REVIEW 14 cited by
KeyVideoLLM: Towards Large-scale Video Keyframe Selection
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
KeyVideoLLM: Towards Large-scale Video Keyframe Selection
read the original abstract
Recently, with the rise of web videos, managing and understanding large-scale video datasets has become increasingly important. Video Large Language Models (VideoLLMs) have emerged in recent years due to their strong video understanding capabilities. However, training and inference processes for VideoLLMs demand vast amounts of data, presenting significant challenges to data management, particularly regarding efficiency, robustness, and effectiveness. In this work, we present KeyVideoLLM, a text-video frame similarity-based keyframe selection method designed to manage VideoLLM data efficiently, robustly, and effectively. Specifically, KeyVideoLLM achieves a remarkable data compression rate of up to 60.9 times, substantially lowering disk space requirements, which proves its high efficiency. Additionally, it maintains a 100% selection success rate across all video formats and scales, enhances processing speed by up to 200 times compared to existing keyframe selection methods, and does not require hyperparameter tuning. Beyond its outstanding efficiency and robustness, KeyVideoLLM further improves model performance in video question-answering tasks during both training and inference stages. Notably, it consistently achieved the state-of-the-art (SoTA) experimental results on diverse datasets.
Forward citations
Cited by 14 Pith papers
-
Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs
VISE is the first benchmark for sycophancy in Video-LLMs, with two training-free mitigation strategies based on key-frame selection and internal representation steering.
-
Reasoning with Memory: A Temporal Granularity-Adaptive Framework for Training-Free Long Video Understanding
ReMem improves zero-shot long-video QA by combining LLM-based temporal granularity parsing, CLIP-based dual-semantic frame scoring, and structure-aware dynamic frame routing.
-
Q-Fold: Query-Aware Focus-Context Spatio-Temporal Folding for Long Video Understanding
Q-Fold is a query-aware spatio-temporal folding technique that constructs heterogeneous focus-context inputs from long videos to improve Video-MLLM performance under fixed visual budgets.
-
AdaCodec: A Predictive Visual Code for Video MLLMs
AdaCodec introduces a predictive visual code that cuts visual token use in video MLLMs by sending full frames only on high predictive cost and otherwise encoding inter-frame changes as P-tokens, yielding better benchm...
-
PEEK: Picking Essential frames via Efficient Knowledge distillation
PEEK distills caption-conditioned frame relevance into a lightweight visual model, outperforming adaptive baselines on ActivityNet Captions and MSR-VTT especially at 1-2 frame budgets while adding only 5.2% overhead.
-
Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs
MS-SFNN builds PDE solutions from element-wise products of outputs from d independent fixed-random-weight subnetworks with tunable scaling and cosine activations, then solves coefficients by least squares, claiming su...
-
LDDR: Linear-DPP-Based Dynamic-Resolution Frame Sampling for Video MLLMs
LDDR proposes a linear DPP-based dynamic-resolution frame sampler that achieves 3x speedup and up to 2.5-point gains on video MLLM benchmarks by selecting non-redundant frames and allocating tokens accordingly.
-
VisualRouter: Query-Grounded Visual Sampling for Long Video Understanding
Query-type routing plus hybrid relevance-coverage and event-aware DPP sampling beats uniform and other training-free frame selectors on long-video benchmarks.
-
QCA: Query- and Content-Aware Keyframe Selection for Long Video Understanding
QCA selects compact, query-relevant keyframes from long videos via segment-wise budget allocation and diversity-aware addition, achieving higher accuracy than GPT-4o on LongVideoBench with half the frames.
-
Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs
MS-SFNN encodes multi-scale Fourier features in a separable product of fixed-weight cosine subnetworks and solves for linear coefficients by least squares, claiming better accuracy than PINN and SV-SNN on high-frequency PDEs.
-
Multi-Scale Separable Fourier Neural Networks for Solving High-Frequency PDEs
MS-SFNN encodes multi-scale Fourier features in a separable product of 1D cosine subnetworks and solves high-frequency PDEs via least-squares basis coefficients, claiming better accuracy than PINN and SV-SNN.
-
Efficient Inference for Large Vision-Language Models: Bottlenecks, Techniques, and Prospects
A survey that taxonomizes efficiency methods for LVLMs across the full inference pipeline, decouples the problem into information density, long-context attention, and memory limits, and outlines four future research f...
-
OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
OpenWorldLib defines world models as perception-centered systems with interaction and long-term memory, and provides a modular inference codebase unifying interactive video, 3D, reasoning, and VLA tasks.
-
OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
OpenWorldLib offers a standardized codebase and definition for world models that combine perception, interaction, and memory to understand and predict the world.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.