14 citations · 47 across the 16 of their papers we have counts for
21 papers
HumanMoveVQA: Can Video MLLMs reason about human movement in videos?
Pulkit Gera, Faegheh Sardari, Asmar Nadeem +4
Despite the rapid advance of Multimodal Large Language Models (MLLMs) in high-level video understanding, a fundamental bottleneck remains: these models collapse complex human motio…
Reframing Dense Action Detection (RefDense): A Paradigm Shift in Problem Solving & a Novel Optimization Strategy
Faegheh Sardari, Armin Mustafa, Philip J. B. Jackson +1
Dense action detection involves detecting multiple co-occurring actions while action classes are often ambiguous and represent overlapping concepts. We argue that handling the dual…
An Effective-Efficient Approach for Dense Multi-Label Action Detection
Faegheh Sardari, Armin Mustafa, Philip J. B. Jackson +1
Unlike the sparse label action detection task, where a single action occurs in each timestamp of a video, in a dense multi-label scenario, actions can overlap. To address this chal…
NarrativeBridge: Enhancing Video Captioning with Causal-Temporal Narrative
Asmar Nadeem, Faegheh Sardari, Robert Dawes +3
Existing video captioning benchmarks and models lack causal-temporal narrative, which is sequences of events linked through cause and effect, unfolding over time and driven by char…
CoLeaF: A Contrastive-Collaborative Learning Framework for Weakly Supervised Audio-Visual Video Parsing
Faegheh Sardari, Armin Mustafa, Philip J. B. Jackson +1
Weakly supervised audio-visual video parsing (AVVP) methods aim to detect audible-only, visible-only, and audible-visible events using only video-level labels. Existing approaches…
CAD -- Contextual Multi-modal Alignment for Dynamic AVQA
Asmar Nadeem, Adrian Hilton, Robert Dawes +2
In the context of Audio Visual Question Answering (AVQA) tasks, the audio visual modalities could be learnt on three levels: 1) Spatial, 2) Temporal, and 3) Semantic. Existing AVQA…