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Haoxuan Li 0002, Yi Bin, Junrong Liao, Yang Yang 0002, Heng Tao Shen

Most existing image-text matching methods adopt triplet loss as the optimization objective, and choosing a proper negative sample for the triplet of is important for effectively training the model, e.g., hard negatives make the model learn efficiently and effectively. However, we observe that existing methods mainly employ the most similar samples as hard negatives, which may not be true negatives. In other words, the samples with high similarity but not paired with the anchor may reserve positive semantic associations, and we call them false negatives. Repelling these false negatives in triplet loss would mislead the semantic representation learning and result in inferior retrieval performance. In this paper, we propose a novel False Negative Elimination (FNE) strategy to select negatives via sampling, which could alleviate the problem introduced by false negatives. Specifically, we first construct the distributions of positive and negative samples separately via their similarities with the anchor, based on the features extracted from image and text encoders. Then we calculate the false negative probability of a given sample based on its similarity with the anchor and the above distributions via the Bayes' rule, which is employed as the sampling weight during negative sampling process. Since there may not exist any false negative in a small batch size, we design a memory module with momentum to retain a large negative buffer and implement our negative sampling strategy spanning over the buffer. In addition, to make the model focus on hard negatives, we reassign the sampling weights for the simple negatives with a cut-down strategy. The extensive experiments are conducted on Flickr30K and MS-COCO, and the results demonstrate the superiority of our proposed false negative elimination strategy. The code is available at https://github.com/LuminosityX/FNE.

Haokun Wen, Xian Zhang, Xuemeng Song, Yinwei Wei, Liqiang Nie

Composed image retrieval (CIR) is a new and flexible image retrieval paradigm, which can retrieve the target image for a multimodal query, including a reference image and its corresponding modification text. Although existing efforts have achieved compelling success, they overlook the conflict relationship modeling between the reference image and the modification text for improving the multimodal query composition and the adaptive matching degree modeling for promoting the ranking of the candidate images that could present different levels of matching degrees with the given query. To address these two limitations, in this work, we propose a Target-Guided Composed Image Retrieval network (TG-CIR). In particular, TG-CIR first extracts the unified global and local attribute features for the reference/target image and the modification text with the contrastive language-image pre-training model (CLIP) as the backbone, where an orthogonal regularization is introduced to promote the independence among the attribute features. Then TG-CIR designs a target-query relationship-guided multimodal query composition module, comprising a target-free student composition branch and a target-based teacher composition branch, where the target-query relationship is injected into the teacher branch for guiding the conflict relationship modeling of the student branch. Last, apart from the conventional batch-based classification loss, TG-CIR additionally introduces a batch-based target similarity-guided matching degree regularization to promote the metric learning process. Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed method.

Wenjie Wang 0007, Xinyu Lin 0001, Liuhui Wang, Fuli Feng, Yinwei Wei, Tat-Seng Chua

Recommender systems rely on user-item interactions to learn Collaborative Filtering (CF) signals and easily under-recommend the cold-start items without historical interactions. To boost cold-start item recommendation, previous studies usually incorporate item features (e.g., micro-video content features) into CF models. They essentially align the feature representations of warm-start items with CF representations during training, and then adopt the feature representations of cold-start items to make recommendations. However, cold-start items might have feature distribution shifts from warm-start ones due to different upload times. As such, these cold-start item features fall into the underrepresented feature space, where their feature representations cannot align well with CF signals, causing poor cold-start recommendation. To combat item feature shifts, the key lies in pushing feature representation learning to well represent the shifted item features and align with the CF representations in the underrepresented feature space. To this end, we propose an equivariant learning framework, which aims to achieve equivariant alignment between item features, feature representations, and CF representations in the underrepresented feature space. Specifically, since cold-start items are unavailable for training, we interpolate the features and CF representations of two underrepresented warm items to simulate the feature shifts. The interpolated feature representations are then regulated to achieve equivariant alignment with the interpolated features and CF representations via three alignment losses. We instantiate the proposed framework on two competitive cold-start models, and empirical results on three datasets validate that the framework significantly improves cold-start recommendation.

