Wenming Yang

Zhehan Kan, Yanlin Liu, Kun Yin et al. · tencent-ai

DeepSeek R1 has significantly advanced complex reasoning for large language models (LLMs). While recent methods have attempted to replicate R1's reasoning capabilities in multimodal settings, they face limitations, including inconsistencies between reasoning and final answers, model instability and crashes during long-chain exploration, and low data learning efficiency. To address these challenges, we propose TACO, a novel reinforcement learning algorithm for visual reasoning. Building on Generalized Reinforcement Policy Optimization (GRPO), TACO introduces Think-Answer Consistency, which tightly couples reasoning with answer consistency to ensure answers are grounded in thoughtful reasoning. We also introduce the Rollback Resample Strategy, which adaptively removes problematic samples and reintroduces them to the sampler, enabling stable long-chain exploration and future learning opportunities. Additionally, TACO employs an adaptive learning schedule that focuses on moderate difficulty samples to optimize data efficiency. Furthermore, we propose the Test-Time-Resolution-Scaling scheme to address performance degradation due to varying resolutions during reasoning while balancing computational overhead. Extensive experiments on in-distribution and out-of-distribution benchmarks for REC and VQA tasks show that fine-tuning LVLMs leads to significant performance improvements.

Sinchee Chin, Fan Zhang, Xiaochen Yang et al.

Time Series Anomaly Detection (TSAD) is essential for uncovering rare and potentially harmful events in unlabeled time series data. Existing methods are highly dependent on clean, high-quality inputs, making them susceptible to noise and real-world imperfections. Additionally, intricate temporal relationships in time series data are often inadequately captured in traditional 1D representations, leading to suboptimal modeling of dependencies. We introduce VISTA, a training-free, unsupervised TSAD algorithm designed to overcome these challenges. VISTA features three core modules: 1) Time Series Decomposition using Seasonal and Trend Decomposition via Loess (STL) to decompose noisy time series into trend, seasonal, and residual components; 2) Temporal Self-Attention, which transforms 1D time series into 2D temporal correlation matrices for richer dependency modeling and anomaly detection; and 3) Multivariate Temporal Aggregation, which uses a pretrained feature extractor to integrate cross-variable information into a unified, memory-efficient representation. VISTA's training-free approach enables rapid deployment and easy hyperparameter tuning, making it suitable for industrial applications. It achieves state-of-the-art performance on five multivariate TSAD benchmarks.

Junhao Gu, Peng-Tao Jiang, Hao Zhang et al.

Recent methods exploit the powerful text-to-image (T2I) diffusion models for real-world image super-resolution (Real-ISR) and achieve impressive results compared to previous models. However, we observe two kinds of inconsistencies in diffusion-based methods which hinder existing models from fully exploiting diffusion priors. The first is the semantic inconsistency arising from diffusion guidance. T2I generation focuses on semantic-level consistency with text prompts, while Real-ISR emphasizes pixel-level reconstruction from low-quality (LQ) images, necessitating more detailed semantic guidance from LQ inputs. The second is the training-inference inconsistency stemming from the DDPM, which improperly assumes high-quality (HQ) latent corrupted by Gaussian noise as denoising inputs for each timestep. To address these issues, we introduce ConsisSR to handle both semantic and training-inference consistencies. On the one hand, to address the semantic inconsistency, we proposed a Hybrid Prompt Adapter (HPA). Instead of text prompts with coarse-grained classification information, we leverage the more powerful CLIP image embeddings to explore additional color and texture guidance. On the other hand, we introduce Time-Aware Latent Augmentation (TALA) to bridge the training-inference inconsistency. Based on the probability function p(t), we accordingly enhance the SDSR training strategy. With LQ latent with Gaussian noise as inputs, our TALA not only focuses on diffusion noise but also refine the LQ latent towards the HQ counterpart. Our method demonstrates state-of-the-art performance among existing diffusion models. The code will be made publicly available.

Juncheng Zhang, Qingmin Liao, Shaojun Liu et al.

Multi-focus image fusion, a technique to generate an all-in-focus image from two or more partially-focused source images, can benefit many computer vision tasks. However, currently there is no large and realistic dataset to perform convincing evaluation and comparison of algorithms in multi-focus image fusion. Moreover, it is difficult to train a deep neural network for multi-focus image fusion without a suitable dataset. In this letter, we introduce a large and realistic multi-focus dataset called Real-MFF, which contains 710 pairs of source images with corresponding ground truth images. The dataset is generated by light field images, and both the source images and the ground truth images are realistic. To serve as both a well-established benchmark for existing multi-focus image fusion algorithms and an appropriate training dataset for future development of deep-learning-based methods, the dataset contains a variety of scenes, including buildings, plants, humans, shopping malls, squares and so on. We also evaluate 10 typical multi-focus algorithms on this dataset for the purpose of illustration.