arXiv:2607. 25266v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have enabled long-form video understanding at a scale that was not previously possible.
arXiv:2607. 00446v1 Announce Type: cross Abstract: As video corpora continue to expand in both scale and task complexity, there is increasing demand for approaches that retrieve relevant videos from large-scale corpora (inter-video reasoning) and subsequently perform fine-grained, query-conditioned tasks (intra-video reasoning) within the retrieved content, such as temporal grounding.
arXiv:2609.37426v1 Announce Type: cross Abstract: Modern vision-language models (VLMs) have shown promising results in long-video understanding due to the rich semantic information they can capture....
The paper introduces CoVR‑R, a reason‑aware composed video retrieval system that, given a reference video and an edit instruction, retrieves a target video that satisfies the edit. It employs a zero‑shot reason‑then‑retrieve pipeline using Qwen3.5‑27B to generate structured descriptions and dense embeddings for gallery videos, and performs edit reasoning on the query to produce a target‑video description used as the query embedding. The method combines dense retrieval with a TF‑IDF branch over generated texts, fusing the rankings with split‑specific weights, achieving state‑of‑the‑art retrieval metrics on both validation and blind test splits.
The paper describes the CERTH-ITI team’s approach to the TRECVID 2025 Ad-hoc Video Search (AVS) and Video Question Answering (VQA) tracks. For AVS, a two-stage framework uses foundation vision‑language models for initial retrieval and a Multimodal‑LLM for reranking, improving contextual matching without training. In VQA, an audio‑visual MLLM is fine‑tuned and enhanced with inference‑time scaling; answer generation employs majority voting over responses from random frame subsets, while multiple‑choice responses use mean‑pooling logits, yielding top performance on VQA metrics.
arXiv:2607. 24794v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) demonstrate superior generalization in fundamental video tasks, restricted context windows limit their long video understanding.
arXiv:2606. 00910v1 Announce Type: cross Abstract: Composed Video Retrieval (CoVR) seeks the target video that results from applying a free-form textual modification to a reference video.
The paper introduces OTFS, a principled optimal transport framework for selecting frames in long-video understanding. It formulates frame selection as a structured evidence-allocation problem, using a semi‑unbalanced entropic optimal transport objective and an efficient OTFS‑Sinkhorn solver, followed by a dynamic‑programming extraction step. Experiments on three benchmarks show OTFS consistently outperforms strong training‑free baselines.
arXiv:2608.05707v2 Announce Type: replace Abstract: Frame selection is essential for applying Large Multimodal Models (LMMs) to long videos due to severe frame redundancy and limited context windows....
arXiv:2606. 19627v1 Announce Type: cross Abstract: The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds.
We introduce Predictive State Retrieval (PSR), a task in which a model observes a short video prefix and a temporal question about an object's future state, then retrieves instances from other videos or images that depict that state. Unlike action anticipation, which predicts a label, moment retrieval, which localizes an observed event within a video, or video generation, which synthesizes pixels, PSR combines anticipation with cross-instance retrieval across multiple temporal horizons.
ShotFinder introduces a new benchmark for open‑domain video shot retrieval, formalizing editing requirements as keyframe‑oriented shot descriptions and adding five controllable constraints—temporal order, color, visual style, audio, and resolution. The benchmark comprises 1,210 high‑quality YouTube samples across 20 themes, generated with large models and verified by humans. A three‑stage retrieval pipeline—query expansion via video imagination, candidate video retrieval, and description‑guided shot localization—shows a notable performance gap to humans, especially for color and visual style constraints.