6 accepted papers
Multimodal Large Language Models (MLLMs) have garnered significant attention recently and demonstrate outstanding capabilities in various tasks such as OCR, VQA, captioning, etc. However, hallucination remains a persistent issue. While numerous methods have been proposed to mitigate hallucinations,
Action understanding matters for intelligent agents and has attracted long-term attention. It can be formed as the mapping from the action physical space to the semantic space. Typically researchers built action datasets according to idiosyncratic choices to define classes and push the envelope of b…
Human reasoning can be understood as a cooperation between the intuitive, associative "System-1'' and the deliberative, logical "System-2''. For existing System-1-like methods in visual activity understanding, it is crucial to integrate System-2 processing to improve explainability, generalization,…
Human-Object Interaction (HOI) detection plays a core role in activity understanding. Though recent two/one-stage methods have achieved impressive results, as an essential step, discovering interactive human-object pairs remains challenging. Both one/two-stage methods fail to effectively extract int…
"Human-Object Interaction (HOI) detection plays a crucial role in activity understanding. Though significant progress has been made, interactiveness learning remains a challenging problem in HOI detection: existing methods usually generate redundant negative H-O pair proposals and fail to effectivel…
Human-Object Interaction (HOI) consists of human, object and implicit interaction/verb. Different from previous methods that directly map pixels to HOI semantics, we propose a novel perspective for HOI learning in an analytical manner. In analogy to Harmonic Analysis, whose goal is to study how to r…