The aesthetic quality assessment task is crucial for developing a human-aligned quantitative evaluation system for AIGC. However, its inherently complex nature—spanning visual perception, cognition, and emotion—poses fundamental challenges. Although aesthetic descriptions offer a viable representation of this complexity, two critical challenges persist: (1) data scarcity and imbalance: existing dataset overly focuses on visual perception and neglects deeper dimensions due to the expensive manual annotation; and (2) model fragmentation: current visual networks isolate aesthetic attributes with multi-branch encoder, while multimodal methods represented by contrastive learning struggle to effectively process long-form textual descriptions. To resolve challenge (1), we first present the Refined Aesthetic Description (RAD) dataset, a large-scale (70k), multi-dimensional structured dataset, generated via an iterative pipeline without heavy annotation costs and easy to scale. To address challenge (2), we propose ArtQuant, an aesthetics assessment framework for artistic image which not only couple isolated aesthetic dimensions through joint description generation, but also better model long-text semantics with the help of LLM decoders. Besides, theoretical analysis confirms this symbiosis: RAD's semantic adequacy (data) and generation paradigm (model) collectively minimize prediction entropy, providing mathematical grounding for the framework. Our approach achieves state-of-the-art performance on several datasets while requiring only 33% of conventional training epochs, narrowing the cognitive gap between artistic image and aesthetic judgment. We will release both code and dataset to support future research.

Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Pre-trained video generation models hold great potential for generative video super-resolution (VSR). However, adapting them for full-size VSR, as most existing methods do, suffers from unnecessary intensive full-attention computation and fixed output resolution. To overcome these limitations, we make the first exploration into utilizing video diffusion priors for patch-wise VSR. This is non-trivial because pre-trained video diffusion models are not native for patch-level detail generation. To mitigate this challenge, we propose an innovative approach, called PatchVSR, which integrates a dual-stream adapter for conditional guidance. The patch branch extracts features from input patches to maintain content fidelity while the global branch extracts context features from the resized full video to bridge the generation gap caused by incomplete semantics of patches. Particularly, we also inject the patch’s location information into the model to better contextualize patch synthesis within the global video frame. Experiments demonstrate that our method can synthesize high-fidelity, high-resolution details at the patch level. A tailor-made multi-patch joint modulation is proposed to ensure visual consistency across individually enhanced patches. Due to the flexibility of our patch-based paradigm, we can achieve highly competitive 4K VSR based on a 512×512 resolution base model, with extremely high efficiency.

Multi-modal object tracking has received increasing attention, given the limitations the representation ability in certain challenging scenarios of single RGB modality. Recent prompt tuning techniques enable multimodal tracking to effectively inherit knowledge from foundation models trained with a large amount of RGB tracking data and achieve parameter-efficient training. However, few works focus on the efficient inference of multimodal tracking handling multiple RGB-X (RGB-Thermal, RGB-Depth, RGB-Event, etc.) tracking tasks simultaneously, especially on resource-limited devices such as CPU. In this work, we propose an efficient multimodal tracker named EMTrack. EMTrack follows a concise and unified multimodal tracking framework with simple knowledge distillation. RGB modality and auxiliary modality are added after patch-embedding layer for fusion, reducing the computational complexity of multimodal tracking compared with that of single modality. Before fusion operation, we introduce a modal-specific spatial modulation module to exploit and realize adaptive spatial adjustment of different modality features. Multiple modal-specific experts are adopted to capture specific information for different RGB-X tracking tasks, which assists in handling such tasks in a unified model with joint training. EMTrack achieves competitive performance on various RGB-X tracking benchmarks while reaching a good balance of performance and speed on different platforms. Especially on an Intel Core i9-10850K CPU device, EMTrack achieves 29.1 fps, a real-time speed, with only 2.0G MAC computation.

