ACM MM 2026
PulseAugur coverage of ACM MM 2026 — every cluster mentioning ACM MM 2026 across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
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Prof. Wang Youqing's team paper accepted to ACM MM 2026
A research paper by Professor Wang Youqing's team from Beijing University of Chemical Technology has been accepted to the 34th ACM International Conference on Multimedia (ACM MM 2026). The paper introduces a novel frame…
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New CoReFuse-Med method improves multi-modal medical image segmentation
Researchers from Central China Normal University have developed CoReFuse-Med, a novel method for multi-modal medical image segmentation. This approach addresses the issue of low-quality modalities negatively impacting t…
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New RASG framework enhances emotion recognition using listener-only data
Researchers have developed a new framework called RASG for recognizing interlocutor emotions, which focuses on predicting listener emotions using only listener videos and speaker audios. The framework incorporates Role-…
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MSU team uses Qwen3-VL models for advanced document forgery detection
Researchers from Mississippi State University have developed a novel pipeline for detecting and analyzing text forgeries in documents, which goes beyond simple pixel manipulation to identify semantic alterations. Their …
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Xiaomi MiLM Plus releases PROVE benchmark for video object removal
Xiaomi's MiLM Plus has introduced PROVE, a new benchmark and set of metrics designed to evaluate video object removal models more effectively. Traditional metrics like PSNR and SSIM struggle with the inherently ill-pose…
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SEED system offers explainable detection for AI-generated text forgeries
Researchers have developed SEED, a system designed to detect and explain AI-generated text forgeries. This system, which ranked third in the GenText-Forensics Challenge at ACM MM 2026, utilizes a Vision Transformer (ViT…
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Robots' spatial misrepresentation, not physics, hinders generalization: new research
Researchers from Sun Yat-sen University and X-Era AI Lab have identified that the primary cause of failure in robots operating in new environments is not a lack of physical understanding, but rather misaligned spatial r…