Yishu Liu 0001, Qingpeng Wu, Zheng Zhang 0006, Jingyi Zhang, Guangming Lu 0002

With the powerful representation ability and privileged efficiency, deep cross-modal hashing (DCMH) has become an emerging fast similarity search technique. Prior studies primarily focus on exploring pairwise similarities across modalities, but fail to comprehensively capture the multi-grained semantic correlations during intra- and inter-modal negotiation. To tackle this issue, this paper proposes a novel Multi-granularity Interactive Transformer Hashing (MITH) network, which hierarchically considers both coarse- and fine-grained similarity measurements across different modalities in one unified transformer-based framework. To the best of our knowledge, this is the first attempt for multi-granularity transformer-based cross-modal hashing. Specifically, a well-designed distilled intra-modal interaction module is deployed to excavate modality-specific concept knowledge with global-local knowledge distillation under the guidance of implicit conceptual category-level representations. Moreover, we construct a contrastive inter-modal alignment module to mine modality-independent semantic concept correspondences with instance- and token-wise contrastive learning, respectively. Such a collaborative learning paradigm can jointly alleviate the heterogeneity and semantic gaps among different modalities from a multi-granularity perspective, yielding discriminative modality-invariant hash codes. Extensive experiments on multiple representative cross-modal datasets demonstrate the consistent superiority of MITH over the existing state-of-the-art baselines. The codes are available at https://github.com/DarrenZZhang/MITH.

Peiliang Gong, Ziyu Jia, Pengpai Wang, Yueying Zhou, Daoqiang Zhang

Emotion recognition based on electroencephalography (EEG) has attracted significant attention and achieved considerable advances in the fields of affective computing and human-computer interaction. However, most existing studies ignore the coupling and complementarity of complex spatiotemporal patterns in EEG signals. Moreover, how to exploit and fuse crucial discriminative aspects in high redundancy and low signal-to-noise ratio EEG signals remains a great challenge for emotion recognition. In this paper, we propose a novel attention-based spatial-temporal dual-stream fusion network, named ASTDF-Net, for EEG-based emotion recognition. Specifically, ASTDF-Net comprises three main stages: first, the collaborative embedding module is designed to learn a joint latent subspace to capture the coupling of complicated spatiotemporal information in EEG signals. Second, stacked parallel spatial and temporal attention streams are employed to extract the most essential discriminative features and filter out redundant task-irrelevant factors. Finally, the hybrid attention-based feature fusion module is proposed to integrate significant features discovered from the dual-stream structure to take full advantage of the complementarity of the diverse characteristics. Extensive experiments on two publicly available emotion recognition datasets indicate that our proposed approach consistently outperforms state-of-the-art methods.

Zixin Zhang 0004, Fan Qi, Shuai Li, Changsheng Xu

Federated affective computing, which deploys traditional affective computing in a distributed framework, achieves a trade-off between privacy and utility, and offers a wide variety of applications in business and society. However, the expensive annotation cost of obtaining reliable emotion labels at the local client remains a barrier to the effective use of local emotional data. Therefore, we propose a federated active affective paradigm to improve the performance of federated affective computing with a limited annotation budget on the client. A major challenge in federated active learning is the inconsistency between the active sampling goals of global and local models, particularly in scenarios with Non-IID data across clients, which exacerbates the problem. To address the above challenge, we propose AffectFAL, a federated active affective computing framework. It incorporates a Preference-aware Group Aggregation module, which obtains global models representing the different emotional preferences among clients. We also devise a tailored De-biased Federated Active Sampling strategy with an improved vote entropy, facilitating class balancing of labeled samples and alleviating the problem of sampling goals inconsistency between the global and local models. We evaluate AffectFAL on diverse benchmarks (image, video and physiological signal) and experimental settings for affective computing. Thorough comparisons with other active sampling strategies demonstrate our method's advantages in affective computing for Non-IID federated learning.

Junjie Zhu, Bingjun Luo, Ao Sun, Jinghang Tan, Xibin Zhao, Yue Gao 0002

Despite the great accomplishments of facial expression recognition (FER) models in closed-set scenarios, they still lack open-world robustness when it comes to handling unknown samples. To address the demands of operating in an open environment, open-set FER models should improve their performance in rejecting unknown samples while maintaining their efficiency in recognizing known expressions. With this goal in mind, we propose an open-set FER framework named Variance-Aware Bi-Attention Expression Transformer (VBExT), which enhances conventional closed-set FER models with open-world robustness for unknown samples. Specifically, to make full use of the expression representation capabilities of learned features, we introduce a bi-attention feature augmentation mechanism that learns the important regions and integrates the hierarchical features extracted by the emotional CNN backbone. We also propose a variance-aware distribution modeling method that adapts to the diverse distribution of different expression classes in the open environment, thereby enhancing the detection ability of unknown expressions. Additionally, we have constructed a Fine-Grained Light Facial Expression dataset that includes 30 different light brightnesses to better validate the efficiency of VBExT. Extensive experiments and ablation studies show that VBExT significantly improves the performance of open-set FER and achieves state-of-the-art results on CFEE (lab, basic), RAF-DB (wild, basic+compound), and FGL-FE (multiple light brightnesses, basic).

