Urban Regional Function Guided Traffic Flow Prediction

The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest. In addition to spatial-temporal correlations, the functionality of urban areas also plays a crucial role in traffic flow prediction. However, the exploration of regional functional attributes mainly focuses on adding additional topological structures, ignoring the influence of functional attributes on regional traffic patterns. Different from the existing works, we propose a novel module named POI-MetaBlock, which utilizes the functionality of each region (represented by Point of Interest distribution) as metadata to further mine different traffic characteristics in areas with different functions. Specifically, the proposed POI-MetaBlock employs a self-attention architecture and incorporates POI and time information to generate dynamic attention parameters for each region, which enables the model to fit different traffic patterns of various areas at different times. Furthermore, our lightweight POI-MetaBlock can be easily integrated into conventional traffic flow prediction models. Extensive experiments demonstrate that our module significantly improves the performance of traffic flow prediction and outperforms state-of-the-art methods that use metadata.

Visual-Linguistic Causal Intervention for Radiology Report Generation

Automatic radiology report generation is essential for computer-aided diagnosis and medication guidance. Importantly, automatic radiology report generation (RRG) can relieve the heavy burden of radiologists by generating medical reports automatically from visual-linguistic data relations. However, due to the spurious correlations within image-text data induced by visual and linguistic biases, it is challenging to generate accurate reports that reliably describe abnormalities. Besides, the cross-modal confounder is usually unobservable and difficult to be eliminated explicitly. In this paper, we mitigate the cross-modal data bias for RRG from a new perspective, i.e., visual-linguistic causal intervention, and propose a novel Visual-Linguistic Causal Intervention (VLCI) framework for RRG, which consists of a visual deconfounding module (VDM) and a linguistic deconfounding module (LDM), to implicitly deconfound the visual-linguistic confounder by causal front-door intervention. Specifically, the VDM explores and disentangles the visual confounder from the patch-based local and global features without object detection due to the absence of universal clinic semantic extraction. Simultaneously, the LDM eliminates the linguistic confounder caused by salient visual features and high-frequency context without constructing specific dictionaries. Extensive experiments on IU-Xray and MIMIC-CXR datasets show that our VLCI outperforms the state-of-the-art RRG methods significantly. Source code and models are available at https://github.com/WissingChen/VLCI.

Masked Images Are Counterfactual Samples for Robust Fine-tuning

Deep learning models are challenged by the distribution shift between the training data and test data. Recently, the large models pre-trained on diverse data demonstrate unprecedented robustness to various distribution shifts. However, fine-tuning on these models can lead to a trade-off between in-distribution (ID) performance and out-of-distribution (OOD) robustness. Existing methods for tackling this trade-off do not explicitly address the OOD robustness problem. In this paper, based on causal analysis on the aforementioned problems, we propose a novel fine-tuning method, which use masked images as counterfactual samples that help improving the robustness of the fine-tuning model. Specifically, we mask either the semantics-related or semantics-unrelated patches of the images based on class activation map to break the spurious correlation, and refill the masked patches with patches from other images. The resulting counterfactual samples are used in feature-based distillation with the pre-trained model. Extensive experiments verify that regularizing the fine-tuning with the proposed masked images can achieve a better trade-off between ID and OOD performance, surpassing previous methods on the OOD performance. Our code will be publicly available.

Actional Atomic-Concept Learning for Demystifying Vision-Language Navigation

Vision-Language Navigation (VLN) is a challenging task which requires an agent to align complex visual observations to language instructions to reach the goal position. Most existing VLN agents directly learn to align the raw directional features and visual features trained using one-hot labels to linguistic instruction features. However, the big semantic gap among these multi-modal inputs makes the alignment difficult and therefore limits the navigation performance. In this paper, we propose Actional Atomic-Concept Learning (AACL), which maps visual observations to actional atomic concepts for facilitating the alignment. Specifically, an actional atomic concept is a natural language phrase containing an atomic action and an object, e.g., ``go up stairs''. These actional atomic concepts, which serve as the bridge between observations and instructions, can effectively mitigate the semantic gap and simplify the alignment. AACL contains three core components: 1) a concept mapping module to map the observations to the actional atomic concept representations through the VLN environment and the recently proposed Contrastive Language-Image Pretraining (CLIP) model, 2) a concept refining adapter to encourage more instruction-oriented object concept extraction by re-ranking the predicted object concepts by CLIP, and 3) an observation co-embedding module which utilizes concept representations to regularize the observation representations. Our AACL establishes new state-of-the-art results on both fine-grained (R2R) and high-level (REVERIE and R2R-Last) VLN benchmarks. Moreover, the visualization shows that AACL significantly improves the interpretability in action decision.

On Robust Numerical Solver for ODE via Self-Attention Mechanism

With the development of deep learning techniques, AI-enhanced numerical solvers are expected to become a new paradigm for solving differential equations due to their versatility and effectiveness in alleviating the accuracy-speed trade-off in traditional numerical solvers. However, this paradigm still inevitably requires a large amount of high-quality data, whose acquisition is often very expensive in natural science and engineering problems. Therefore, in this paper, we explore training efficient and robust AI-enhanced numerical solvers with a small data size by mitigating intrinsic noise disturbances. We first analyze the ability of the self-attention mechanism to regulate noise in supervised learning and then propose a simple-yet-effective numerical solver, AttSolver, which introduces an additive self-attention mechanism to the numerical solution of differential equations based on the dynamical system perspective of the residual neural network. Our results on benchmarks, ranging from high-dimensional problems to chaotic systems, demonstrate the effectiveness of AttSolver in generally improving the performance of existing traditional numerical solvers without any elaborated model crafting. Finally, we analyze the convergence, generalization, and robustness of the proposed method experimentally and theoretically.

OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup

Image Virtual try-on aims at replacing the cloth on a personal image with a garment image (in-shop clothes), which has attracted increasing attention from the multimedia and computer vision communities. Prior methods successfully preserve the character of clothing images, however, occlusion remains a pernicious effect for realistic virtual try-on. In this work, we first present a comprehensive analysis of the occlusions and categorize them into two aspects: i) Inherent-Occlusion: the ghost of the former cloth still exists in the try-on image; ii) Acquired-Occlusion: the target cloth warps to the unreasonable body part. Based on the in-depth analysis, we find that the occlusions can be simulated by a novel semantically-guided mixup module, which can generate semantic-specific occluded images that work together with the try-on images to facilitate training a de-occlusion try-on (DOC-VTON) framework. Specifically, DOC-VTON first conducts a sharpened semantic parsing on the try-on person. Aided by semantics guidance and pose prior, various complexities of texture are selectively blending with human parts in a copy-and-paste manner. Then, the Generative Module (GM) is utilized to take charge of synthesizing the final try-on image and learning to de-occlusion jointly. In comparison to the state-of-the-art methods, DOC-VTON achieves better perceptual quality by reducing occlusion effects.

Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification

In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.

UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression

Geometry problem solving is a well-recognized testbed for evaluating the high-level multi-modal reasoning capability of deep models. In most existing works, two main geometry problems: calculation and proving, are usually treated as two specific tasks, hindering a deep model to unify its reasoning capability on multiple math tasks. However, in essence, these two tasks have similar problem representations and overlapped math knowledge which can improve the understanding and reasoning ability of a deep model on both two tasks. Therefore, we construct a large-scale Unified Geometry problem benchmark, UniGeo, which contains 4,998 calculation problems and 9,543 proving problems. Each proving problem is annotated with a multi-step proof with reasons and mathematical expressions. The proof can be easily reformulated as a proving sequence that shares the same formats with the annotated program sequence for calculation problems. Naturally, we also present a unified multi-task Geometric Transformer framework, Geoformer, to tackle calculation and proving problems simultaneously in the form of sequence generation, which finally shows the reasoning ability can be improved on both two tasks by unifying formulation. Furthermore, we propose a Mathematical Expression Pretraining (MEP) method that aims to predict the mathematical expressions in the problem solution, thus improving the Geoformer model. Experiments on the UniGeo demonstrate that our proposed Geoformer obtains state-of-the-art performance by outperforming task-specific model NGS with over 5.6% and 3.2% accuracies on calculation and proving problems, respectively.

DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Contrastive Prompt-Tuning

Large-scale text-to-image generation models with an exponential evolution can currently synthesize high-resolution, feature-rich, high-quality images based on text guidance. However, they are often overwhelmed by words of new concepts, styles, or object entities that always emerge. Although there are some recent attempts to use fine-tuning or prompt-tuning methods to teach the model a new concept as a new pseudo-word from a given reference image set, these methods are not only still difficult to synthesize diverse and high-quality images without distortion and artifacts, but also suffer from low controllability. To address these problems, we propose a DreamArtist method that employs a learning strategy of contrastive prompt-tuning, which introduces both positive and negative embeddings as pseudo-words and trains them jointly. The positive embedding aggressively learns characteristics in the reference image to drive the model diversified generation, while the negative embedding introspects in a self-supervised manner to rectify the mistakes and inadequacies from positive embedding in reverse. It learns not only what is correct but also what should be avoided. Extensive experiments on image quality and diversity analysis, controllability analysis, model learning analysis and task expansion have demonstrated that our model learns not only concept but also form, content and context. Pseudo-words of DreamArtist have similar properties as true words to generate high-quality images.

Category-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels

As a promising solution of reducing annotation cost, training multi-label models with partial positive labels (MLR-PPL), in which merely few positive labels are known while other are missing, attracts increasing attention. Due to the absence of any negative labels, previous works regard unknown labels as negative and adopt traditional MLR algorithms. To reject noisy labels, recent works regard large loss samples as noise but ignore the semantic correlation different multi-label images. In this work, we propose to explore semantic correlation among different images to facilitate the MLR-PPL task. Specifically, we design a unified framework, Category-Adaptive Label Discovery and Noise Rejection, that discovers unknown labels and rejects noisy labels for each category in an adaptive manner. The framework consists of two complementary modules: (1) Category-Adaptive Label Discovery module first measures the semantic similarity between positive samples and then complement unknown labels with high similarities; (2) Category-Adaptive Noise Rejection module first computes the sample weights based on semantic similarities from different samples and then discards noisy labels with low weights. Besides, we propose a novel category-adaptive threshold updating that adaptively adjusts the threshold, to avoid the time-consuming manual tuning process. Extensive experiments demonstrate that our proposed method consistently outperforms current leading algorithms.