Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models. Many prior works have proposed methods for mitigating gender biases, often by attempting to remove gender expression information from images. To understand the feasibility and practicality of these approaches, we investigate what "gender artifacts" exist in large-scale visual datasets. We define a "gender artifact" as a visual cue correlated with gender , focusing specifically on cues that are learnable by a modern image classifier and have an interpretable human corollary. Through our analyses, we find that gender artifacts are ubiquitous in the COCO and OpenImages datasets, occurring everywhere from low-level information (e.g., the mean value of the color channels) to higher-level image composition (e.g., pose and location of people). Further, bias mitigation methods that attempt to remove gender actually remove more information from the scene than the person. Given the prevalence of gender artifacts, we claim that attempts to remove these artifacts from such datasets are largely infeasible as certain removed artifacts may be necessary for the downstream task of object recognition. Instead, the responsibility lies with researchers and practitioners to be aware that the distribution of images within datasets is highly gendered and hence develop fairness-aware methods which are robust to these distributional shifts across groups.
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Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one. The question of how to strike a good trade-off between visual quality and computational speed has remained one of the open challenges in video stabilization. Inspired by the analogy between wobbly frames and jigsaw puzzles, we propose an iterative optimization-based learning approach using synthetic datasets for video stabilization, which consists of two interacting submodules: motion trajectory smoothing and full-frame outpainting. First, we develop a two-level (coarse-to-fine) stabilizing algorithm based on the probabilistic flow field. The confidence map associated with the estimated optical flow is exploited to guide the search for shared regions through backpropagation. Second, we take a divide-and-conquer approach and propose a novel multiframe fusion strategy to render full-frame stabilized views. An important new insight brought about by our iterative optimization approach is that the target video can be interpreted as the fixed point of nonlinear mapping for video stabilization. We formulate video stabilization as a problem of minimizing the amount of jerkiness in motion trajectories, which guarantees convergence with the help of fixed-point theory. Extensive experimental results are reported to demonstrate the superiority of the proposed approach in terms of computational speed and visual quality. The code will be available on GitHub.
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images
PDF ↗Weird, unusual, and uncanny images pique the curiosity of observers because they challenge commonsense. For example, an image released during the 2022 world cup depicts the famous soccer stars Lionel Messi and Cristiano Ronaldo playing chess, which playfully violates our expectation that their competition should occur on the football field. Humans can easily recognize and interpret these unconventional images, but can AI models do the same? We introduce WHOOPS!, a new dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. We consider several tasks posed over the dataset. In addition to image captioning, cross-modal matching, and visual question answering, we introduce a difficult explanation generation task, where models must identify and explain why a given image is unusual. Our results show that state-of-the-art models such as GPT3 and BLIP2 still lag behind human performance on WHOOPS!. We hope our dataset will inspire the development of AI models with stronger visual commonsense reasoning abilities.
Towards Realistic Evaluation of Industrial Continual Learning Scenarios with an Emphasis on Energy Consumption and Computational Footprint
PDF ↗Incremental Learning (IL) aims to develop Machine Learning (ML) models that can learn from continuous streams of data and mitigate catastrophic forgetting. We analyse the current state-of-the-art Class-IL implementations and demonstrate why the current body of research tends to be one-dimensional, with an excessive focus on accuracy metrics. A realistic evaluation of Continual Learning methods should also emphasise energy consumption and overall computational load for a comprehensive understanding. This paper addresses research gaps between current IL research and industrial project environments, including varying incremental tasks and the introduction of Joint Training in tandem with IL. We introduce InVar-100 (Industrial Objects in Varied Contexts), a novel dataset meant to simulate the visual environments in industrial setups and perform various experiments for IL. Additionally, we incorporate explainability (using class activations) to interpret the model predictions. Our approach, RECIL (Real-World Scenarios and Energy Efficiency Considerations for Class Incremental Learning) provides meaningful insights about the applicability of IL approaches in practical use cases. The overarching aim is to bring the Incremental Learning and Green AI fields together and encourage the application of CIL methods in real-world scenarios. Code and dataset are available.
