dilated Convolutions**Emma Strubell, Patrick Verga, David Belanger, et al.*2017 · 544 citations*](https://md.research.elamur.ai/free/en/papers/strubell-2017-fast-accurate-entity-recognition-iterated/)
272. [**What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams**Di Jin, Eileen Pan, Nassim Oufattole, et al.*2020 · 540 citations*](https://md.research.elamur.ai/free/en/papers/jin-2020-disease-this-patient-have-large/)
273. [**Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning**Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, et al.*2019 · 536 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/)
274. [**How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites**Zhe Chen, Weiyun Wang, Hao Tian, et al.*2024 · 529 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-far-gpt-4v-closing/)
275. [**MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models**Chaoyou Fu, Peixian Chen, Yunhang Shen, et al.*2023 · 529 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-mme-comprehensive-evaluation-benchmark-multimodal/)
276. [**Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks**Jason Weston, Antoine Bordes, Sumit Chopra, et al.*2015 · 529 citations*](https://md.research.elamur.ai/free/en/papers/weston-2015-towards-ai-complete-question-answering/)
277. [**LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs**Christoph Schuhmann, Richard Vencu, Romain Beaumont, et al.*2021 · 528 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2021-laion-400m-open-dataset-clip/)
278. [**Machine Comprehension Using Match-LSTM and Answer Pointer**Shuohang Wang, Jing Jiang*2016 · 528 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-machine-comprehension-using-match-lstm/)
279. [**TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking**Yucheng Wang, Bowen Yu, Yueyang Zhang, et al.*2020 · 527 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-tplinker-single-stage-joint-extraction/)
280. [**Large Language Models Can Be Easily Distracted by Irrelevant Context**Freda Shi, Xinyun Chen, Kanishka Misra, et al.*2023 · 525 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/)
281. [**ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection**Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, et al.*2018 · 523 citations*](https://md.research.elamur.ai/free/en/papers/hazarika-2018-icon-interactive-conversational-memory-network/)
282. [**The Natural Language Decathlon: Multitask Learning as Question Answering**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 523 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
283. [**Instruction Tuning for Large Language Models: A Survey**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 515 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/)
284. [**Toward Controlled Generation of Text**Zhiting Hu, Zichao Yang, Xiaodan Liang, et al.*2017 · 513 citations*](https://md.research.elamur.ai/free/en/papers/hu-2017-toward-controlled-generation-text/)
285. [**Baichuan 2: Open Large-scale Language Models**Aiyuan Yang, Bin Xiao, Bingning Wang, et al.*2023 · 512 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-baichuan-2-open-large-scale/)
286. [**FinQA: A Dataset of Numerical Reasoning over Financial Data**Zhiyu Chen, Wenhu Chen, Charese Smiley, et al.*2021 · 510 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-finqa-dataset-numerical-reasoning-over/)
287. [**SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking**Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant*2021 · 510 citations*](https://md.research.elamur.ai/free/en/papers/formal-2021-splade-sparse-lexical-expansion/)
288. [**TabFact: A Large-scale Dataset for Table-based Fact Verification**Wenhu Chen, Hongmin Wang, Jianshu Chen, et al.*2019 · 508 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/)
289. [**LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2023 · 505 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-longbench-bilingual-multitask-benchmark/)
290. [**Chinese Word Segmentation as Character Tagging**Nianwen Xue*2003 · 504 citations*](https://md.research.elamur.ai/free/en/papers/xue-2003-chinese-word-segmentation-as-character/)
291. [**FLAT: Chinese NER Using Flat-Lattice Transformer**Xiaonan Li, Hang Yan, Xipeng Qiu, et al.*2020 · 503 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-flat-chinese-ner-flat-lattice/)
292. [**Sparse, Dense, and Attentional Representations for Text Retrieval**Yi Luan, Jacob Eisenstein, Kristina Toutanova, et al.*2021 · 500 citations*](https://md.research.elamur.ai/free/en/papers/luan-2021-sparse-dense-attentional-representations-text/)
293. [**Deep Learning Scaling is Predictable, Empirically**Joel Hestness, Sharan Narang, Newsha Ardalani, et al.*2017 · 497 citations*](https://md.research.elamur.ai/free/en/papers/hestness-2017-deep-learning-scaling-predictable-empirically/)
294. [**Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction**Hu Xu, Bing Liu, Lei Shu, et al.*2018 · 494 citations*](https://md.research.elamur.ai/free/en/papers/xu-2018-double-embeddings-cnn-based-sequence/)
295. [**Deep Visual-Semantic Alignments for Generating Image Descriptions**Andrej Karpathy, Li Fei-Fei*2014 · 493 citations*](https://md.research.elamur.ai/free/en/papers/karpathy-2014-deep-visual-semantic-alignments-generating/)
296. [**A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge**Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, et al.*2022 · 490 citations*](https://md.research.elamur.ai/free/en/papers/schwenk-2022-okvqa-benchmark-visual-question-answering/)
297. [**Language Models are Multilingual Chain-of-Thought Reasoners**Freda Shi, Mirac Suzgun, Markus Freitag, et al.*2022 · 490 citations*](https://md.research.elamur.ai/free/en/papers/shi-2022-language-models-multilingual-chain-thought/)
