Author name cluster

Chaofeng Chen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

2 author rows

ICLR Conference 2025 Conference Paper

Text4Seg: Reimagining Image Segmentation as Text Generation

Mengcheng Lan
Chaofeng Chen
Yue Zhou 0005
Jiaxing Xu
Yiping Ke
Xinjiang Wang
Litong Feng
Wayne Zhang 0001

Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in vision-language tasks; however, effectively integrating image segmentation into these models remains a significant challenge. In this paper, we introduce Text4Seg, a novel text-as-mask paradigm that casts image segmentation as a text generation problem, eliminating the need for additional decoders and significantly simplifying the segmentation process. Our key innovation is semantic descriptors, a new textual representation of segmentation masks where each image patch is mapped to its corresponding text label. This unified representation allows seamless integration into the auto-regressive training pipeline of MLLMs for easier optimization. We demonstrate that representing an image with $16\times16$ semantic descriptors yields competitive segmentation performance. To enhance efficiency, we introduce the Row-wise Run-Length Encoding (R-RLE), which compresses redundant text sequences, reducing the length of semantic descriptors by 74\% and accelerating inference by $3\times$, without compromising performance. Extensive experiments across various vision tasks, such as referring expression segmentation and comprehension, show that Text4Seg achieves state-of-the-art performance on multiple datasets by fine-tuning different MLLM backbones. Our approach provides an efficient, scalable solution for vision-centric tasks within the MLLM framework.

Details

AAAI Conference 2024 Conference Paper

Iterative Token Evaluation and Refinement for Real-World Super-resolution

Chaofeng Chen
Shangchen Zhou
Liang Liao
Haoning Wu
Wenxiu Sun
Qiong Yan
Weisi Lin

Real-world image super-resolution (RWSR) is a long-standing problem as low-quality (LQ) images often have complex and unidentified degradations. Existing methods such as Generative Adversarial Networks (GANs) or continuous diffusion models present their own issues including GANs being difficult to train while continuous diffusion models requiring numerous inference steps. In this paper, we propose an Iterative Token Evaluation and Refinement (ITER) framework for RWSR, which utilizes a discrete diffusion model operating in the discrete token representation space, i.e., indexes of features extracted from a VQGAN codebook pre-trained with high-quality (HQ) images. We show that ITER is easier to train than GANs and more efficient than continuous diffusion models. Specifically, we divide RWSR into two sub-tasks, i.e., distortion removal and texture generation. Distortion removal involves simple HQ token prediction with LQ images, while texture generation uses a discrete diffusion model to iteratively refine the distortion removal output with a token refinement network. In particular, we propose to include a token evaluation network in the discrete diffusion process. It learns to evaluate which tokens are good restorations and helps to improve the iterative refinement results. Moreover, the evaluation network can first check status of the distortion removal output and then adaptively select total refinement steps needed, thereby maintaining a good balance between distortion removal and texture generation. Extensive experimental results show that ITER is easy to train and performs well within just 8 iterative steps.

PDF Details DOI

ICML Conference 2024 Conference Paper

Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels

Haoning Wu 0001
Zicheng Zhang
Weixia Zhang
Chaofeng Chen
Liang Liao
Chunyi Li
Yixuan Gao
Annan Wang

The explosion of visual content available online underscores the requirement for an accurate machine assessor to robustly evaluate scores across diverse types of visual contents. While recent studies have demonstrated the exceptional potentials of large multi-modality models (LMMs) on a wide range of related fields, in this work, we explore how to teach them for visual rating aligning with human opinions. Observing that human raters only learn and judge discrete text-defined levels in subjective studies, we propose to emulate this subjective process and teach LMMs with text-defined rating levels instead of scores. The proposed Q-Align achieves state-of-the-art accuracy on image quality assessment (IQA), image aesthetic assessment (IAA), as well as video quality assessment (VQA) under the original LMM structure. With the syllabus, we further unify the three tasks into one model, termed the OneAlign. Our experiments demonstrate the advantage of discrete levels over direct scores on training, and that LMMs can learn beyond the discrete levels and provide effective finer-grained evaluations. Code and weights will be released.

Details

ICLR Conference 2024 Conference Paper

Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision

Haoning Wu 0001
Zicheng Zhang
Erli Zhang 0001
Chaofeng Chen
Liang Liao
Annan Wang
Chunyi Li
Wenxiu Sun

