SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation

• URL: http://arxiv.org/abs/2504.05925v1
• Date: Tue, 08 Apr 2025 11:31:37 GMT
• Title: SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation
• Authors: Hao Du, Bo Wu, Yan Lu, Zhendong Mao,
• Abstract summary: Vision-language temporal alignment is a crucial capability for human dynamic recognition and cognition in real-world scenarios.<n>We introduce SVLTA, the Synthetic Vision-Language Temporal Alignment derived via a well-designed and feasible control generation method within a simulation environment.<n>Our experiments reveal diagnostic insights through the evaluations in temporal question answering, distributional shift sensitiveness, and temporal alignment adaptation.
• Score: 33.02002580363215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-language temporal alignment is a crucial capability for human dynamic recognition and cognition in real-world scenarios. While existing research focuses on capturing vision-language relevance, it faces limitations due to biased temporal distributions, imprecise annotations, and insufficient compositionally. To achieve fair evaluation and comprehensive exploration, our objective is to investigate and evaluate the ability of models to achieve alignment from a temporal perspective, specifically focusing on their capacity to synchronize visual scenarios with linguistic context in a temporally coherent manner. As a preliminary step, we present the statistical analysis of existing benchmarks and reveal the existing challenges from a decomposed perspective. To this end, we introduce SVLTA, the Synthetic Vision-Language Temporal Alignment derived via a well-designed and feasible control generation method within a simulation environment. The approach considers commonsense knowledge, manipulable action, and constrained filtering, which generates reasonable, diverse, and balanced data distributions for diagnostic evaluations. Our experiments reveal diagnostic insights through the evaluations in temporal question answering, distributional shift sensitiveness, and temporal alignment adaptation.

Related papers

• Beyond Semantics: Rediscovering Spatial Awareness in Vision-Language Models [10.792834356227118]
  Vision-Language Models (VLMs) excel at identifying and describing objects but struggle with spatial reasoning. Inspired by the dual-pathway (ventral-dorsal) model of human vision, we investigate why VLMs fail spatial tasks despite strong object recognition capabilities.
  arXiv  Detail & Related papers  (2025-03-21T17:51:14Z)
• FutureVision: A methodology for the investigation of future cognition [0.5644620681963636]
  We conduct a pilot study examining how visual fixation patterns vary during the evaluation of futuristic scenarios.<n>Preliminary results show that far-future and pessimistic scenarios are associated with longer fixations and more erratic saccades.
  arXiv  Detail & Related papers  (2025-02-03T18:29:06Z)
• Detecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives [84.03001845263]
  Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management.<n>Traditional narrative analysis often focuses on local indicators in microstructure, such as word usage and syntax.<n>We propose to investigate specific cognitive and linguistic challenges by analyzing topical shifts, temporal dynamics, and the coherence of narratives over time.
  arXiv  Detail & Related papers  (2025-01-07T12:16:26Z)
• Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability [20.057227484862523]
  Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in processing both visual and textual information.<n>This survey presents a comprehensive examination of alignment and misalignment in LVLMs through an explainability lens.
  arXiv  Detail & Related papers  (2025-01-02T16:53:50Z)
• Dynamic Cross-Modal Alignment for Robust Semantic Location Prediction [0.0]
  This paper introduces textitContextualized Vision-Language Alignment (CoVLA), a discriminative framework designed to address the challenges of contextual ambiguity and modality discrepancy inherent in this task.<n>Experiments on a benchmark dataset demonstrate that CoVLA significantly outperforms state-of-the-art methods, achieving improvements of 2.3% in accuracy and 2.5% in F1-score.
  arXiv  Detail & Related papers  (2024-12-13T05:29:37Z)
• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Reframing Spatial Reasoning Evaluation in Language Models: A Real-World Simulation Benchmark for Qualitative Reasoning [4.422649561583363]
  We present a novel benchmark for assessing spatial reasoning in language models (LMs) It is grounded in realistic 3D simulation data, offering a series of diverse room layouts with various objects and their spatial relationships. A key contribution is our logic-based consistency-checking tool, which enables the assessment of multiple plausible solutions.
  arXiv  Detail & Related papers  (2024-05-23T21:22:00Z)
• Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions [75.45274978665684]
  Vision-Language Understanding (VLU) benchmarks contain samples where answers rely on assumptions unsupported by the provided context. We collect contextual data for each sample whenever available and train a context selection module to facilitate evidence-based model predictions. We develop a general-purpose Context-AwaRe Abstention detector to identify samples lacking sufficient context and enhance model accuracy.
  arXiv  Detail & Related papers  (2024-05-18T02:21:32Z)
• VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models [57.43276586087863]
  Large Vision-Language Models (LVLMs) suffer from hallucination issues, wherein the models generate plausible-sounding but factually incorrect outputs. Existing benchmarks are often limited in scope, focusing mainly on object hallucinations. We introduce a multi-dimensional benchmark covering objects, attributes, and relations, with challenging images selected based on associative biases.
  arXiv  Detail & Related papers  (2024-04-22T04:49:22Z)
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [84.42837346400151]
  Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare. Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions. We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
  arXiv  Detail & Related papers  (2022-06-16T17:15:15Z)
• Stateful Offline Contextual Policy Evaluation and Learning [88.9134799076718]
  We study off-policy evaluation and learning from sequential data. We formalize the relevant causal structure of problems such as dynamic personalized pricing. We show improved out-of-sample policy performance in this class of relevant problems.
  arXiv  Detail & Related papers  (2021-10-19T16:15:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.