298. [**Phi-4 Technical Report**Marah Abdin, Jyoti Aneja, Harkirat Behl, et al.*2024 · 489 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-4-technical-report/)
299. [**DyNet: The Dynamic Neural Network Toolkit**Graham Neubig, Chris Dyer, Yoav Goldberg, et al.*2017 · 485 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2017-dynet-dynamic-neural-network-toolkit/)
300. [**Semantic Parsing for Single-Relation Question Answering**Wen-tau Yih, Xiaodong He, Christopher Meek*2014 · 483 citations*](https://md.research.elamur.ai/free/en/papers/yih-2014-semantic-parsing-single-relation-question/)
301. [**Regularizing Neural Networks by Penalizing Confident Output Distributions**Gabriel Pereyra, George Tucker, Jan Chorowski, et al.*2017 · 482 citations*](https://md.research.elamur.ai/free/en/papers/pereyra-2017-regularizing-neural-networks-penalizing-confident/)
302. [**DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference**Ji Xin, Raphael Tang, Jaejun Lee, et al.*2020 · 480 citations*](https://md.research.elamur.ai/free/en/papers/xin-2020-deebert-dynamic-early-exiting-accelerating/)
303. [**ToTTo: A Controlled Table-To-Text Generation Dataset**Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, et al.*2020 · 479 citations*](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/)
304. [**Enabling Large Language Models to Generate Text with Citations**Tianyu Gao, Howard Yen, Jiatong Yu, et al.*2023 · 477 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-enabling-large-language-models-generate/)
305. [**ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction**Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al.*2022 · 477 citations*](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/)
306. [**DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature**Eric Mitchell, Yoonho Lee, Alexander Khazatsky, et al.*2023 · 474 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2023-detectgpt-zero-shot-machine-generated/)
307. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 474 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
308. [**Large Language Models: A Survey**Shervin Minaee, Tomas Mikolov, Narjes Nikzad, et al.*2024 · 467 citations*](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/)
309. [**XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning**Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, et al.*2020 · 465 citations*](https://md.research.elamur.ai/free/en/papers/ponti-2020-xcopa-multilingual-dataset-causal-commonsense/)
310. [**Masked Autoencoders Are Scalable Vision Learners**Kaiming He, Xinlei Chen, Saining Xie, et al.*2021 · 463 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-masked-autoencoders-scalable-vision-learners/)
311. [**Denoising Diffusion Implicit Models**Jiaming Song, Chenlin Meng, Stefano Ermon*2020 · 460 citations*](https://md.research.elamur.ai/free/en/papers/song-2020-denoising-diffusion-implicit-models/)
312. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 459 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/)
313. [**Distributional Semantics in Technicolor**Elia Bruni, Gemma Boleda, Marco Baroni, et al.*2012 · 459 citations*](https://md.research.elamur.ai/free/en/papers/bruni-2012-distributional-semantics-technicolor/)
314. [**LinkBERT: Pretraining Language Models with Document Links**Michihiro Yasunaga, Jure Leskovec, Percy Liang*2022 · 452 citations*](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/)
315. [**OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework**Peng Wang, An Yang, Rui Men, et al.*2022 · 452 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-ofa-unifying-architectures-tasks-modalities/)
316. [**Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model**Yitao Cai, Huiyu Cai, Xiaojun Wan*2019 · 450 citations*](https://md.research.elamur.ai/free/en/papers/cai-2019-multi-modal-sarcasm-detection-twitter/)
317. [**OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems**Chaoqun He, Renjie Luo, Yuzhuo Bai, et al.*2024 · 448 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-olympiadbench-challenging-benchmark-promoting-agi/)
318. [**LLM-Pruner: On the Structural Pruning of Large Language Models**Xinyin Ma, Gongfan Fang, Xinchao Wang*2023 · 447 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-llm-pruner-structural-pruning-large/)
319. [**CogView: Mastering Text-to-Image Generation via Transformers**Ming Ding, Zhuoyi Yang, Wenyi Hong, et al.*2021 · 446 citations*](https://md.research.elamur.ai/free/en/papers/ding-2021-cogview-mastering-text-image/)
320. [**Neural Document Summarization by Jointly Learning to Score and Select Sentences**Qingyu Zhou, Nan Yang, Furu Wei, et al.*2018 · 446 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2018-neural-document-summarization-jointly-learning/)
321. [**The Faiss library**Matthijs Douze, Alexandr Guzhva, Chengqi Deng, et al.*2024 · 442 citations*](https://md.research.elamur.ai/free/en/papers/douze-2024-faiss-library/)
322. [**MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts**Pan Lu, Hritik Bansal, Tony Xia, et al.*2023 · 441 citations*](https://md.research.elamur.ai/free/en/papers/lu-2023-mathvista-evaluating-mathematical-reasoning-foundation/)
323. [**MVBench: A Comprehensive Multi-modal Video Understanding Benchmark**Kunchang Li, Yali Wang, Yinan He, et al.*2023 · 441 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-mvbench-comprehensive-multi-modal-video/)
324. [**ShareGPT4V: Improving Large Multi-Modal Models with Better Captions**Lin Chen, Jinsong Li, Xiaoyi Dong, et al.*2023 · 441 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-sharegpt4v-improving-large-multi-modal/)
325. [**LLaVA-OneVision: Easy Visual Task Transfer**Bo Li, Yuanhan Zhang, Dong Guo, et al.*2024 · 440 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llava-onevision-easy-visual-task/)

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