Peiguang Jing, Xianyi Liu, Ji Wang, Yinwei Wei, Liqiang Nie, Yuting Su 0001

Emotion distribution learning has gained increasing attention with the tendency to express emotions through images. As for emotion ambiguity arising from humans' subjectivity, substantial previous methods generally focused on learning appropriate representations from the holistic or significant part of images. However, they rarely consider establishing connections with the stylistic information although it can lead to a better understanding of images. In this paper, we propose a style-guided high-order attention network for image emotion distribution learning termed StyleEDL, which interactively learns stylistic-aware representations of images by exploring the hierarchical stylistic information of visual contents. Specifically, we consider exploring the intra- and inter-layer correlations among GRAM-based stylistic representations, and meanwhile exploit an adversary-constrained high-order attention mechanism to capture potential interactions between subtle visual parts. In addition, we introduce a stylistic graph convolutional network to dynamically generate the content-dependent emotion representations to benefit the final emotion distribution learning. Extensive experiments conducted on several benchmark datasets demonstrate the effectiveness of our proposed StyleEDL compared to state-of-the-art methods. The implementation is released at: https://github.com/liuxianyi/StyleEDL.

Zeng Tao, Yan Wang 0068, Zhaoyu Chen 0001, Boyang Wang 0003, Shaoqi Yan, Kaixun Jiang, Shuyong Gao, Wenqiang Zhang

The in-the-wild dynamic facial expression recognition (DFER) has been challenging due to several high-dynamics factors such as limited dynamic expression-related frames and variable non-expression noise in facial expression sequences. To provide more expression-related clips for DFER models, we propose a novel and interpretable frequency-based method (Freq-HD) for high-dynamics affective clip selection. It can select clips containing pure expression changes from sequences and aid different DFER network structures in recognizing in-the-wild dynamic facial expressions more accurately and efficiently. We first design a novel spatial-temporal frequency analysis (STFA) module to compute the dynamics values of each clip by using sliding windows and spatial-temporal frequency analysis. Moreover, we propose a multi-band complementary selection (MBC) module to amend the inappropriate reaction of the dynamics values of different spatial frequency bands in STFA when expression-irrelevant noise occurs. Specifically, the MBC uses an ingenious mapping method to generate the inhibitory factors to complement and separate the dynamics of expressions and non-expressions in different frequency bands. The Freq-HD can select the most expression-correlated clips and the consisting frames, which could be incorporated into any existing DFER models. We extensively evaluate the Freq-HD on two in-the-wild datasets and four DFER baselines, showing that our method significantly improves the subsequent network performance while using fewer input frames and reducing computation cost. More ablation studies and visualization analysis provide further empirical evidence of the effectiveness of our method.

Daoming Zong, Chaoyue Ding, Baoxiang Li, Jiakui Li, Ken Zheng, Qunyan Zhou 0002

Multimodal Sentiment Analysis (MSA) is a popular research topic aimed at utilizing multimodal signals for understanding human emotions. The primary approach to solving this task is to develop complex fusion techniques. However, the heterogeneity and unaligned nature between modalities pose significant challenges to fusion. Additionally, existing methods lack consideration for the efficiency of modal fusion. To tackle these issues, we propose AcFormer, which contains two core ingredients: i) contrastive learning within and across modalities to explicitly align different modality streams before fusion; and ii) pivot attention for multimodal interaction/fusion. The former encourages positive triplets of image-audio-text to have similar representations in contrast to negative ones. The latter introduces attention pivots that can serve as cross-modal information bridges and limit cross-modal attention to a certain number of fusion pivot tokens. We evaluate AcFormer on multiple MSA tasks, including multimodal emotion recognition, humor detection, and sarcasm detection. Empirical evidence shows that AcFormer achieves the optimal performance with minimal computation cost compared to previous state-of-the-art methods. Our code is publicly available at https://github.com/dingchaoyue/AcFormer.

