Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation | Paper Citation Record

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This paper cites Ridnik, Nadav Zamir, Asaf Noy, Ita- mar Friedman, Matan Protter, and Lihi Zelnik-Manor.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Ridnik, Nadav Zamir, Asaf Noy, Ita- mar Friedman, Matan Protter, and Lihi Zelnik-Manor

Reference 1

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This paper cites Learning imbalanced datasets with label- distribution-aware margin loss.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Learning imbalanced datasets with label- distribution-aware margin loss

Reference 2

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This paper cites On evaluating ad- versarial robustness, 2019.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation On evaluating ad- versarial robustness, 2019

Reference 3

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This paper cites SMOTE: Synthetic Minority Over-sampling Technique.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation SMOTE: Synthetic Minority Over-sampling Technique

Reference 4

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This paper cites Knowledge-embedded routing network for scene graph gen- eration, 2019.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Knowledge-embedded routing network for scene graph gen- eration, 2019

Reference 5

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This paper cites Expanding scene graph boundaries: Fully open-vocabulary scene graph generation via visual-concept alignment and retention, 2023.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Expanding scene graph boundaries: Fully open-vocabulary scene graph generation via visual-concept alignment and retention, 2023

Reference 6

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This paper cites Ackermann, M.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Ackermann, M

Reference 7

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This paper cites Class-balanced loss based on effective number of samples, 2019.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Class-balanced loss based on effective number of samples, 2019

Reference 8

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unresolved cited work

Reference 9

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unresolved cited work

Reference 10

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Observation 6a96b4a1-9d1b-4507-943f-f123b29a90e4 · outbound

This paper cites Exploiting long-term de- pendencies for generating dynamic scene graphs.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Exploiting long-term de- pendencies for generating dynamic scene graphs

Reference 11

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Schapire

Reference 12

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This paper cites Automated Curriculum Learning for Neural Networks.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Automated Curriculum Learning for Neural Networks

Reference 13

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This paper cites On the power of cur- riculum learning in training deep networks.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation On the power of cur- riculum learning in training deep networks

Reference 14

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This paper cites Learning to Weight Samples for Dynamic Early-exiting Networks.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Learning to Weight Samples for Dynamic Early-exiting Networks

Reference 15

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This paper cites Learning deep representation for imbalanced classifica- tion.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Learning deep representation for imbalanced classifica- tion

Reference 16

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This paper cites Adversarial examples are not bugs, they are features, 2019.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Adversarial examples are not bugs, they are features, 2019

Reference 17

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This paper cites Papadopoulos, and Vittorio Fer- rari.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Papadopoulos, and Vittorio Fer- rari

Reference 18

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This paper cites Action genome: Actions as compo- sition of spatio-temporal scene graphs.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Action genome: Actions as compo- sition of spatio-temporal scene graphs

Reference 19

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This paper cites Easy samples first: Self-paced reranking for zero-example multimedia search.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Easy samples first: Self-paced reranking for zero-example multimedia search

Reference 20

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This paper cites Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels

Reference 21

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Decoupling Representation and Classifier for Long-Tailed Recognition

Reference 22

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This paper cites Correlation debiasing for unbiased scene graph generation in videos, 2023.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Correlation debiasing for unbiased scene graph generation in videos, 2023

Reference 23

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This paper cites Llm4sgg: Large language model for weakly supervised scene graph generation, 2023.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Llm4sgg: Large language model for weakly supervised scene graph generation, 2023

Reference 24

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This paper cites 3-d scene graph: A sparse and semantic representa- tion of physical environments for intelligent agents.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation 3-d scene graph: A sparse and semantic representa- tion of physical environments for intelligent agents

Reference 25

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Shamma, Michael S

Reference 26

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This paper cites Pawan Kumar, Ben Packer, and Daphne Koller.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Pawan Kumar, Ben Packer, and Daphne Koller

Reference 27

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This paper cites Zero-shot visual relation detection via com- posite visual cues from large language models, 2023.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Zero-shot visual relation detection via com- posite visual cues from large language models, 2023

Reference 28

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This paper cites Long-tailed visual recognition via gaussian clouded logit adjustment.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Long-tailed visual recognition via gaussian clouded logit adjustment

Reference 29

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Girshick, Kaiming He, and Piotr Doll´ar

Reference 30

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Teacher–student curriculum learning

Reference 31

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unbiased scene graph generation in videos

Reference 32

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Rethink- ing learning approaches for long-term action anticipation,

Reference 33

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Factors in finetuning deep model for object detection with long-tail distribution

Reference 34

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Robust learning of tractable probabilistic models

Reference 35

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This paper cites CaptainCook4D: A Dataset for Understanding Errors in Procedural Activities,.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation CaptainCook4D: A Dataset for Understanding Errors in Procedural Activities,

Reference 36

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This paper cites Towards scene graph anticipation.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Towards scene graph anticipation

Reference 37

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Learning to reweight examples for robust deep learning