The favorable performance of Vision Transformers (ViTs) is often attributed to the multi-head self-attention (MSA), which enables global interactions at each layer of a ViT model. Previous works acknowledge the property of long-range dependency for the effectiveness in MSA. In this work, we study the role of MSA in terms of the different axis, density. Our preliminary analyses suggest that the spatial interactions of learned attention maps are close to dense interactions rather than sparse ones. This is a curious phenomenon because dense attention maps are harder for the model to learn due to softmax. We interpret this opposite behavior against softmax as a strong preference for the ViT models to include dense interaction. We thus manually insert the dense uniform attention to each layer of the ViT models to supply the much-needed dense interactions. We call this method Context Broadcasting, CB. Our study demonstrates the inclusion of CB takes the role of dense attention, and thereby reduces the degree of density in the original attention maps by complying softmax in MSA. We also show that, with negligible costs of CB (1 line in your model code and no additional parameters), both the capacity and generalizability of the ViT models are increased.
PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification
PDF ↗Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent space between the visual patch and linguistic label domains and introduces the conditional transport (CT) theory to bridge the acknowledged gap. While recent cross-modal attention-based studies have attempted to align such two representations and achieved impressive performance, they required carefully-designed alignment modules and extra complex operations in the attention computation. We find that by formulating the multi-label classification as a CT problem, we can exploit the interactions between the image and label efficiently by minimizing the bidirectional CT cost. Specifically, after feeding the images and textual labels into the modality-specific encoders, we view each image as a mixture of patch embeddings and a mixture of label embeddings, which capture the local region features and the class prototypes, respectively. CT is then employed to learn and align those two semantic sets by defining the forward and backward navigators. Importantly, the defined navigators in CT distance model the similarities between patches and labels, which provides an interpretable tool to visualize the learned prototypes. Extensive experiments on three public image benchmarks show that the proposed model consistently outperforms the previous methods.
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
PDF ↗Despite thousands of researchers, engineers, and artists actively working on improving text-to-image generation models, systems often fail to produce images that accurately align with the text inputs. We introduce TIFA (Text-to-image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA). Specifically, given a text input, we automatically generate several question-answer pairs using a language model. We calculate image faithfulness by checking whether existing VQA models can answer these questions using the generated image. TIFA is a reference-free metric that allows for fine-grained and interpretable evaluations of generated images.TIFA also has better correlations with human judgments than existing metrics. Based on this approach, we introduce TIFA v1.0, a benchmark consisting of 4K diverse text inputs and 25K questions across 12 categories (object, counting, etc.). We present a comprehensive evaluation of existing text-to-image models using TIFA v1.0 and highlight the limitations and challenges of current models. For instance, we find that current text-to-image models, despite doing well on color and material, still struggle in counting, spatial relations, and composing multiple objects. We hope our benchmark will help carefully measure the research progress in text-to-image synthesis and provide valuable insights for further research.
Visual Emotion Analysis (VEA) aims at predicting people's emotional responses to visual stimuli. This is a promising, yet challenging, task in affective computing, which has drawn increasing attention in recent years. Most of the existing work in this area focuses on feature design, while little attention has been paid to dataset construction. In this work, we introduce EmoSet, the first large-scale visual emotion dataset annotated with rich attributes, which is superior to existing datasets in four aspects: scale, annotation richness, diversity, and data balance. EmoSet comprises 3.3 million images in total, with 118,102 of these images carefully labeled by human annotators, making it five times larger than the largest existing dataset. EmoSet includes images from social networks, as well as artistic images, and it is well balanced between different emotion categories. Motivated by psychological studies, in addition to emotion category, each image is also annotated with a set of describable emotion attributes: brightness, colorfulness, scene type, object class, facial expression, and human action, which can help understand visual emotions in a precise and interpretable way. The relevance of these emotion attributes is validated by analyzing the correlations between them and visual emotion, as well as by designing an attribute module to help visual emotion recognition. We believe EmoSet will bring some key insights and encourage further research in visual emotion analysis and understanding. Project page: https://vcc.tech/EmoSet.