Fan Jiang 0009, Zilei Wang

Efficient and accurate perception system is critical for autonomous driving, including traffic object detection, drivable area segmentation, and lane detection. Most previous works do not consider the spatial and semantic cues in traffic scenes. In this paper, we propose a novel multi-task learning network to exploit these priors. Specifically, to model the co-occurrence and spatial relationships of traffic objects, we propose to use a Graph Convolutional Network (GCN) block operating on the patches of feature maps. It enables adaptive discovery and incorporation of semantic and spatial relationships in the feature space. Furthermore, we propose a sub-feature sharing method to mitigate negative transfer in multi-task learning. On the basis of a fully shared base network, we split the feature space of different tasks along the channel dimension, resulting in the shared and private features for each task. It allows the network parameters to be selectively updated by different tasks during training. Experimental results on the challenging BDD100K dataset demonstrate that our proposed approach gets consistent improvement with fewer parameters, and achieves new state-of-the-art performance in terms of accuracy and speed.

Shizhou Zhang, Qingchun Yang, De Cheng, Yinghui Xing, Guoqiang Liang 0001, Peng Wang 0015, Yanning Zhang 0001

In this work, we construct a large-scale dataset for Ground-to-Aerial Person Search, named G2APS, which contains 31,770 images of 260,559 annotated bounding boxes for 2,644 identities appearing in both of the UAVs and ground surveillance cameras. To our knowledge, this is the first dataset for cross-platform intelligent surveillance applications, where the UAVs could work as a powerful complement for the ground surveillance cameras. To more realistically simulate the actual cross-platform Ground-to-Aerial surveillance scenarios, the surveillance cameras are fixed about 2 meters above the ground, while the UAVs capture videos of persons at different location, with a variety of view-angles, flight attitudes and flight modes. Therefore, the dataset has the following unique characteristics: 1) drastic view-angle changes between query and gallery person images from cross-platform cameras; 2) diverse resolutions, poses and views of the person images under 9 rich real-world scenarios. On basis of the G2APS benchmark dataset, we demonstrate detailed analysis about current two-step and end-to-end person search methods, and further propose a simple yet effective knowledge distillation scheme on the head of the ReID network, which achieves state-of-the-art performances on both of the G2APS and the previous two public person search datasets, i.e., PRW and CUHK-SYSU. The dataset and source code available on https://github.com/yqc123456/HKD_for_person_search.

Sun'ao Liu, Yiheng Zhang, Zhaofan Qiu, Hongtao Xie 0001, Yongdong Zhang 0001, Ting Yao 0003

Referring image segmentation aims to segment the target object described by a natural-language utterance. Recent approaches typically distinguish pixels by aligning pixel-wise visual features with linguistic features extracted from the referring description. Nevertheless, such a free-form description only specifies certain discriminative attributes of the target object or its relations to a limited number of objects, which fails to represent the rich visual context adequately. The stand-alone linguistic features are therefore unable to align with all visual concepts, resulting in inaccurate segmentation. In this paper, we propose to address this issue by incorporating rich visual context into linguistic features for sufficient vision-language alignment. Specifically, we present Context-Aware Referring Image Segmentation (CARIS), a novel architecture that enhances the contextual awareness of linguistic features via sequential vision-language attention and learnable prompts. Technically, CARIS develops a context-aware mask decoder with sequential bidirectional cross-modal attention to integrate the linguistic features with visual context, which are then aligned with pixel-wise visual features. Furthermore, two groups of learnable prompts are employed to delve into additional contextual information from the input image and facilitate the alignment with non-target pixels, respectively. Extensive experiments demonstrate that CARIS achieves new state-of-the-art performances on three public benchmarks. Code is available at https://github.com/lsa1997/CARIS.

Jiawei Liang, Siyuan Liang 0004, Aishan Liu, Ke Ma 0001, Jingzhi Li 0002, Xiaochun Cao

Knowledge Distillation (KD) for object detection aims to train a compact detector by transferring knowledge from a teacher model. Since the teacher model perceives data in a way different from humans, existing KD methods only distill knowledge that is consistent with labels annotated by human expert while neglecting knowledge that is not consistent with human perception, which results in insufficient distillation and sub-optimal performance. In this paper, we propose inconsistent knowledge distillation (IKD), which aims to distill knowledge inherent in the teacher model's counter-intuitive perceptions. We start by considering the teacher model's counter-intuitive perceptions of frequency and non-robust features. Unlike previous works that exploit fine-grained features or introduce additional regularizations, we extract inconsistent knowledge by providing diverse input using data augmentation. Specifically, we propose a sample-specific data augmentation to transfer the teacher model's ability in capturing distinct frequency components and suggest an adversarial feature augmentation to extract the teacher model's perceptions of non-robust features in the data. Extensive experiments demonstrate the effectiveness of our method which outperforms state-of-the-art KD baselines on one-stage, two-stage and anchor-free object detectors (at most +1.0 mAP). Our codes will be made available at https://github.com/JWLiang007/IKD.git.

