{"total":23,"rows":[{"id":"2312.03700","title":"OneLLM: One Framework to Align All Modalities with Language","created":"2023-12-06","cited_by_count":187,"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":"2308.03685","title":"Learning Concise and Descriptive Attributes for Visual Recognition","created":"2023-08-07","cited_by_count":73,"area":"mllm"},{"id":"2402.05741","title":"Real-World Robot Applications of Foundation Models: A Review","created":"2024-02-08","cited_by_count":61,"area":"mllm"},{"id":"2304.04231","title":"CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model","created":"2023-04-09","cited_by_count":47,"area":"mllm"},{"id":"2403.20312","title":"Learn \"No\" to Say \"Yes\" Better: Improving Vision-Language Models via Negations","created":"2024-03-29","cited_by_count":46,"area":"mllm"},{"id":"2405.10255","title":"When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models","created":"2024-05-16","cited_by_count":43,"area":"mllm"},{"id":"2311.17618","title":"ShapeGPT: 3D Shape Generation with A Unified Multi-modal Language Model","created":"2023-11-29","cited_by_count":39,"area":"mllm"},{"id":"2406.13875","title":"WATT: Weight Average Test-Time Adaptation of CLIP","created":"2024-06-19","cited_by_count":33,"area":"mllm"},{"id":"2507.20198","title":"A Survey of Token Compression for Efficient Multimodal Large Language Models","created":"2025-07-27","cited_by_count":25,"area":"mllm"},{"id":"2303.06591","title":"Accommodating Audio Modality in CLIP for Multimodal Processing","created":"2023-03-12","cited_by_count":18,"area":"mllm"},{"id":"2407.16977","title":"Selective Vision-Language Subspace Projection for Few-shot CLIP","created":"2024-07-24","cited_by_count":18,"area":"mllm"},{"id":"2510.17205","title":"$\\mathcal{V}isi\\mathcal{P}runer$: Decoding Discontinuous Cross-Modal Dynamics for Efficient Multimodal LLMs","created":"2025-10-20","cited_by_count":17,"area":"mllm"},{"id":"2405.00754","title":"CLIPArTT: Adaptation of CLIP to New Domains at Test Time","created":"2024-05-01","cited_by_count":14,"area":"mllm"},{"id":"2505.21844","title":"Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation","created":"2025-05-28","cited_by_count":10,"area":"mllm"},{"id":"2509.21990","title":"WAVE: Learning Unified & Versatile Audio-Visual Embeddings with Multimodal LLM","created":"2025-09-26","cited_by_count":7,"area":"mllm"},{"id":"2409.17727","title":"Robotic-CLIP: Fine-tuning CLIP on Action Data for Robotic Applications","created":"2024-09-26","cited_by_count":5,"area":"mllm"},{"id":"2509.21033","title":"SupCLAP: Controlling Optimization Trajectory Drift in Audio-Text Contrastive Learning with Support Vector Regularization","created":"2025-09-25","cited_by_count":3,"area":"mllm"},{"id":"2308.10185","title":"ViT-Lens: Initiating Omni-Modal Exploration through 3D Insights","created":"2023-08-20","cited_by_count":1,"area":"mllm"},{"id":"2605.07574","title":"PolarVLM: Bridging the Semantic-Physical Gap in Vision-Language Models","created":"2026-05-08","cited_by_count":1,"area":"mllm"},{"id":"2608.08418","title":"Learning Deep Modality-Shared Self-Expressiveness for Image Clustering with Textual Information","created":"2026-08-09","cited_by_count":0,"area":"mllm"},{"id":"2608.17203","title":"Expressivity In Multimodal Contrastive Learning","created":"2026-08-17","cited_by_count":0,"area":"mllm"},{"id":"2608.29395","title":"GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models","created":"2026-08-29","cited_by_count":0,"area":"mllm"}],"next_cursor":null}