Toward Multi-Granularity Decision-Making: Explicit Visual Reasoning with Hierarchical Knowledge
PDF ↗Answering visual questions requires the ability to parse visual observations and correlate them with a variety of knowledge. Existing visual question answering (VQA) models either pay little attention to the role of knowledge or do not take into account the granularity of knowledge, e.g., attaching the color of "grassland" to "ground"). They have yet to develop the capability of modeling knowledge of multiple granularity, and are also vulnerable to spurious data biases. To fill the gap, this paper makes progresses from two distinct perspectives: (1) It presents a Hierarchical Concept Graph (HCG) that discriminates and associates multi-granularity concepts with a multi-layered hierarchical structure, aligning visual observations with knowledge across different levels to alleviate data biases. (2) To facilitate a comprehensive understanding of how knowledge contributes throughout the decision-making process, we further propose an interpretable Hierarchical Concept Neural Module Network (HCNMN) It explicitly propagates multi-granularity knowledge across the hierarchical structure and incorporates them with a sequence of reasoning steps, providing a transparent interface to elaborate on the integration of observations and knowledge. Through extensive experiments on multiple challenging datasets (i.e., GQA,VQA,FVQA,OK-VQA) , we demonstrate the effectiveness of our method in answering questions in different scenarios. Our code is available at https://github.com/SuperJohnZhang/HCNMN.
VLN-CE is a recently released embodied task, where AI agents need to navigate a freely traversable environment to reach a distant target location, given language instructions. It poses great challenges due to the huge space of possible strategies. Driven by the belief that the ability to anticipate the consequences of future actions is crucial for the emergence of intelligent and interpretable planning behavior, we propose Dreamwalker --- a world model based VLN-CE agent. The world model is built to summarize the visual, topological, and dynamic properties of the complicated continuous environment into a discrete, structured, and compact representation. Dreamwalker can simulate and evaluate possible plans entirely in such internal abstract world, before executing costly actions. As opposed to existing model-free VLN-CE agents simply making greedy decisions in the real world, which easily results in shortsighted behaviors, Dreamwalker is able to make strategic planning through large amounts of "mental experiments." Moreover, the imagined future scenarios reflect our agent's intention, making its decision-making process more transparent. Extensive experiments and ablation studies on VLN-CE dataset confirm the effectiveness of the proposed approach and outline fruitful directions for future work. Our code will be released.
This paper presents OxfordTVG-HIC (Humorous Image Captions), a large-scale dataset for humour generation and understanding. Humour is an abstract, subjective, and context-dependent cognitive construct involving several cognitive factors, making it a challenging task to generate and interpret. Hence, humour generation and understanding can serve as a new task for evaluating the ability of deep-learning methods to process abstract and subjective information. Due to the scarcity of data, humour-related generation tasks such as captioning remain underexplored. To address this gap, OxfordTVG-HIC offers approximately 2.9M image-text pairs with humour scores to train a generalizable humour captioning model. Contrary to existing captioning datasets, OxfordTVG-HIC features a wide range of emotional and semantic diversity resulting in out-of-context examples that are particularly conducive to generating humour. Moreover, OxfordTVG-HIC is curated devoid of offensive content. We also show how OxfordTVGHIC can be leveraged for evaluating the humour of a generated text. Through explainability analysis of the trained models, we identify the visual and linguistic cues influential for evoking humour prediction (and generation). We observe qualitatively that these cues are aligned with the benign violation theory of humour in cognitive psychology.
Unified systems for multi-sensor devices, particularly eye-tracking in Virtual Reality (VR), are intricate and often require the listening and streaming of multichannel data. In this project, we propose a visual analysis framework for replicating a participant's viewing involvement by interpreting head movements as rotations and point-of-gaze (POG) as on-screen indicators. Our solution suggests an additional layer of system for near-real-time for processing and analyzing this multi-device data to connect with the data and enable both near-real-time or subsequent offline viewing of the entire VR eye-tracking session. Moreover, our method provides a no-batteries-need solution to create traditional eye-tracking visualization techniques. Finally, we apply three prior education technology analysis metrics: higher density gaze for students, shorter fixation time, and less fixation duration variance for students to determine expertise levels in this system. We systematically establish a ubiquitous, multi-device, eye-tracking solution to incorporate this approach. We evaluate the effectiveness of our system through a user study, using both expertise and non- expertise levels, and selectively surveying to ascertain the quality of the replicated experience and we test the system by running a real-world user study with sixty four different participants. We demonstrate the application's significance and potential to integrate prior analysis metrics using the collected data which this data collection and analysis have been approved by IRB.
