Text- and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild

Systems for multimodal Emotion Recognition (ER) commonly rely on features extracted from different modalities (e.g., visual, audio, and textual) to predict the seven basic emotions. However, compound emotions often occur in real-world scenarios and are more difficult to predict. Compound multimodal ER becomes more challenging in videos due to the added uncertainty of diverse modalities. In addition, standard features-based models may not fully capture the complex and subtle cues needed to understand compound emotions. %%%% Since relevant cues can be extracted in the form of text, we advocate for textualizing all modalities, such as visual and audio, to harness the capacity of large language models (LLMs). These models may understand the complex interaction between modalities and the subtleties of complex emotions. Although training an LLM requires large-scale datasets, a recent surge of pre-trained LLMs, such as BERT and LLaMA, can be easily fine-tuned for downstream tasks like compound ER. This paper compares two multimodal modeling approaches for compound ER in videos -- standard feature-based vs. text-based. Experiments were conducted on the challenging C-EXPR-DB dataset for compound ER, and contrasted with results on the MELD dataset for basic ER. Our code is available

Leveraging Transformers for Weakly Supervised Object Localization in Unconstrained Videos

Weakly-Supervised Video Object Localization (WSVOL) involves localizing an object in videos using only video-level labels, also referred to as tags. State-of-the-art WSVOL methods like Temporal CAM (TCAM) rely on class activation mapping (CAM) and typically require a pre-trained CNN classifier. However, their localization accuracy is affected by their tendency to minimize the mutual information between different instances of a class and exploit temporal information during training for downstream tasks, e.g., detection and tracking. In the absence of bounding box annotation, it is challenging to exploit precise information about objects from temporal cues because the model struggles to locate objects over time. To address these issues, a novel method called transformer based CAM for videos (TrCAM-V), is proposed for WSVOL. It consists of a DeiT backbone with two heads for classification and localization. The classification head is trained using standard classification loss (CL), while the localization head is trained using pseudo-labels that are extracted using a pre-trained CLIP model. From these pseudo-labels, the high and low activation values are considered to be foreground and background regions, respectively. Our TrCAM-V method allows training a localization network by sampling pseudo-pixels on the fly from these regions. Additionally, a conditional random field (CRF) loss is employed to align the object boundaries with the foreground map. During inference, the model can process individual frames for real-time localization applications. Extensive experiments on challenging YouTube-Objects unconstrained video datasets show that our TrCAM-V method achieves new state-of-the-art performance in terms of classification and localization accuracy.

SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution

Confocal fluorescence microscopy is one of the most accessible and widely used imaging techniques for the study of biological processes. Scanning confocal microscopy allows the capture of high-quality images from 3D samples, yet suffers from well-known limitations such as photobleaching and phototoxicity of specimens caused by intense light exposure, which limits its use in some applications, especially for living cells. Cellular damage can be alleviated by changing imaging parameters to reduce light exposure, often at the expense of image quality. Machine/deep learning methods for single-image super-resolution (SISR) can be applied to restore image quality by upscaling lower-resolution (LR) images to produce high-resolution images (HR). These SISR methods have been successfully applied to photo-realistic images due partly to the abundance of publicly available data. In contrast, the lack of publicly available data partly limits their application and success in scanning confocal microscopy. In this paper, we introduce a large scanning confocal microscopy dataset named SR-CACO-2 that is comprised of low- and high-resolution image pairs marked for three different fluorescent markers. It allows the evaluation of performance of SISR methods on three different upscaling levels (X2, X4, X8). SR-CACO-2 contains the human epithelial cell line Caco-2 (ATCC HTB-37), and it is composed of 22 tiles that have been translated in the form of 9,937 image patches for experiments with SISR methods. Given the new SR-CACO-2 dataset, we also provide benchmarking results for 15 state-of-the-art methods that are representative of the main SISR families. Results show that these methods have limited success in producing high-resolution textures, indicating that SR-CACO-2 represents a challenging problem. Our dataset, code and pretrained weights are available: https://github.com/sbelharbi/sr-caco-2.

MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection

In this paper, we present a different way to use two modalities, in which either one modality or the other is seen by a single model. This can be useful when adapting an unimodal model to leverage more information while respecting a limited computational budget. This would mean having a single model that is able to deal with any modalities. To describe this, we coined the term anymodal learning. An example of this, is a use case where, surveillance in a room when the lights are off would be much more valuable using an infrared modality while a visible one would provide more discriminative information when lights are on. This work investigates how to efficiently leverage visible and infrared/thermal modalities for transformer-based object detection backbone to create an anymodal architecture. Our work does not create any inference overhead during the testing while exploring an effective way to exploit the two modalities during the training. To accomplish such a task, we introduce the novel anymodal training technique: Mixed Patches (MiPa), in conjunction with a patch-wise domain agnostic module, which is responsible of learning the best way to find a common representation of both modalities. This approach proves to be able to balance modalities by reaching competitive results on individual modality benchmarks with the alternative of using an unimodal architecture on three different visible-infrared object detection datasets. Finally, our proposed method, when used as a regularization for the strongest modality, can beat the performance of multimodal fusion methods while only requiring a single modality during inference. Notably, MiPa became the state-of-the-art on the LLVIP visible/infrared benchmark. Code: https://github.com/heitorrapela/MiPa

Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology

Given the emergence of deep learning, digital pathology has gained popularity for cancer diagnosis based on histology images. Deep weakly supervised object localization (WSOL) models can be trained to classify histology images according to cancer grade and identify regions of interest (ROIs) for interpretation, using inexpensive global image-class annotations. A WSOL model initially trained on some labeled source image data can be adapted using unlabeled target data in cases of significant domain shifts caused by variations in staining, scanners, and cancer type. In this paper, we focus on source-free (unsupervised) domain adaptation (SFDA), a challenging problem where a pre-trained source model is adapted to a new target domain without using any source domain data for privacy and efficiency reasons. SFDA of WSOL models raises several challenges in histology, most notably because they are not intended to adapt for both classification and localization tasks. In this paper, 4 state-of-the-art SFDA methods, each one representative of a main SFDA family, are compared for WSOL in terms of classification and localization accuracy. They are the SFDA-Distribution Estimation, Source HypOthesis Transfer, Cross-Domain Contrastive Learning, and Adaptively Domain Statistics Alignment. Experimental results on the challenging Glas (smaller, breast cancer) and Camelyon16 (larger, colon cancer) histology datasets indicate that these SFDA methods typically perform poorly for localization after adaptation when optimized for classification.

Realistic Model Selection for Weakly Supervised Object Localization

Weakly Supervised Object Localization (WSOL) allows for training deep learning models for classification and localization, using only global class-level labels. The lack of bounding box (bbox) supervision during training represents a considerable challenge for hyper-parameter search and model selection. Earlier WSOL works implicitly observed localization performance over a test set which leads to biased performance evaluation. More recently, a better WSOL protocol has been proposed, where a validation set with bbox annotations is held out for model selection. Although it does not rely on the test set, this protocol is unrealistic since bboxes are not available in real-world applications, and when available, it is better to use them directly to fit model weights. Our initial empirical analysis shows that the localization performance of a model declines significantly when using only image-class labels for model selection (compared to using bounding-box annotations). This suggests that adding bounding-box labels is preferable for selecting the best model for localization. In this paper, we introduce a new WSOL validation protocol that provides a localization signal without the need for manual bbox annotations. In particular, we leverage noisy pseudo boxes from an off-the-shelf ROI proposal generator such as Selective-Search, CLIP, and RPN pretrained models for model selection. Our experimental results with several WSOL methods on ILSVRC and CUB-200-2011 datasets show that our noisy boxes allow selecting models with performance close to those selected using ground truth boxes, and better than models selected using only image-class labels.

Modality Translation for Object Detection Adaptation Without Forgetting Prior Knowledge

A common practice in deep learning consists of training large neural networks on massive datasets to perform accurately for different domains and tasks. While this methodology may work well in numerous application areas, it only applies across modalities due to a larger distribution shift in data captured using different sensors. This paper focuses on the problem of adapting a large object detection model to one or multiple modalities while being efficient. To do so, we propose ModTr as an alternative to the common approach of fine-tuning large models. ModTr consists of adapting the input with a small transformation network trained to minimize the detection loss directly. The original model can therefore work on the translated inputs without any further change or fine-tuning to its parameters. Experimental results on translating from IR to RGB images on two well-known datasets show that this simple ModTr approach provides detectors that can perform comparably or better than the standard fine-tuning without forgetting the original knowledge. This opens the doors to a more flexible and efficient service-based detection pipeline in which, instead of using a different detector for each modality, a unique and unaltered server is constantly running, where multiple modalities with the corresponding translations can query it. Code: https://github.com/heitorrapela/ModTr.

Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces the Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by finding and aligning body part features extracted from both I and V modalities. Indeed, BMDG aims to reduce the modality gaps in two steps. First, it aligns modalities in feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. In particular, our method minimizes the cross-modal gap by identifying and aligning shared prototypes that capture key discriminative features across modalities, then uses multiple bridging steps based on this information to enhance the feature representation. Experiments conducted on challenging V-I ReID datasets indicate that our BMDG approach outperforms state-of-the-art part-based models or methods that generate an intermediate domain from V-I person ReID.

Joint Multimodal Transformer for Dimensional Emotional Recognition in the Wild

Audiovisual emotion recognition (ER) in videos has immense potential over unimodal performance. It effectively leverages the inter- and intra-modal dependencies between visual and auditory modalities. This work proposes a novel audio-visual emotion recognition system utilizing a joint multimodal transformer architecture with key-based cross-attention. This framework aims to exploit the complementary nature of audio and visual cues (facial expressions and vocal patterns) in videos, leading to superior performance compared to solely relying on a single modality. The proposed model leverages separate backbones for capturing intra-modal temporal dependencies within each modality (audio and visual). Subsequently, a joint multimodal transformer architecture integrates the individual modality embeddings, enabling the model to effectively capture inter-modal (between audio and visual) and intra-modal (within each modality) relationships. Extensive evaluations on the challenging Affwild2 dataset demonstrate that the proposed model significantly outperforms baseline and state-of-the-art methods in ER tasks.

Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptive Object Detection

Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains. Multi-source domain adaptation (MSDA) allows leveraging multiple annotated source datasets, and unlabeled target data to improve the accuracy and robustness of the detection model. Most state-of-the-art MSDA methods for OD perform feature alignment in a class-agnostic manner. This is challenging since the objects have unique modal information due to variations in object appearance across domains. A recent prototype-based approach proposed a class-wise alignment, yet it suffers from error accumulation due to noisy pseudo-labels which can negatively affect adaptation with imbalanced data. To overcome these limitations, we propose an attention-based class-conditioned alignment scheme for MSDA that aligns instances of each object category across domains. In particular, an attention module coupled with an adversarial domain classifier allows learning domain-invariant and class-specific instance representations. Experimental results on multiple benchmarking MSDA datasets indicate that our method outperforms the state-of-the-art methods and is robust to class imbalance. Our code is available at https://github.com/imatif17/ACIA.