DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes | Paper Citation Record

Observation 2f0771a3-f1ca-4c86-8055-a9b79bb5f917 · outbound

This paper cites Conceptgraphs: Open-vocabulary 3d scene graphs for perception and planning,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Conceptgraphs: Open-vocabulary 3d scene graphs for perception and planning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.709445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4c7416d5-173b-4e37-be71-32794146e0e3 · outbound

This paper cites Beyond bare queries: Open-vocabulary object retrieval with 3d scene graph,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Beyond bare queries: Open-vocabulary object retrieval with 3d scene graph,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.700268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 84232814-3510-4da1-a4f1-dd96325f2251 · outbound

This paper cites Search3d: Hierarchical open-vocabulary 3d segmenta- tion,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Search3d: Hierarchical open-vocabulary 3d segmenta- tion,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.689789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d7074e35-bd7b-4e54-99cb-ece5e1f239d7 · outbound

This paper cites Hierarchical open-vocabulary 3d scene graphs for language-grounded robot navigation,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Hierarchical open-vocabulary 3d scene graphs for language-grounded robot navigation,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.891225Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7d94e18-e615-42c0-b8b0-51c029add4f6 · outbound

This paper cites Clio: Real-time task-driven open-set 3d scene graphs,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Clio: Real-time task-driven open-set 3d scene graphs,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.895182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9494be8-c440-42a5-b3bf-6204d5c977cd · outbound

This paper cites 4d panoptic scene graph generation,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes 4d panoptic scene graph generation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.668082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 46c15d20-7df7-4f50-a137-3da68dc4bdce · outbound

This paper cites G-retriever: Retrieval-augmented generation for textual graph understanding and question answering,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes G-retriever: Retrieval-augmented generation for textual graph understanding and question answering,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.658325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 645bd11b-dc08-4e04-8761-17006d2928db · outbound

This paper cites Let Your Graph Do the Talking: Encoding Structured Data for LLMs.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Let Your Graph Do the Talking: Encoding Structured Data for LLMs

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.905097Z

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Observation e8cc9849-0fb3-46d9-bcf3-73b8e8b9bccb · outbound

This paper cites Can llms enhance performance prediction for deep learning models?.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Can llms enhance performance prediction for deep learning models?

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.647941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8128cca7-1eee-44b0-a647-76d99cacdfdf · outbound

This paper cites STAR: A Benchmark for Situated Reasoning in Real-World Videos.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes STAR: A Benchmark for Situated Reasoning in Real-World Videos

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.912140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c57e5aa-525e-46ac-9490-95e8720d9733 · outbound

This paper cites Agqa 2.0: An updated benchmark for compositional spatio-temporal reasoning,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Agqa 2.0: An updated benchmark for compositional spatio-temporal reasoning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.637619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1885515b-95f2-4100-8240-6e09a118c67a · outbound

This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annotations,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Visual genome: Connecting language and vision using crowdsourced dense image annotations,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.922505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f758b0d9-18c6-47ca-b6e9-be1bb9780696 · outbound

This paper cites Gqa: A new dataset for real- world visual reasoning and compositional question answering,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Gqa: A new dataset for real- world visual reasoning and compositional question answering,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.925544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a767cad-a860-4189-86b5-bef289d6efa8 · outbound

This paper cites Panoptic scene graph generation,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Panoptic scene graph generation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.618448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 65521ffd-71c5-42e0-b747-d37d6c798f85 · outbound

This paper cites Action genome: Actions as compositions of spatio-temporal scene graphs,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Action genome: Actions as compositions of spatio-temporal scene graphs,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.609510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0fed5c8e-ac80-492e-a37b-dd2684cfbfa4 · outbound

This paper cites Panoptic video scene graph generation,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Panoptic video scene graph generation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.600160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a090021c-ddbf-4edd-b9a9-393dcf6de988 · outbound

This paper cites Egtr: Extracting graph from transformer for scene graph generation,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Egtr: Extracting graph from transformer for scene graph generation,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.938041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30a67c2d-1411-459d-a581-f32df200fe56 · outbound