Reference 38

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Schein and Lyle H

Reference 39

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation As- sembly101: A large-scale multi-view video dataset for under- standing procedural activities, 2022

Reference 40

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Video visual relation detection

Reference 41

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Relationformer: A unified framework for image-to-graph generation, 2022

Reference 42

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Girshick

Reference 43

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unresolved cited work

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Equalization loss for long-tailed object recognition

Reference 45

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Attention is all you need

Reference 46

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Long-tailed Recognition by Routing Diverse Distribution-Aware Experts

Reference 47

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Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Learn- ing to model the tail

Reference 48

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This paper cites Curriculum learning by transfer learning: Theory and experiments with deep net- works.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Curriculum learning by transfer learning: Theory and experiments with deep net- works

Reference 49

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This paper cites Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets

Reference 50

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This paper cites Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification

Reference 51

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This paper cites Grasping trajectory optimization with point clouds, 2024.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Grasping trajectory optimization with point clouds, 2024

Reference 52

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This paper cites Robinson, and Ren´e Vidal.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Robinson, and Ren´e Vidal

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.546463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation af463ae3-b864-4b9d-a127-5d43be58096d · outbound

This paper cites Range loss for deep face recognition with long- tailed training data.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Range loss for deep face recognition with long- tailed training data

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.529816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation da081710-00e3-4261-8cff-10c905de4f69 · outbound

This paper cites Zhang, Z.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Zhang, Z

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.514110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ef6f2b3b-5fc8-4164-a169-eec531976305 · outbound

This paper cites Less is more: Toward zero-shot local scene graph generation via foundation models, 2023.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Less is more: Toward zero-shot local scene graph generation via foundation models, 2023

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.497164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7395e3af-8b28-46d9-8723-263d7774d910 · outbound

This paper cites Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.477373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7ffd1084-1c9a-40d9-a8a8-d7d3e0dd2207 · outbound

This paper cites Vlprompt: Vision-language prompting for panoptic scene graph genera- tion, 2023.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Vlprompt: Vision-language prompting for panoptic scene graph genera- tion, 2023

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.460904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 764d863f-cfff-41ab-8f4b-43fc6b9dfa19 · outbound

This paper cites Motivation.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Motivation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.443359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c476a49d-8f65-4c7a-a066-f096a57e2b0a · outbound

This paper cites Structured Visual Representation.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Structured Visual Representation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.418179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f6bbefe6-3bd4-4b88-812a-01aa574b66e3 · outbound

This paper cites Approach 16 10.1 .IMPARTAIL.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Approach 16 10.1 .IMPARTAIL

Reference 62

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T17:00:21.399435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1f435df7-aa22-4c83-aa82-095f7dde964d · outbound

This paper cites Motivation.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Motivation

Reference 63

Resolution: parse uncertain; raw_fallback, observed 2026-08-12T17:00:21.378614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 38f4bd63-b417-4882-8388-929560d7c899 · outbound

This paper cites This imbalance leads to biased models that fail to generalize effectively across all relationship types, compromising nuanced and accurate scene understanding.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation This imbalance leads to biased models that fail to generalize effectively across all relationship types, compromising nuanced and accurate scene understanding

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.363747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 668ec6b0-2313-46b6-a988-77343c1ddd43 · outbound

This paper cites These distributional shifts degrade performance, limiting the applicability of scene graph models in dynamic and unpredictable environments.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation These distributional shifts degrade performance, limiting the applicability of scene graph models in dynamic and unpredictable environments

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.345226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 05b98c69-108b-4416-8c6f-26afb8b6daf7 · outbound

This paper cites Structured Visual Representation Tasks.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Structured Visual Representation Tasks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.327136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d4de632d-ccf8-49db-808f-15d59846b18b · outbound

This paper cites an unresolved cited work.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:00:21.311363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 958951c2-7279-4924-a87e-27c385373fb3 · outbound

This paper cites Asymmetric Loss (ASL) [1] and Distribution-Balanced (DB) loss [50] focus on balancing positive and negative labels in multi-label classification.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Asymmetric Loss (ASL) [1] and Distribution-Balanced (DB) loss [50] focus on balancing positive and negative labels in multi-label classification

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.296526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7e5aa955-806c-4932-ba78-d609b1796d6f · outbound

This paper cites • This is a primary concern of the field as the Action Genome is the only large-scale dataset available as a testbed for the Spatio-Temporal Scene Graph tasks.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation • This is a primary concern of the field as the Action Genome is the only large-scale dataset available as a testbed for the Spatio-Temporal Scene Graph tasks

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.282097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 088e9229-291a-4df3-8e0d-d0246d8b538f · outbound

This paper cites Instead, our work can be considered a starting point for further developing robust learning techniques.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Instead, our work can be considered a starting point for further developing robust learning techniques

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.265118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 304f7390-8320-4cf3-a612-04db09b34a88 · outbound

