EVIDENT routes MLLM adaptation for video temporal grounding through entity-grounded visual evidence using an Entity Bottleneck Adapter, Entity-Binding Distillation, and Entity-to-eVidence gating to improve cross-domain robustness.
hub
arXiv preprint arXiv:2403.10228 , year=
17 Pith papers cite this work. Polarity classification is still indexing.
17
Pith papers citing it
hub tools
citation-role summary
baseline 2
background 1
citation-polarity summary
representative citing papers
A proposer-solver agent pair achieves supervised-level video temporal grounding and fine-grained captioning from 2.5K unlabeled videos via self-reinforcing evolution.
OmniVTG creates a new large-scale open-world VTG dataset using iterative concept-gap filling and timestamped captioning, paired with a three-stage self-correction CoT paradigm that yields SOTA zero-shot results on four existing benchmarks.
Bridge-STG decouples spatio-temporal alignment via semantic bridging and query-guided localization modules to achieve state-of-the-art m_vIoU of 34.3 on VidSTG among MLLM methods.
Fully end-to-end training with a sentence-conditioned adapter outperforms frozen-backbone baselines for localizing video segments that match sentence queries.
TaRO improves video temporal grounding in MLLMs via constructive reasoning exploration from dense captions and a temporal-sensitivity reward that uses logit drops on disrupted event boundaries, followed by curriculum learning to SOTA results.
LyraV uses FDTC and SToP for per-frame incremental decoding to reach 98.29% video synchrony at 3.89 FPS while preserving general understanding.
DRIFT adapts pretrained VLMs to continuous decoding via a base predictor plus residual flow matching, outperforming regression and generative baselines on grounding and robotic control tasks.
MarkIt converts videos into query-conditioned marked versions via a linguistic-parsing and open-vocabulary segmentation bridge that embeds instance masks, semantic markers, and frame indices to improve Vid-LLM temporal grounding.
ViLL-E introduces a dynamic embedding mechanism and joint contrastive-generative training for VideoLLMs, delivering up to 7% gains in temporal localization and 4% in video retrieval while enabling new zero-shot capabilities.
UniversalVTG is a lightweight foundation model for video temporal grounding that achieves state-of-the-art results across five benchmarks while being over 100 times smaller than recent MLLM-based methods.
DEViL offloads spatial grounding to a detector via a distilled reference-semantic token and temporal consistency regularization, reaching 43.1% m_vIoU at 14.33 FPS on HC-STVG.
F2G improves video temporal grounding accuracy by decoupling event identification from boundary measurement using predictive temporal perception to create citable evidence segments for LLM reasoning.
A controlled study on compact video LLMs finds that continuous temporal decoding delivers the strongest accuracy-efficiency trade-off for video temporal grounding across three benchmarks.
TempR1 applies temporal-aware multi-task RL using GRPO and three types of localization rewards to achieve SOTA temporal understanding in MLLMs with synergistic gains from joint optimization.
InternVideo2.5 improves video MLLMs by incorporating dense vision task annotations via direct preference optimization and compact spatiotemporal representations via adaptive hierarchical token compression, yielding better benchmark performance, 6x longer video memory, and new capabilities likeobject
citing papers explorer
-
EVIDENT: Routing MLLM Adaptation through Entity-Grounded Visual Evidence for Cross-Domain Video Temporal Grounding
EVIDENT routes MLLM adaptation for video temporal grounding through entity-grounded visual evidence using an Entity Bottleneck Adapter, Entity-Binding Distillation, and Entity-to-eVidence gating to improve cross-domain robustness.
-
EvoGround: Self-Evolving Video Agents for Video Temporal Grounding
A proposer-solver agent pair achieves supervised-level video temporal grounding and fine-grained captioning from 2.5K unlabeled videos via self-reinforcing evolution.
-
OmniVTG: A Large-Scale Dataset and Training Paradigm for Open-World Video Temporal Grounding
OmniVTG creates a new large-scale open-world VTG dataset using iterative concept-gap filling and timestamped captioning, paired with a three-stage self-correction CoT paradigm that yields SOTA zero-shot results on four existing benchmarks.
-
Bridging Time and Space: Decoupled Spatio-Temporal Alignment for Video Grounding
Bridge-STG decouples spatio-temporal alignment via semantic bridging and query-guided localization modules to achieve state-of-the-art m_vIoU of 34.3 on VidSTG among MLLM methods.
-
A Paradigm Shift: Fully End-to-End Training for Temporal Sentence Grounding in Videos
Fully end-to-end training with a sentence-conditioned adapter outperforms frozen-backbone baselines for localizing video segments that match sentence queries.
-
Temporal-Aware Reasoning Optimization for Video Temporal Grounding
TaRO improves video temporal grounding in MLLMs via constructive reasoning exploration from dense captions and a temporal-sensitivity reward that uses logit drops on disrupted event boundaries, followed by curriculum learning to SOTA results.
-
Don't Pause: Streaming Video-Language Synchrony for Online Video Understanding
LyraV uses FDTC and SToP for per-frame incremental decoding to reach 98.29% video synchrony at 3.89 FPS while preserving general understanding.
-
DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models
DRIFT adapts pretrained VLMs to continuous decoding via a base predictor plus residual flow matching, outperforming regression and generative baselines on grounding and robotic control tasks.
-
MarkIt: Training-Free Visual Markers for Precise Video Temporal Grounding
MarkIt converts videos into query-conditioned marked versions via a linguistic-parsing and open-vocabulary segmentation bridge that embeds instance masks, semantic markers, and frame indices to improve Vid-LLM temporal grounding.
-
ViLL-E: Video LLM Embeddings for Retrieval
ViLL-E introduces a dynamic embedding mechanism and joint contrastive-generative training for VideoLLMs, delivering up to 7% gains in temporal localization and 4% in video retrieval while enabling new zero-shot capabilities.
-
UniversalVTG: A Universal and Lightweight Foundation Model for Video Temporal Grounding
UniversalVTG is a lightweight foundation model for video temporal grounding that achieves state-of-the-art results across five benchmarks while being over 100 times smaller than recent MLLM-based methods.
-
Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding
DEViL offloads spatial grounding to a detector via a distilled reference-semantic token and temporal consistency regularization, reaching 43.1% m_vIoU at 14.33 FPS on HC-STVG.
-
Foresee-to-Ground: From Predictive Temporal Perception to Evidence-Driven Reasoning for Video Temporal Grounding
F2G improves video temporal grounding accuracy by decoupling event identification from boundary measurement using predictive temporal perception to create citable evidence segments for LLM reasoning.
-
How Should Video LLMs Output Time? An Analysis of Efficient Temporal Grounding Paradigms
A controlled study on compact video LLMs finds that continuous temporal decoding delivers the strongest accuracy-efficiency trade-off for video temporal grounding across three benchmarks.
-
TempR1: Improving Temporal Understanding of MLLMs via Temporal-Aware Multi-Task Reinforcement Learning
TempR1 applies temporal-aware multi-task RL using GRPO and three types of localization rewards to achieve SOTA temporal understanding in MLLMs with synergistic gains from joint optimization.
-
InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling
InternVideo2.5 improves video MLLMs by incorporating dense vision task annotations via direct preference optimization and compact spatiotemporal representations via adaptive hierarchical token compression, yielding better benchmark performance, 6x longer video memory, and new capabilities likeobject
- Multimodal Large Language Model-Enabled Video Translation: A Role-Oriented Survey