This paper cites Reltr: Relation transformer for scene graph generation,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Reltr: Relation transformer for scene graph generation,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.941400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6806e38-1f43-436e-a43a-bf1604f5a723 · outbound

This paper cites Oed: towards one-stage end-to- end dynamic scene graph generation,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Oed: towards one-stage end-to- end dynamic scene graph generation,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.580376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1605903d-9619-4b17-b3b2-2cfbf2bf3ee1 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes SAM 2: Segment Anything in Images and Videos

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.948634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e316375-8fa3-404a-83ff-1832345ffe1f · outbound

This paper cites Llavanext: Improved reasoning, ocr, and world knowledge,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Llavanext: Improved reasoning, ocr, and world knowledge,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.952811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22188316-e050-47d6-9f6f-be776b314683 · outbound

This paper cites Yolo-world: Real-time open-vocabulary object detection,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Yolo-world: Real-time open-vocabulary object detection,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.956865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3418715-8ace-498d-bcf7-ef9b3d605286 · outbound

This paper cites GPT-4 Technical Report.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes GPT-4 Technical Report

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.960371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c725269-06a3-4ead-ae5e-0a3f44765277 · outbound

This paper cites Yi: Open Foundation Models by 01.AI.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Yi: Open Foundation Models by 01.AI

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.963321Z

Source-reported events for the cited work

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Observation 77f7d309-02d3-4c7c-b892-64d46f8a505c · outbound

This paper cites NVILA: Efficient Frontier Visual Language Models.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes NVILA: Efficient Frontier Visual Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.967414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 740d89b9-7117-4b46-a85b-ff8648391f89 · outbound

This paper cites Qwen2.5-VL Technical Report.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Qwen2.5-VL Technical Report

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.970524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a8e3370-8c8f-4ecf-ad26-162d9a30876b · outbound

This paper cites Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Graph-Based Captioning: Enhancing Visual Descriptions by Interconnecting Region Captions

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.973960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04a36963-6c86-4e53-87ab-7c9073ebd39a · outbound

This paper cites ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.977587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6eeaa08-33b7-48ca-b31f-e80b1c3944c1 · outbound

This paper cites Llm4sgg: large language models for weakly supervised scene graph generation,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Llm4sgg: large language models for weakly supervised scene graph generation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.560483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 93d64783-f5cc-43c8-b905-59f1660eccb1 · outbound

This paper cites Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.984048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db1551b9-ffdd-478d-b7fb-7678a1a290c6 · outbound

This paper cites From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understanding.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes From Seconds to Hours: Reviewing MultiModal Large Language Models on Comprehensive Long Video Understanding

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.987304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9818144f-18fa-476e-a85b-1e19b57e3fa5 · outbound

This paper cites Visual Large Language Models for Generalized and Specialized Applications.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Visual Large Language Models for Generalized and Specialized Applications

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.990891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d0cf3e8-42d0-4d39-a3f8-79333589329e · outbound

This paper cites (2.5+ 1) d spatio- temporal scene graphs for video question answering,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes (2.5+ 1) d spatio- temporal scene graphs for video question answering,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.551272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4cc7d048-e096-4191-b2f3-254a8c9b05f7 · outbound

This paper cites Action scene graphs for long-form understanding of egocentric videos,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Action scene graphs for long-form understanding of egocentric videos,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.541815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1b858b1f-bfeb-47f1-b0bd-5e31f8284cb9 · outbound

This paper cites HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:50:44.318336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d0f2412a-688e-4361-b79c-5c67e17a3d8c · outbound

This paper cites STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.003979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1abcacc-c6b9-435a-a869-a612ea80fcd2 · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.007182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4250c15-2b0a-4a16-ae60-b28dee74ec5e · outbound

This paper cites Benchmarking graph neural networks,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Benchmarking graph neural networks,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.010411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d938cc35-c39c-4172-ac51-f9f679921791 · outbound

This paper cites Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.013299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd3c880a-c483-4c30-9183-6ac3d7a94fd0 · outbound