Panoramic activity recognition is required to jointly identify multi-granularity human behaviors including individual actions, group activities, and global activities in multi-person videos. Previous methods encode these behaviors hierarchically through multiple stages, which disturb the inherent co-occurrence across multi-granularity behaviors in the same scene. To this end, we propose a novel Multi-granularity Unified Perception (MUP) framework that perceives different granularity behaviors universally to explore the co-occurrence motion pattern via the same parameters in an end-to-end fashion. To be specific, the proposed framework stacks three Unified Motion Encoding (UME) blocks for modeling multiple granularity behaviors with shared parameters. UME block mines intra-relevant and cross-relevant semantics synchronously from input feature sequences via Intra-granularity Motion Embedding (IME) and Cross-granularity Motion Prototyping (CMP). In particular, IME aims to model the interactions among visual features within each granularity based on the attention mechanism. CMP aims to aggregate features across different granularities (i.e., person to group) via several learnable prototypes. Extensive experiments demonstrate that MUP outperforms the state-of-the-art methods on JRDB-PAR and has satisfactory interpretability.
Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications. Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice. To balance the visual quality and application, we propose a heuristic normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow. Specifically, we first develop an invertible mapping to achieve the translation between the degraded image and its clear counterpart. Considering the differentiability and interpretability, we incorporate the heuristic prior into the data-driven mapping procedure, where the ambient light and medium transmission coefficient benefit credible generation. Furthermore, we introduce a detection perception module to transmit the implicit semantic guidance into the enhancement procedure, where the enhanced images hold more detection-favorable features and are able to promote the detection performance. Extensive experiments prove the superiority of our WaterFlow, against state-of-the-art methods quantitatively and qualitatively.
MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion
PDF ↗Reconstructing visual stimuli from brain recordings has been a meaningful and challenging task. Especially, the achievement of precise and controllable image reconstruction bears great significance in propelling the progress and utilization of brain-computer interfaces. Despite the advancements in complex image reconstruction techniques, the challenge persists in achieving a cohesive alignment of both semantic (concepts and objects) and structure (position, orientation, and size) with the image stimuli. To address the aforementioned issue, we propose a two-stage image reconstruction model called MindDiffuser1. In Stage 1, the VQ-VAE latent representations and the CLIP text embeddings decoded from fMRI are put into Stable Diffusion, which yields a preliminary image that contains semantic information. In Stage 2, we utilize the CLIP visual feature decoded from fMRI as supervisory information, and continually adjust the two feature vectors decoded in Stage 1 through backpropagation to align the structural information. The results of both qualitative and quantitative analyses demonstrate that our model has surpassed the current state-of-the-art models on Natural Scenes Dataset (NSD). The subsequent experimental findings corroborate the neurobiological plausibility of the model, as evidenced by the interpretability of the multimodal feature employed, which align with the corresponding brain responses.
MaTCR: Modality-Aligned Thought Chain Reasoning for Multimodal Task-Oriented Dialogue Generation
PDF ↗In recent years, multimodal task-oriented dialogue systems have attracted increasing attention from communities, owing to their ability to naturally and efficiently provide user service. Despite the commercial value of multimodal dialogue systems, they are still confronted with two challenges: (1) capture users' intention from lengthy context and side knowledge for question comprehension; (2) jointly consider the multimodal information for response generation. In view of the challenges, previous methods designed for specific scenario lack auxiliary reasoning structures with effective modality interaction, which hinders the comprehension of user's needs and impedes the generation of desired responses. To address these issues, we propose a Modality-aligned Thought Chain Reasoning (MaTCR) framework to insert explicit reasoning process for multimodal task-oriented dialogue generation. We construct a multimodal thought chain by summarizing intermediate user queries from aligned visual and textual context, which helps to guide the comprehension of user intentions for generating reasonable responses. To effectively extract and integrate multimodal information for high-quality thought chain reasoning, we design a multimodal reasoner consisting of visual representation learning and modality-aligned fusion. We comparatively justify MaTCR with several strong baselines, including the currently highly regarded large language model. Extensive experiments over a benchmark dataset demonstrate that MaTCR outperforms the existing methods and provides stronger interpretability.
