B-VLLM: A Vision Large Language Model with Balanced Spatio-Temporal Tokens

Recently, Vision Large Language Models (VLLMs) integrated with vision encoders have shown promising performance in vision understanding. The key of VLLMs is to encode visual content into sequences of visual tokens, enabling VLLMs to simultaneously process both visual and textual content. However, understanding videos, especially long videos, remain a challenge to VLLMs as the number of visual tokens grows rapidly when encoding videos, resulting in the risk of exceeding the context window of VLLMs and introducing heavy computation burden. To restrict the number of visual tokens, existing VLLMs either: (1) uniformly downsample videos into a fixed number of frames or (2) reducing the number of visual tokens encoded from each frame. We argue the former solution neglects the rich temporal cue in videos and the later overlooks the spatial details in each frame. In this work, we present Balanced-VLLM (B-VLLM): a novel VLLM framework that aims to effectively leverage task relevant spatio-temporal cues while restricting the number of visual tokens under the VLLM context window length. At the core of our method, we devise a text-conditioned adaptive frame selection module to identify frames relevant to the visual understanding task. The selected frames are then de-duplicated using a temporal frame token merging technique. The visual tokens of the selected frames are processed through a spatial token sampling module and an optional spatial token merging strategy to achieve precise control over the token count. Experimental results show that B-VLLM is effective in balancing the number of frames and visual tokens in video understanding, yielding superior performance on various video understanding benchmarks. Our code is available at https://github.com/zhuqiangLu/B-VLLM.

DuoCast: Duo-Probabilistic Meteorology-Aware Model for Extended Precipitation Nowcasting

Recently, extended short-term precipitation nowcasting struggles with decreasing precision because of insufficient consideration of meteorological knowledge, such as weather fronts which significantly influence precipitation intensity, duration, and spatial distribution. Therefore, in this paper, we present DuoCast, a novel dual-probabilistic meteorology-aware model designed to address both broad weather evolution and micro-scale fluctuations using two diffusion models, PrecipFlow and MicroDynamic, respectively. Our PrecipFlow model captures evolution trends through an Extreme Precipitation-Aware Encoder (EPA-Encoder), which includes AirConvolution and FrontAttention blocks to process two levels of precipitation data: general and extreme. The output conditions a UNet-based diffusion to produce prediction maps enriched with weather front information. The MicroDynamic model further refines the results to capture micro-scale variability. Extensive experiments on four public benchmarks demonstrate the effectiveness of our DuoCast, achieving superior performance over state-of-the-art methods. Our code is available at https://github.com/ph-w2000/DuoCast.