ReFoCUS: Reinforcement-guided Frame Optimization for Contextual
  Understanding
Recent progress in Large Multi-modal Models (LMMs) has enabled effective
vision-language reasoning, yet the ability to understand video content remains
constrained by suboptimal frame selection strategies. Existing approaches often
rely on static heuristics or external retrieval modules to feed frame
information into video-LLMs, which may fail to provide the query-relevant
information. In this work, we introduce ReFoCUS (Reinforcement-guided Frame
Optimization for Contextual UnderStanding), a novel frame-level policy
optimization framework that shifts the optimization target from textual
responses to visual input selection. ReFoCUS learns a frame selection policy
via reinforcement learning, using reward signals derived from a reference LMM
to reflect the model's intrinsic preferences for frames that best support
temporally grounded responses. To efficiently explore the large combinatorial
frame space, we employ an autoregressive, conditional selection architecture
that ensures temporal coherence while reducing complexity. Our approach does
not require explicit supervision at the frame-level and consistently improves
reasoning performance across multiple video QA benchmarks, highlighting the
benefits of aligning frame selection with model-internal utility.
683fe4caf8916dcd6d1c936a
Hosu Lee
claimed_verified
2025-06-04T08:54:26.062Z
673060959e631f353ae1b5e0
/avatars/d4b1e23de90ff1d02c38186a259b8d1e.svg
Hosu Lee
user
lakelee
683fe4caf8916dcd6d1c936b
Junho Kim
claimed_verified
2025-06-04T08:54:30.239Z
653238bed0f5a9e537ed966d
/avatars/19de93c8170837d8a8a61473f96617ea.svg
Junho Kim
user
arkimjh
683fe4caf8916dcd6d1c936c
Hyunjun Kim
683fe4caf8916dcd6d1c936d
Yong Man Ro
ReFoCUS: Reinforcement-guided Frame Optimization for Contextual
  Understanding
Recent progress in Large Multi-modal Models (LMMs) has enabled effective
vision-language reasoning, yet the ability to understand video content remains
constrained by suboptimal frame selection strategies. Existing approaches often
rely on static heuristics or external retrieval modules to feed frame
information into video-LLMs, which may fail to provide the query-relevant
information. In this work, we introduce ReFoCUS (Reinforcement-guided Frame
Optimization for Contextual UnderStanding), a novel frame-level policy
optimization framework that shifts the optimization target from textual
responses to visual input selection. ReFoCUS learns a frame selection policy
via reinforcement learning, using reward signals derived from a reference LMM
to reflect the model's intrinsic preferences for frames that best support
temporally grounded responses. To efficiently explore the large combinatorial
frame space, we employ an autoregressive, conditional selection architecture
that ensures temporal coherence while reducing complexity. Our approach does
not require explicit supervision at the frame-level and consistently improves
reasoning performance across multiple video QA benchmarks, highlighting the
benefits of aligning frame selection with model-internal utility.
ReFoCUS uses reinforcement learning to optimize frame selection for video-LLM, enhancing reasoning performance in video QA by aligning with model preferences.
Large Multi-modal Models vision-language reasoning frame selection strategies reinforcement learning frame selection policy reference LMM autoregressive architecture conditional selection architecture temporal coherence video QA benchmarks