The rapid evolution of Multi-modality Large Language Models (MLLMs) has catalyzed a shift in computer vision from specialized models to general-purpose foundation models. Nevertheless, there is still an inadequacy in assessing the abilities of MLLMs on **low-level visual perception and understanding**. To address this gap, we present **Q-Bench**, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment. **_a)_** To evaluate the low-level **_perception_** ability, we construct the **LLVisionQA** dataset, consisting of 2,990 diverse-sourced images, each equipped with a human-asked question focusing on its low-level attributes. We then measure the correctness of MLLMs on answering these questions. **_b)_** To examine the **_description_** ability of MLLMs on low-level information, we propose the **LLDescribe** dataset consisting of long expert-labelled *golden* low-level text descriptions on 499 images, and a GPT-involved comparison pipeline between outputs of MLLMs and the *golden* descriptions. **_c)_** Besides these two tasks, we further measure their visual quality **_assessment_** ability to align with human opinion scores. Specifically, we design a softmax-based strategy that enables MLLMs to predict *quantifiable* quality scores, and evaluate them on various existing image quality assessment (IQA) datasets. Our evaluation across the three abilities confirms that MLLMs possess preliminary low-level visual skills. However, these skills are still unstable and relatively imprecise, indicating the need for specific enhancements on MLLMs towards these abilities. We hope that our benchmark can encourage the research community to delve deeper to discover and enhance these untapped potentials of MLLMs.

Details

AAAI Conference 2023 Conference Paper

MIMO Is All You Need：A Strong Multi-in-Multi-Out Baseline for Video Prediction

Shuliang Ning
Mengcheng Lan
Yanran Li
Chaofeng Chen
Qian Chen
Xunlai Chen
Xiaoguang Han
Shuguang Cui

The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner. This way often leads to severe performance degradation when they try to extrapolate a longer period of future, thus limiting the practical use of the prediction model. Alternatively, a Multi-In-Multi-Out (MIMO) architecture that outputs all the future frames at one shot naturally breaks the recursive manner and therefore prevents error accumulation. However, only a few MIMO models for video prediction are proposed and they only achieve inferior performance due to the date. The real strength of the MIMO model in this area is not well noticed and is largely under-explored. Motivated by that, we conduct a comprehensive investigation in this paper to thoroughly exploit how far a simple MIMO architecture can go. Surprisingly, our empirical studies reveal that a simple MIMO model can outperform the state-of-the-art work with a large margin much more than expected, especially in dealing with long-term error accumulation. After exploring a number of ways and designs, we propose a new MIMO architecture based on extending the pure Transformer with local spatio-temporal blocks and a new multi-output decoder, namely MIMO-VP, to establish a new standard in video prediction. We evaluate our model in four highly competitive benchmarks. Extensive experiments show that our model wins 1st place on all the benchmarks with remarkable performance gains and surpasses the best SISO model in all aspects including efficiency, quantity, and quality. A dramatic error reduction is achieved when predicting 10 frames on Moving MNIST and Weather datasets respectively. We believe our model can serve as a new baseline to facilitate the future research of video prediction tasks. The code will be released.

PDF Details DOI

NeurIPS Conference 2022 Conference Paper

S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint

Wenqi Yang
Guanying Chen
Chaofeng Chen
Zhenfang Chen
Kwan-Yee K. Wong

In this paper, we address the "dual problem" of multi-view scene reconstruction in which we utilize single-view images captured under different point lights to learn a neural scene representation. Different from existing single-view methods which can only recover a 2. 5D scene representation (i. e. , a normal / depth map for the visible surface), our method learns a neural reflectance field to represent the 3D geometry and BRDFs of a scene. Instead of relying on multi-view photo-consistency, our method exploits two information-rich monocular cues, namely shading and shadow, to infer scene geometry. Experiments on multiple challenging datasets show that our method is capable of recovering 3D geometry, including both visible and invisible parts, of a scene from single-view images. Thanks to the neural reflectance field representation, our method is robust to depth discontinuities. It supports applications like novel-view synthesis and relighting. Our code and model can be found at https: //ywq. github. io/s3nerf.

PDF Details

AAAI Conference 2018 Conference Paper

Char-Net: A Character-Aware Neural Network for Distorted Scene Text Recognition

Wei Liu
Chaofeng Chen
Kwan-Yee Wong

In this paper, we present a Character-Aware Neural Network (Char-Net) for recognizing distorted scene text. Our Char- Net is composed of a word-level encoder, a character-level encoder, and a LSTM-based decoder. Unlike previous work which employed a global spatial transformer network to rectify the entire distorted text image, we take an approach of detecting and rectifying individual characters. To this end, we introduce a novel hierarchical attention mechanism (HAM) which consists of a recurrent RoIWarp layer and a characterlevel attention layer. The recurrent RoIWarp layer sequentially extracts a feature region corresponding to a character from the feature map produced by the word-level encoder, and feeds it to the character-level encoder which removes the distortion of the character through a simple spatial transformer and further encodes the character region. The character-level attention layer then attends to the most relevant features of the feature map produced by the characterlevel encoder and composes a context vector, which is ﬁnally fed to the LSTM-based decoder for decoding. This approach of adopting a simple local transformation to model the distortion of individual characters not only results in an improved efﬁciency, but can also handle different types of distortion that are hard, if not impossible, to be modelled by a single global transformation. Experiments have been conducted on six public benchmark datasets. Our results show that Char- Net can achieve state-of-the-art performance on all the benchmarks, especially on the IC-IST which contains scene text with large distortion. Code will be made available.

PDF Details