{"total":21,"rows":[{"id":"2306.14610","title":"SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality","created":"2023-06-26","cited_by_count":243,"area":"mllm"},{"id":"2212.07796","title":"CREPE: Can Vision-Language Foundation Models Reason Compositionally?","created":"2022-12-13","cited_by_count":181,"area":"mllm"},{"id":"2305.03689","title":"COLA: A Benchmark for Compositional Text-to-image Retrieval","created":"2023-05-05","cited_by_count":53,"area":"mllm"},{"id":"2303.14465","title":"Equivariant Similarity for Vision-Language Foundation Models","created":"2023-03-25","cited_by_count":52,"area":"mllm"},{"id":"2303.02995","title":"HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention","created":"2023-03-06","cited_by_count":44,"area":"mllm"},{"id":"2304.02560","title":"VicTR: Video-conditioned Text Representations for Activity Recognition","created":"2023-04-05","cited_by_count":28,"area":"mllm"},{"id":"2409.17958","title":"The Hard Positive Truth about Vision-Language Compositionality","created":"2024-09-26","cited_by_count":22,"area":"mllm"},{"id":"2411.18042","title":"HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation","created":"2024-11-27","cited_by_count":19,"area":"mllm"},{"id":"2404.02145","title":"Iterated Learning Improves Compositionality in Large Vision-Language Models","created":"2024-04-02","cited_by_count":17,"area":"mllm"},{"id":"2501.08326","title":"Omni-RGPT: Unifying Image and Video Region-level Understanding via Token Marks","created":"2025-01-14","cited_by_count":17,"area":"mllm"},{"id":"2303.09756","title":"Video Action Recognition with Attentive Semantic Units","created":"2023-03-17","cited_by_count":12,"area":"mllm"},{"id":"2312.17345","title":"3VL: Using Trees to Improve Vision-Language Models' Interpretability","created":"2023-12-28","cited_by_count":7,"area":"mllm"},{"id":"2510.15963","title":"ESCA: Contextualizing Embodied Agents via Scene-Graph Generation","created":"2025-10-11","cited_by_count":6,"area":"mllm"},{"id":"2508.00399","title":"iSafetyBench: A video-language benchmark for safety in industrial environment","created":"2025-08-01","cited_by_count":5,"area":"mllm"},{"id":"2505.07815","title":"Imagine, Verify, Execute: Memory-guided Agentic Exploration with Vision-Language Models","created":"2025-05-12","cited_by_count":2,"area":"mllm"},{"id":"2605.08064","title":"Proxy3D: Efficient 3D Representations for Vision-Language Models via Semantic Clustering and Alignment","created":"2026-05-08","cited_by_count":2,"area":"mllm"},{"id":"2605.13667","title":"SceneGraphVLM: Dynamic Scene Graph Generation from Video with Vision-Language Models","created":"2026-05-13","cited_by_count":1,"area":"mllm"},{"id":"2503.15846","title":"A Closer Look at Dynamic Scene Graph Generation In the Era of Multimodal Large Language Models","created":"2025-03-20","cited_by_count":0,"area":"mllm"},{"id":"2603.06600","title":"FuzzingRL: Reinforcement Fuzz-Testing for Revealing VLM Failures","created":"2026-02-17","cited_by_count":0,"area":"mllm"},{"id":"2606.15417","title":"From Frames to Temporal Graphs: In-Context Egocentric Action Recognition with Vision-Language Models","created":"2026-06-13","cited_by_count":0,"area":"mllm"},{"id":"2606.31187","title":"Learning to Deny: Action Denial in Multimodal Large Language Models","created":"2026-06-30","cited_by_count":0,"area":"mllm"}],"next_cursor":null}