This paper cites an unresolved cited work.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:00:21.250820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 935d41c0-0d14-4317-b481-9c6c65d02134 · outbound

This paper cites These might fail to capture the performance over higher-order spatial and temporal relationships.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation These might fail to capture the performance over higher-order spatial and temporal relationships

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.237254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8d7e54b4-e9ff-42df-ae7c-95452f677dcd · outbound

This paper cites IMPARTAIL Here, we present the complete algorithm for the proposed unbiased learning framework.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation IMPARTAIL Here, we present the complete algorithm for the proposed unbiased learning framework

Reference 73

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T17:00:21.222250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6aaa334b-024e-4eb7-8b47-300948ce76eb · outbound

This paper cites an unresolved cited work.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:00:21.209225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5360f307-7196-4766-9869-1a2a1d5114cc · outbound

This paper cites an unresolved cited work.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unresolved cited work

Reference 75

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:00:21.195298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation afaa2070-0409-4de9-bf98-296ca5bebb7e · outbound

This paper cites Note: ORPU, STPU, and the predicate decoders can be adapted from any VidSGG or SGA method following an object-centric framework.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Note: ORPU, STPU, and the predicate decoders can be adapted from any VidSGG or SGA method following an object-centric framework

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.182195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ea118c2d-3130-4037-83de-ed797ae9cfda · outbound

This paper cites Focusing on underrepresented classes helps the model achieve a balanced class distribution.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Focusing on underrepresented classes helps the model achieve a balanced class distribution

Reference 77

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T17:00:21.166837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation efc02e7d-c302-4234-9049-61f25b61c713 · outbound

This paper cites Video Scene Graph Generation 11.1.1.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Video Scene Graph Generation 11.1.1

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.150926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cd4780b8-26f4-4dff-828a-c572d4d5110f · outbound

This paper cites – In section 12, we provide findings corresponding to the proposed training scenarios.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation – In section 12, we provide findings corresponding to the proposed training scenarios

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.114859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation faf99be1-36d3-4ccd-8448-aef20b0e946a · outbound

This paper cites an unresolved cited work.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:00:21.136078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8d12c90c-2e93-4d54-9681-417177f70ba9 · outbound

This paper cites – In section 12, we provide findings corresponding to the proposed training scenarios.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation – In section 12, we provide findings corresponding to the proposed training scenarios

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.097705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 58865b06-7cab-4198-b9f3-ed9093e21c7d · outbound

This paper cites Video Scene Graph Generation 12.1.1.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Video Scene Graph Generation 12.1.1

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.083152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fcbad00c-0698-4671-9fa1-0d56624b3919 · outbound

This paper cites an unresolved cited work.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:00:21.063998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4d84d296-d348-4fab-be43-15bf40bf6e01 · outbound

This paper cites an unresolved cited work.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-12T17:00:21.044028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 782a5b62-2228-4709-b3a6-baa44436daa3 · outbound

This paper cites PREDCLS mode shows less variability in recall changes using our method but substantially increases mean recalls @50 for STTran, jumping from 34.80 to 52.90.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation PREDCLS mode shows less variability in recall changes using our method but substantially increases mean recalls @50 for STTran, jumping from 34.80 to 52.90

Reference 86

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T17:00:21.027985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c7b16c39-b9c6-4d42-a722-213d8ce9133d · outbound

This paper cites (a) In Table 8, SceneSayerODE shows the most consistent gain in lower recall metrics (R@10 and mR@10) when IMPARTAIL is included.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation (a) In Table 8, SceneSayerODE shows the most consistent gain in lower recall metrics (R@10 and mR@10) when IMPARTAIL is included

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:21.012274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 044a9abd-5312-4a33-a7db-9101e688549a · outbound

This paper cites (a) As F increases from 0.3 to 0.9, the improvements in mR metrics, particularly for mR@10 and mR@20, become more pronounced.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation (a) As F increases from 0.3 to 0.9, the improvements in mR metrics, particularly for mR@10 and mR@20, become more pronounced

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:20.997264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cf1e658f-5df7-45e1-8c2f-e31b2615ed32 · outbound

This paper cites (a) At lower F values (e.g., F=0.3), the improvements in mR metrics are moderate.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation (a) At lower F values (e.g., F=0.3), the improvements in mR metrics are moderate

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:00:20.983313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 50be49ae-32d5-4e78-b047-5a80f27ee471 · outbound

This paper cites Table 24, Table 25, Table 26, Table 27, present the With Constraint evaluation results for Scene Graph Generation (SGA) for PGAGS.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Table 24, Table 25, Table 26, Table 27, present the With Constraint evaluation results for Scene Graph Generation (SGA) for PGAGS

Reference 90

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T17:00:20.967584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2625d2a2-4add-43be-8ba7-f43d0f82cf6a · outbound

This paper cites an unresolved cited work.

Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation Unresolved cited work

Reference 2021

Resolution: parse uncertain; raw_fallback, observed 2026-08-12T17:00:22.172389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers