UNWIND is a facial‑video framework that detects stress by treating an entire recording as a single input, avoiding the need for temporal windowing or segmentation. It folds the video’s temporal dimension into the channel dimension of a 2‑D spatial representation and processes it with an asymmetric‑attention architecture. Experiments on a 58‑subject stress dataset show that using all 3,600 frames (stride τ = 1) yields a 69.73 % accuracy, comparable to the best 70.02 % accuracy at τ = 15, while computational cost varies from 12.48 to 348.78 GFLOPs.
arXiv:2608. 10442v1 Announce Type: cross Abstract: Automatic stress detection from facial video offers a practical path to non-intrusive affect monitoring, yet existing video-based approaches commonly decompose full recordings into short temporal windows before classification.
arXiv:2606. 25177v1 Announce Type: new Abstract: Cognitive workload monitoring is important for adaptive rehabilitation and assistive interfaces, where task difficulty, pacing, and feedback should be adjusted according to the user's cognitive state to avoid overload and under-challenge.
arXiv:2606. 02522v1 Announce Type: cross Abstract: Video multimodal large language models (MLLMs) have made rapid progress on general and long-form video understanding, yet their ability to preserve brief answer-critical visual evidence remains underexplored.
arXiv:2607. 16760v1 Announce Type: cross Abstract: Driver monitoring systems (DMS) increasingly rely on facial cues to infer drowsiness, distraction, and cognitive load in real time.
arXiv:2604. 15280v2 Announce Type: replace-cross Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans.
The paper presents Test-Time Adaptation via Cache Personalization (TTA‑CaP), a gradient‑free, cache‑based method that personalizes vision‑language models for facial expression recognition in videos. TTA‑CaP uses three complementary caches—a personalized static cache, a positive target cache, and a negative target cache—controlled by a tri‑gate mechanism to prevent corruption and provide robust subject‑matched evidence. Experiments on BioVid, StressID, and BAH datasets show that TTA‑CaP outperforms state‑of‑the‑art test‑time adaptation methods while keeping computational and memory overhead low.
arXiv:2608. 15999v1 Announce Type: new Abstract: Automatic emotion assessment can benefit from combining neural and behavioral signals, but many multimodal approaches rely on separate, modality-specific feature-extraction pipelines before fusion.
arXiv:2607. 12774v1 Announce Type: cross Abstract: This article presents our results for the 11th Affective Behavior Analysis in-the-Wild (ABAW) competition.
arXiv:2607. 24570v1 Announce Type: cross Abstract: Large-scale video platforms process millions of uploads hourly, requiring moderation systems that can localize when and where policy violations occur within each video.
LongVU‑TTT is a causal test‑time training method for long‑video multimodal large language models that inserts a convolutional resampler with fast‑weight updates between the vision encoder and the LLM. The fast weights adapt per video and contextualize frame features before compression, while a hybrid selector keeps explicit visual evidence for downstream reasoning. Experiments show that TTT‑Conv outperforms TTT‑MLP and bidirectional Mamba2 on MLVU, and beats attention‑ and fixed‑state recurrent resamplers on three benchmarks, achieving competitive results on five video‑understanding tasks after reducing 512 frames to 128 LLM frames.
arXiv:2609.37001v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) enable high-quality video generation but suffer from substantial inference latency, primarily attributable to the compu...
TT-VidT is a video pretraining method that decouples the temporal axis by combining a per‑frame ViT-B/16 spatial encoder with a compact Temporal Transfer Layer trained via Diff Compression. The authors conduct a systematic 24‑configuration study to isolate architecture, objective, and decoder effects, showing that the full TT-VidT design yields the strongest motion‑sensitive representations. In downstream fine‑tuning, TT‑VidT outperforms state‑of‑the‑art baselines on Jester, Something‑Something V2, ARID, and Diving48 while using significantly fewer encoder FLOPs.