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Found 10 papers, 8 papers with code
Kimi-VL Technical Report
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2. 8B parameters in its language decoder (Kimi-VL-A3B).
Spatial-R1: Enhancing MLLMs in Video Spatial Reasoning
Enhancing the spatial reasoning capabilities of Multi-modal Large Language Models (MLLMs) for video understanding is crucial yet challenging.
TEMPLE:Temporal Preference Learning of Video LLMs via Difficulty Scheduling and Pre-SFT Alignment
We further analyze the transferability of DPO data across architectures and the role of difficulty scheduling in optimization.
Generative Frame Sampler for Long Video Understanding
Despite recent advances in Video Large Language Models (VideoLLMs), effectively understanding long-form videos remains a significant challenge.
PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension
To provide a comprehensive evaluation, PunchBench incorporates diverse question formats and image-captions from various domains.
Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue
We then develop a module named Joint Cross Attention-based Sentiment Inference (JCA-SI) by extending the multimodal sentiment analysis model JCA to derive the joint sentiment label for each video-audio clip.
Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation
Multimodal Sarcasm Explanation (MuSE) is a new yet challenging task, which aims to generate a natural language sentence for a multimodal social post (an image as well as its caption) to explain why it contains sarcasm.
Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics
This framework consists of three parts: 1) a local feature extraction module to learn representations for each region; 2) a global context module to extract global contextual priors and upsample them to generate the global features; and 3) a region-specific predictor based on tensor decomposition to provide customized predictions for each region, which is very parameter-efficient compared to previous methods.
Fine-Grained Urban Flow Inference
To tackle these issues, we develop a model entitled UrbanFM which consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs that uses a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influence of different external factors.
UrbanFM: Inferring Fine-Grained Urban Flows
In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations.
Ranked #2 on
Fine-Grained Urban Flow Inference
on TaxiBJ-P4