Introducing deep trackers to thermal infrared (TIR) tracking is hampered by the scarcity of large training datasets. To alleviate the predicament, a common approach is full fine-tuning (FFT) based on pretrained RGB parameters. Nevertheless, due to its inefficient training pattern and representation collapse risk, some parameter-efficient fine-tuning (PEFT) alternatives have been promoted recently. However, the existing PEFT algorithms typically follow a bottom-up way, where their attention solely relies on the input and lacks the capability of task-guided top-down attention, which provides the task-relevant representation such as the human visual perception system. In this article, we introduce ReFocus, a new PEFT method that adapts the pretrained RGB foundation tracking model to the downstream TIR tracking task through the guidance of high-level task-specific signals in a top-down attention manner. By freezing the entire foundation model and only training query-guided feature selection and top-down blocks, ReFocus achieves state-of-the-art (SOTA) TIR tracking performance while keeping training efficiency. Extensive experiments on five TIR tracking benchmarks demonstrate that ReFocus significantly improves the performance of the foundation tracker. Besides, further ablation studies show the effectiveness and flexible adaptability of the proposed method to lighter foundation models and different tracking frameworks. Compared to FFT and other bottom-up PEFT paradigms, such as head probe, low-rank adaptation (LoRA), and adapter, our method achieves comparable or superior performance with fewer training parameters and reveals the advantage of learning stability.

Re-detection is a necessary capability for long-term tracking. Target candidate proposals in the whole image can provide a chance of tracking reset when tracking fails due to tracking drift or target invisibility. In this paper, we propose a unified local-global tracker based on the same transformer architecture sharing weights, which can not only search in a continuous local region but also provide target candidates of the global image in every frame. The requirements of both long-term and short-term scenarios can be addressed using a unified model. A simple proposal selection scheme is adopted to properly select the candidate proposals of re-detection, to assist tracking and obtain better performance. The scheme performs reevaluation of all high-quality proposals based on a transformer-based embedding network, once the predicted state of the local tracking is not sufficient to be accurate. To capture appearance variations brought by online updates in minimum risks, a long-term-friendly dynamic template update scheme is also designed. Extensive experiments are conducted to demonstrate the effectiveness of our proposed tracker, including three short-term tracking benchmarks and six long-term benchmarks. Our tracker can achieve results comparable to that of the state-of-the-art. The proposed tracker can also work well in balancing the performance and speed, achieving an average speed of approximately 25 fps tested on LaSOT testing set.

Weakly supervised learning based on scribble annotations in target extraction of remote sensing images has drawn much interest due to scribbles’ flexibility in denoting winding objects and low cost of manually labeling. However, scribbles are too sparse to identify object structure and detailed information, bringing great challenges in target localization and boundary description. To alleviate these problems, in this paper, we construct two inner structure- constraints, a deformation consistency loss and a trainable active contour loss, together with a scribble-constraint to supervise the optimization of the encoder-decoder network without introducing any auxiliary module or extra operation based on prior cues. Comprehensive experiments demonstrate our method’s superiority over five state-of-the- art algorithms in this field. Source code is available at https://github.com/yitongli123/ISC-TE.

We study the high dynamic range (HDR) imaging problem in dual-lens systems. Existing methods usually treat the HDR imaging problem as an image fusion problem and the HDR result is estimated by fusing the aligned short exposure image and long exposure image. However, the image fusion pipeline depends highly on the image alignment, which is difficult to be perfect. We propose to transfer the dual-lens HDR imaging problem into the disentangled enhancement of exposure correction and denoising for the short exposure image, guided by the long exposure image. In the guided exposure correction module, we make use of the guidance image and 3D color transformation to propose a guided 3D exposure CNN (GEC) to get the rough HDR result from the short exposure image. Then, in the guided denoising module, we make use of the cross-attention mechanism to propose a guided denoising transformer (GDT) to directly use the long exposure image as guidance to denoise the rough HDR result in a pyramid way. And in both modules, we bypass the difficult image alignment processing. Experimental results demonstrate the superiority of our method over the state-of-the-art ones.

