arXiv Computer Vision
Finding Change in Satellite Archives from Text: How to Combine Before-and-After Images Efficiently
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arXiv Computation and Language
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Select, Compress, Reinvest: A Controlled Study of Visual-Token Allocation in Long-Video MLLMs
The study investigates how long‑video language models decide which frames to keep, compress, and reuse, testing each decision in isolation across six selection rules, three benchmarks, and two answering models. It finds that selecting frames based on queries yields the biggest performance boost, that halving spatial resolution costs little, and that reallocating saved tokens to more compressed frames can further improve accuracy. The work also highlights the importance of a unified evaluation harness to avoid misleading comparisons.
arXiv Computer Vision
Sep 22
Retrieval Geometry Shapes Cache-Based Clip Adaptation
The paper investigates how the choice of retrieval encoder affects cache‑based test‑time adaptation for CLIP. By keeping the memory fixed and varying the retrieval space across sixteen encoders, the authors show that retrieval space can dramatically alter performance, with gains ranging from +0.44 to +19.7 points on ImageNet‑A. They introduce MARC, a training‑free system that pairs frozen CLIP with DINOv2‑B for retrieval, achieving superior out‑of‑distribution accuracy and efficiency compared to prior methods.
Hugging Face Trending Papers
Sep 20
Retrieval Geometry Shapes Cache-Based Clip Adaptation
Cache-based test-time adaptation improves CLIP predictions by storing and retrieving examples from the target stream while keeping the model frozen. However, existing methods largely treat the feature...
arXiv AI
Jun 12
Rethinking RAG in Long Videos: What to Retrieve and How to Use It?
arXiv:2606. 13141v1 Announce Type: new Abstract: Retrieval-augmented generation is moving beyond text into long, egocentric video, where systems must select query-relevant chunks across multiple modalities and temporal granularities.
arXiv Computer Vision
Oct 1
DeCoPrune: Efficient KV-Cache Pruning for Autoregressive Video Diffusion via Denoising Consistency
arXiv:2609.39096v1 Announce Type: new Abstract: Autoregressive video diffusion supports streaming generation and interactive control, but its KV cache grows with the generated history. Existing compr...
arXiv AI
1d ago
RECAP: Context-Aware Prediction Recycling with Request-Local and Persistent Memories for Multimodal Speculative Decoding
The paper introduces RECAP, a method that improves speculative decoding for vision‑language models by recycling a target model’s own predictions. RECAP stores predictions using one‑, two‑, and three‑token keys, allowing contextual memories that are request‑specific and token‑level memories that persist across requests. By fusing retrieved predictions based on entropy‑confidence weighting and using best‑first search to build a candidate tree, RECAP achieves significant speedups over autoregressive decoding without requiring training or a draft model.