{"total":23,"rows":[{"id":"2204.14198","title":"Flamingo: a Visual Language Model for Few-Shot Learning","created":"2022-04-29","cited_by_count":4386,"area":"mllm"},{"id":"2312.02051","title":"TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding","created":"2023-12-04","cited_by_count":406,"area":"mllm"},{"id":"2401.13601","title":"MM-LLMs: Recent Advances in MultiModal Large Language Models","created":"2024-01-24","cited_by_count":278,"area":"mllm"},{"id":"2302.14115","title":"Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning","created":"2023-02-27","cited_by_count":263,"area":"mllm"},{"id":"2202.09061","title":"VLP: A Survey on Vision-Language Pre-training","created":"2022-02-18","cited_by_count":159,"area":"mllm"},{"id":"2410.19702","title":"TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning","created":"2024-10-25","cited_by_count":106,"area":"mllm"},{"id":"2311.18775","title":"CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation","created":"2023-11-30","cited_by_count":63,"area":"mllm"},{"id":"2502.10810","title":"SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding","created":"2025-02-15","cited_by_count":57,"area":"mllm"},{"id":"2408.12763","title":"Assessing Modality Bias in Video Question Answering Benchmarks with Multimodal Large Language Models","created":"2024-08-22","cited_by_count":23,"area":"mllm"},{"id":"2401.06129","title":"Distilling Vision-Language Models on Millions of Videos","created":"2024-01-11","cited_by_count":20,"area":"mllm"},{"id":"2508.10922","title":"A Survey on Video Temporal Grounding with Multimodal Large Language Model","created":"2025-08-07","cited_by_count":20,"area":"mllm"},{"id":"2306.10169","title":"Meta-Personalizing Vision-Language Models to Find Named Instances in Video","created":"2023-06-16","cited_by_count":16,"area":"mllm"},{"id":"2402.01293","title":"Can MLLMs Perform Text-to-Image In-Context Learning?","created":"2024-02-02","cited_by_count":13,"area":"mllm"},{"id":"2510.17023","title":"Enrich and Detect: Video Temporal Grounding with Multimodal LLMs","created":"2025-10-19","cited_by_count":12,"area":"mllm"},{"id":"2501.00958","title":"2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining","created":"2025-01-01","cited_by_count":9,"area":"mllm"},{"id":"2306.06306","title":"DocumentCLIP: Linking Figures and Main Body Text in Reflowed Documents","created":"2023-06-09","cited_by_count":7,"area":"mllm"},{"id":"2509.12145","title":"Open-ended Hierarchical Streaming Video Understanding with Vision Language Models","created":"2025-09-15","cited_by_count":6,"area":"mllm"},{"id":"2309.15915","title":"Zero-Shot and Few-Shot Video Question Answering with Multi-Modal Prompts","created":"2023-09-27","cited_by_count":5,"area":"mllm"},{"id":"2506.06275","title":"Movie Facts and Fibs (MF$^2$): A Benchmark for Long Movie Understanding","created":"2025-06-06","cited_by_count":5,"area":"mllm"},{"id":"2310.05060","title":"DeVAn: Dense Video Annotation for Video-Language Models","created":"2023-10-08","cited_by_count":3,"area":"mllm"},{"id":"2305.02297","title":"Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime","created":"2023-05-03","cited_by_count":1,"area":"mllm"},{"id":"2310.04914","title":"Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks","created":"2023-10-07","cited_by_count":1,"area":"mllm"},{"id":"2510.10426","title":"Taming a Retrieval Framework to Read Images in Humanlike Manner for Augmenting Generation of MLLMs","created":"2025-10-12","cited_by_count":1,"area":"mllm"}],"next_cursor":null}