We study the colorization problem in monochrome-color dual-lens camera systems, i.e. colorizing the gray image from the monochrome camera using the color image from the color camera as reference. In related methods, cost volume based CNN methods achieve the state-of-the-art results, but they are costly in GPU memory due to building the 4D cost volume. Recently, some slice-wise cross-attention based methods are proposed for related problems. The slice-wise cross-attention has much less costs in GPU memory but directly using them for this colorization problem cannot generate competing results. We make use of the non-local computation property of cross-attention to propose a transformer based method. To overcome the limitations of straight-forward slice-wise cross-attention, we propose the spatially consistent cross-attention (SCCA) block to encourage pixels of slices across different epipolar lines in the gray image to find spatially consistent correspondence with pixels of the reference color image. And, to further reduce the memory cost while keeping the colorization accuracy, we design a pyramid processing strategy to cascade a series of SCCA blocks with smaller slice size and perform the colorization from coarse to fine. To extract more powerful image features, we use several regional self-attention (RSA) blocks with U-style connections. Experimental results show that we outperform the state-of-the-art methods largely on the synthesized datasets of Cityscapes, Sintel, and SceneFlow, and the real monochrome-color dual-lens dataset.

Colorization in monochrome-color camera systems aims to colorize the gray image IGfrom the monochrome camera using the color image RCfrom the color camera as reference. Since monochrome cameras have better imaging quality than color cameras, the colorization can help obtain higher quality color images. Related learning based methods usually simulate the monochrome-color camera systems to generate the synthesized data for training, due to the lack of ground-truth color information of the gray image in the real data. However, the methods that are trained relying on the synthesized data may get poor results when colorizing real data, because the synthesized data may deviate from the real data. We present a self-supervised CNN model, named Cycle CNN, which can directly use the real data from monochrome-color camera systems for training. In detail, we use the Weighted Average Colorization (WAC) network to do the colorization twice. First, we colorize IGusing RCas reference to obtain the first-time colorization result IC. Second, we colorize the de-colored map of RC, i.e. RG, using the concatenated image of IGand Cb/Cr channels of the first-time colorization result IC, i.e. ICCband ICCr, as reference to obtain the second-time colorization result RC'. In this way, for the second-time colorization result RC', we use the Cb and Cr channels of the original color map RCas ground-truth and introduce the cycle consistency loss to push RC'Cb/Cr≈ RCCb/Cr. Also, for the Y channel of the first-time colorization result ICY, we propose the Global Curve Adjustment (GCA) network and the structure similarity loss to encourage the structure similarity between ICYand IG. In addition, we introduce a spatial smoothness loss within the WAC network to encourage spatial smoothness of the colorization result. Combining all these losses, we could train the Cycle CNN using the real data in the absence of the ground-truth color information of IG. Experimental results show that we can outperform related methods largely for colorizing real data.

We present an unsupervised segmentation framework for stereo videos using stereoscopic trajectories. The proposed stereo trajectory shows favorable properties for modeling the long-term motion information through the whole sequence and explicitly capturing the corresponding relationships between two stereo views. The stereo prior is important for inferring the desired object and guarantees the consistent spatial-temporal segmentation, which contributes to an enjoyable stereo experience. We start by deriving stereo trajectories from left and right views simultaneously, which are represented via a graph structure. Then we detect object-like stereo trajectories via the graph structure to efficiently infer the desired object. Finally, an energy optimization function is proposed to produce the stereo segmentation results via leveraging the object information from stereo trajectories. To benefit potential research, we collected a new stereoscopic video benchmark, which consists of a total of 50 stereo video clips and includes many challenges in segmentation. Extensive experimental results demonstrate that our stereo segmentation method achieves higher performance and preserves better stereo structures, compared with prevailing competitors. The source code and results are available at: https://github.com/shenjianbing/StereoSeg.

We propose a new metric to predict perceived crosstalk using the original images rather than both the original and ghosted images. The proposed metrics are based on color information. First, we extract a disparity map, a color difference map, and a color contrast map from original image pairs. Then, we use those maps to construct two new metrics (Vdispc and Vdlogc). Metric Vdispc considers the effect of the disparity map and the color difference map, while Vdlogc addresses the influence of the color contrast map. The prediction performance is evaluated using various types of stereoscopic crosstalk images. By incorporating Vdispc and Vdlogc, the new metric Vpdlc is proposed to achieve a higher correlation with the perceived subject crosstalk scores. Experimental results show that the new metrics achieve better performance than previous methods, which indicate that color information is one key factor for crosstalk visible prediction. Furthermore, we construct a new data set to evaluate our new metrics.