arXiv:2606. 09261v2 Announce Type: replace Abstract: In this paper, we present XInsight Lab's solution to the micro-gesture classification track of the 4th MiGA Challenge at IJCAI 2026, in which our solution ranked first and achieved a new state-of-the-art result.
arXiv:2606.07355v2 Announce Type: replace Abstract: Micro-gesture online recognition aims to temporally localize and classify subtle gestures in untrimmed videos. Owing to their extremely short durat...
arXiv:2607. 16322v1 Announce Type: cross Abstract: Micro-gesture recognition demands the detection of fleeting, spatially localized movements that are frequently overwhelmed by dominant static appearances and background noise.
arXiv:2609.25582v1 Announce Type: new Abstract: Public surface electromyography (EMG) datasets vary widely in electrode layout, channel count, frequency support, and size. Simply mixing them for pret...
MyoFlow introduces a discriminative flow-matching framework for high‑density surface electromyography (HD‑sEMG) gesture recognition that addresses distribution shifts caused by electrode re‑donning and physiological variability. By using a domain‑conditioned rectified flow to transport encoded windows toward gesture anchors, the method enables zero‑shot prediction without a separate classifier head. On the Hyser dataset, MyoFlow outperforms the strongest diffusion‑based baseline by 4.24 % in cross‑session accuracy and 6.37 % in cross‑subject accuracy, and achieves 91.71 % mean zero‑shot accuracy and 97.39 % mean few‑shot accuracy on the CEMHSEY dataset.
arXiv:2605. 01720v3 Announce Type: replace-cross Abstract: Existing large-scale sign language resources typically provide supervision only at the level of raw video-text alignment and are often produced in laboratory settings.
arXiv:2607. 03657v1 Announce Type: cross Abstract: Gloss-free Sign Language Translation (SLT) translates sign language videos into spoken-language sentences without gloss annotations, avoiding costly labeling but requiring fine-grained modeling of hands, body, and facial cues.
arXiv:2607. 22779v1 Announce Type: cross Abstract: Hand gesture recognition via surface electromyography (sEMG) is fundamental to prosthetic control.
arXiv:2606. 11925v1 Announce Type: cross Abstract: Sign language translation (SLT) converts sign language video into spoken language text and holds significant promise for improving accessibility and enabling communication between signing and non-signing communities.
SMART is a new framework that jointly tackles continuous sign language recognition (CSLR) and spotting by leveraging a multimodal large language model (MLLM) to generate motion descriptions as auxiliary semantic cues. It performs stable video‑text alignment with small batch sizes and introduces a Multi‑Scale Temporal Adapter to capture temporal interactions during transformer encoding. The framework also incorporates CSFormer, a CSLR‑guided spotting module that injects recognition‑derived gloss evidence into a boundary‑aware spotting network, enabling mutual benefit between recognition and spotting tasks.
arXiv:2503. 07825v3 Announce Type: replace-cross Abstract: We present an advance in wearable technology: a mobile-optimized, real-time, ultra-low-power event camera system that enables natural hand gesture control for smart glasses, dramatically improving user experience.
arXiv:2608. 06407v1 Announce Type: cross Abstract: Automated Sign Language Recognition for under-represented languages remains a largely unsolved problem.
arXiv:2608. 07192v1 Announce Type: new Abstract: Low-resolution infrared (IR) array sensors represent an interesting solution for privacy-preserving human sensing in embedded systems.