This paper cites Roformer: En- hanced transformer with rotary position embedding,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Roformer: En- hanced transformer with rotary position embedding,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.016593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a12445ac-7fcb-4ea8-88e4-d207e0a54489 · outbound

This paper cites Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.019556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cd2c11b-fd18-4e51-806c-f2e36ee7cb5b · outbound

This paper cites Agqa: A benchmark for compositional spatio-temporal reasoning,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Agqa: A benchmark for compositional spatio-temporal reasoning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.516269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c168e277-342d-4817-9a93-37fe007677cd · outbound

This paper cites Lora: Low-rank adaptation of large language models.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Lora: Low-rank adaptation of large language models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.025383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8f94f6c-05ae-411a-8abb-bfb10691e8ad · outbound

This paper cites The Llama 3 Herd of Models.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes The Llama 3 Herd of Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.029059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85b94c4e-2a7b-44a0-9418-7c43271d08d7 · outbound

This paper cites Do We Really Need Complicated Model Architectures For Temporal Networks?.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Do We Really Need Complicated Model Architectures For Temporal Networks?

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.032856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc2ae432-b86d-49e9-82a7-11240a495aa5 · outbound

This paper cites Attention is all you need,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Attention is all you need,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.036476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea23b79a-d636-4a5f-9585-088fc0a12db6 · outbound

This paper cites Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.040102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3d640f6-5953-462d-85d4-55aaab99866f · outbound

This paper cites Mist: Multi-modal iterative spatial-temporal transformer for long-form video question answering,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Mist: Multi-modal iterative spatial-temporal transformer for long-form video question answering,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.495603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 41a135df-9a6f-469b-b794-3ed9cdc277a5 · outbound

This paper cites Self-chained image-language model for video localization and question answering,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Self-chained image-language model for video localization and question answering,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.485545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 219cc9f7-2e91-40e7-a4f4-2e2d071e4456 · outbound

This paper cites Vila: Efficient video-language alignment for video question answering,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Vila: Efficient video-language alignment for video question answering,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.476300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 17ca962d-91c5-44f7-ae3e-ab8089b6aac4 · outbound

This paper cites End-to-End Video Question Answering with Frame Scoring Mechanisms and Adaptive Sampling.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes End-to-End Video Question Answering with Frame Scoring Mechanisms and Adaptive Sampling

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.054394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c57fd8c-d894-4d96-baa6-3b39df3c6587 · outbound

This paper cites Look, Remember and Reason: Grounded reasoning in videos with language models.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Look, Remember and Reason: Grounded reasoning in videos with language models

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:50:44.244898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9fe86359-9402-4674-81ae-90c404ae6f0a · outbound

This paper cites Glance and focus: Memory prompt- ing for multi-event video question answering,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Glance and focus: Memory prompt- ing for multi-event video question answering,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.465775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 204bab2a-2e42-4642-97bb-685fc96fff48 · outbound

This paper cites Learning to reason iteratively and parallelly for complex visual reasoning scenarios,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Learning to reason iteratively and parallelly for complex visual reasoning scenarios,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.456171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7e94c167-1768-4650-bcc6-10a6cde4d138 · outbound

This paper cites Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.067588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fec0ce2d-c820-44ef-b6b5-a7500e15d60f · outbound

This paper cites Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:50:44.134813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e527ce8f-8f68-4236-8d92-491033c3c6e2 · outbound

This paper cites G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.074558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49832ac9-9274-4e89-9d63-778c489cd185 · outbound

This paper cites A note on the prize collecting traveling salesman problem,.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes A note on the prize collecting traveling salesman problem,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:50:44.445522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 825f9578-8f73-459c-9c8b-16f2f5cc1acb · outbound

This paper cites FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:44.081050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c137e4b7-3635-4d63-91ba-aceac1a5e0ed · outbound

This paper cites AGQA 2.0: An Updated Benchmark for Compositional Spatio-Temporal Reasoning.

DyGEnc: Encoding a Sequence of Textual Scene Graphs to Reason and Answer Questions in Dynamic Scenes AGQA 2.0: An Updated Benchmark for Compositional Spatio-Temporal Reasoning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T23:50:43.919104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers