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Abstract: All-in-one image restoration aims to recover high-quality images from multiple degradation types using a single unified model. Recent methods improve unified restoration with hybrid attention, prompt learning, or routing mechanisms, but generally focus on enhancing degradation adaptivity while employing similar feature modeling strategies across network stages. Consequently, the distinct representation requirements of encoding and decoding under diverse degradations remain insufficiently explored. In this paper, we propose SASFormer, a Stage-Aware Selective Attention Transformer for all-in-one blind image restoration. SASFormer follows a stage-aware design principle: the encoder uses established hybrid channel-spatial attention to build robust content-preserving and degradation-aware representations, while the decoder employs adaptive selective attention for restoration-oriented reconstruction. To address the stage-specific requirements of decoder reconstruction, we develop Adaptive Selective Channel Self-Attention (ASCSA) and Adaptive Selective Spatial Self-Attention (ASSSA), enabling the decoder to dynamically identify restoration-relevant channel responses and spatial contexts. Specifically, ASCSA performs adaptive query-channel selection and attention masking, while ASSSA adaptively integrates spatial attention across multiple window sizes. Compact implicit prompts further modulate the selection process without requiring explicit degradation labels. Extensive experiments on 3-, 5-, and 11-degradation settings demonstrate that SASFormer achieves strong restoration performance with favorable parameter efficiency. Moreover, stage-wise representation analysis and ablation studies provide empirical support for the proposed stage-aware design and the effectiveness of decoder-side adaptive selection.

Abstract: Keyframe selection (KFS) has long produced compact video summaries for browsing and retrieval, and representative frames for thumbnails. More recently, when conditioned on a question, KFS provides an alternative to uniform sampling for long-video question answering by selecting frames that are more relevant to the question. Most methods rank frames by similarity to the question. Yet a relevant frame may score poorly when the question combines subjects or moments that no single frame shows, or requires implicit information absent from its wording. Other methods try to break down the question into subqueries, but suffer from inaccurate decomposition due to their static initial context. Therefore, we propose CueKFS, a training-free method that reformulates question--frame matching as comparing frames against a set of dynamically generated visual cues. From an initial set of salient frames, we decompose the question into cues. Each cue concurrently probes the video to navigate to its own evidence. A reasoning VLM then agentically revises the cue set against its evidence to re-explore the video. CueKFS then allocates the budget across the surviving cues. Across three benchmarks, CueKFS establishes state-of-the-art results in all 27 evaluated settings with available prior results, achieving budget-averaged gains of up to +4.54% over the previous baseline and a median of only two VLM calls. We further provide a detailed behavioral analysis of CueKFS, showing that agentic cue refinement drives active re-exploration of the video, yielding relative similarity gains of up to 92% over the initial context.