Speech-driven 3D face animation technique, extending its applications to various multimedia fields.Previous research has generated promising realistic lip movements and facial expressions from audio signals. However, traditional regression models solely driven by data face several essential problems, such as difficulties in accessing precise labels and domain gaps between different modalities, leading to unsatisfactory results lacking precision and coherence.To enhance the visual accuracy of generated lip movement while reducing the dependence on labeled data, we propose a novel framework SelfTalk, by involving self-supervision in a cross-modals network system to learn 3D talking faces. The framework constructs a network system consisting of three modules: facial animator, speech recognizer, and lip-reading interpreter. The core of SelfTalk is a commutative training diagram that facilitates compatible features exchange among audio, text, and lip shape, enabling our models to learn the intricate connection between these factors. The proposed framework leverages the knowledge learned from the lip-reading interpreter to generate more plausible lip shapes. Extensive experiments and user studies demonstrate that our proposed approach achieves state-of-the-art performance both qualitatively and quantitatively. We recommend watching the supplementary video.
Multi-view (or -modality) representation learning aims to understand the relationships between different view representations. Existing methods disentangle multi-view representations into consistent and view-specific representations by introducing strong inductive biases, which can limit their generalization ability. In this paper, we propose a novel multi-view representation disentangling method that aims to go beyond inductive biases, ensuring both interpretability and generalizability of the resulting representations. Our method is based on the observation that discovering multi-view consistency in advance can determine the disentangling information boundary, leading to a decoupled learning objective. We also found that the consistency can be easily extracted by maximizing the transformation invariance and clustering consistency between views. These observations drive us to propose a two-stage framework. In the first stage, we obtain multi-view consistency by training a consistent encoder to produce semantically-consistent representations across views as well as their corresponding pseudo-labels. In the second stage, we disentangle specificity from comprehensive representations by minimizing the upper bound of mutual information between consistent and comprehensive representations. Finally, we reconstruct the original data by concatenating pseudo-labels and view-specific representations. Our experiments on four multi-view datasets demonstrate that our proposed method outperforms 12 comparison methods in terms of clustering and classification performance. The visualization results also show that the extracted consistency and specificity are compact and interpretable. Our code can be found at https://github.com/Guanzhou-Ke/DMRIB.
In this work, we propose PMSDSEN, a parallel multi-scale encoder-decoder network architecture for semantic segmentation, inspired by the human visual perception system's ability to aggregate contextual information in various contexts and scales. Our approach introduces the efficient Parallel Multi-Scale Detail and Semantic Encoding (PMSDSE) unit to extract detailed local information and coarse large-range relationships in parallel, enabling the recognition of object boundaries and object-level areas. By stacking multiple PMSDSEs, our network learns fine-grained details and textures along with abstract category and semantic information, effectively utilizing a larger range of surrounding context information for robust segmentation. To further enhance the network's receptive field without increasing computational complexity, the Multi-Scale Semantic Extractor (MSSE) at the end of the encoder is utilized for multi-scale semantic context extraction and detailed information encoding. Additionally, the Dynamic Weighted Feature Fusion (DWFF) strategy is employed to integrate shallow layer detail information and deep layer semantic information during the decoder stage. Our method can obtain multi-scale context from local to global, achieving efficiently low-level feature extraction to high-level semantic interpretation at different scales and in different contexts. Without bells and whistles, PMSDSEN obtains a better trade-off between accuracy and complexity on popular benchmarks, including Cityscapes and Camvid. Specifically, PMSDSEN attains 73.2% mIoU with only 0.9M parameters on the Cityscapes test set. Codes and supplementary materials link: https://github.com/liux520/PMSDSEN.
To circumvent the problems caused by prolonged acquisition periods, compressed sensing MRI enjoys a high usage profile to accelerate the recovery of high-quality images from under-sampled k-space data. Most current solutions dedicate to solving this issue with the pursuit of certain prior properties, yet the treatments are all enforced in the original space, resulting in limited feature information. To achieve a performance promotion yet with the guarantee of running efficiency, in this work, we propose a latent-space unfolding network (LsUNet). Specifically, by an elaborately designed reversible network, the inputs are first mapped to a channel-lifted latent space, which taps the potential of capturing spatial-invariant features sufficiently. Within the latent space, we then unfold an accelerated optimization algorithm to iterate an efficient and feasible solution, in which a parallelly dual-domain update is equipped for better feature fusion. Finally, an inverse embedding transformation of the recovered high-dimensional representation is applied to achieve the expected estimation. LsUNet enjoys high interpretability due to the physically induced modules, which not only facilitates an intuitive understanding of the internal operating mechanism but also endows it with high generalization ability. Comprehensive experiments on different datasets and various sampling rates/patterns demonstrate the advantages of our proposal over the latest methods both visually and numerically.