Yang Bai, Jingyao Wang, Min Cao 0005, Chen Chen 0036, Ziqiang Cao, Liqiang Nie, Min Zhang 0005

Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description. Existing methods are dominated by training models with parallel image-text pairs, which are very costly to collect. In this paper, we make the first attempt to explore TBPS without parallel image-text data (μ-TBPS), in which only non-parallel images and texts, or even image-only data, can be adopted. Towards this end, we propose a two-stage framework, generation-then-retrieval (GTR), to first generate the corresponding pseudo text for each image and then perform the retrieval in a supervised manner. In the generation stage, we propose a fine-grained image captioning strategy to obtain an enriched description of the person image, which firstly utilizes a set of instruction prompts to activate the off-the-shelf pretrained vision-language model to capture and generate fine-grained person attributes, and then converts the extracted attributes into a textual description via the finetuned large language model or the hand-crafted template. In the retrieval stage, considering the noise interference of the generated texts for training model, we develop a confidence score-based training scheme by enabling more reliable texts to contribute more during the training. Experimental results on multiple TBPS benchmarks (i.e., CUHK-PEDES, ICFG-PEDES and RSTPReid) show that the proposed GTR can achieve a promising performance without relying on parallel image-text data.

Xueyi Zhang 0001, Chengwei Zhang, Tao Wang 0074, Jun Tang 0001, Songyang Lao, Haizhou Li 0001

Class incremental learning has yet to be explored in the field of lip-reading, which can circumvent data privacy issues and avoid the high training costs associated with joint training. In this paper, we introduce a benchmark for Class-Incremental Lip-Reading (CILR). To simultaneously improve the plasticity for new classes and stability for old classes in incremental learning, we propose a Slow-Fast Time Parameter Aggregation Network (TPAN) that decouples representation learning of new and old knowledge, taking into account the task characteristics of lip-reading. The TPAN comprises two dynamically evolving branches: one that uses fast gradient descent and the other employs slow momentum updates to retain old knowledge while adapting to new knowledge. Additionally, to achieve efficient knowledge transfer of the incremental model, we design a Hybrid Sequence-Distribution Distillation (HSDD) strategy to transfer knowledge in temporal feature view and classification probability view. We present a comprehensive comparison of the proposed method and previous state-of-the-art class incremental learning methods on the most commonly used lip-reading datasets LRW and LRW1000. The experimental result show that the proposed method can reduce the effect of catastrophic forgetting and improve the incremental accuracy.

Qichao Ying, Jiaxin Liu, Sheng Li 0006, Haisheng Xu, Zhenxing Qian, Xinpeng Zhang 0001

The widespread use of face retouching filters on short-video platforms has raised concerns about the authenticity of digital appearances and the impact of deceptive advertising. To address these issues, there is a pressing need to develop advanced face retouching techniques. However, the lack of large-scale and fine-grained face retouching datasets has been a major obstacle to progress in this field. In this paper, we introduce RetouchingFFHQ, a large-scale and fine-grained face retouching dataset that contains over half a million conditionally-retouched images. RetouchingFFHQ stands out from previous datasets due to its large scale, high quality, fine-grainedness, and customization. By including four typical types of face retouching operations and different retouching levels, we extend the binary face retouching detection into a fine-grained, multi-retouching type, and multi-retouching level estimation problem. Additionally, we propose a Multi-granularity Attention Module (MAM) as a plugin for CNN backbones for enhanced cross-scale representation learning. Extensive experiments using different baselines as well as our proposed method on RetouchingFFHQ show decent performance on face retouching detection.

Liu Liu 0012, Jianming Du, Hao Wu 0040, Xun Yang 0001, Zhenguang Liu, Richang Hong, Meng Wang 0001

Human life is populated with articulated objects. Current category-level articulated object 9D pose estimation (Articulated Object 9D Pose Estimation, ArtOPE) methods usually meet the challenges of shared object representation requirement, kinematics-agnostic pose modeling and self-occlusions. In this paper, we propose a novel framework called Articulated object 9D Pose Estimation via Reinforcement Learning (ArtPERL), which formulates the category-level ArtOPE as a reinforcement learning problem. Given a point cloud or RGB-D image input, ArtPERL firstly retrieves the part-sensitive articulated object as reference point cloud, and then introduces a joint-centric pose modeling strategy that estimates 9D pose by fitting joint states via reinforced agent training. Finally, we further propose a pose optimization that refine the predicted 9D pose considering kinematic constraints. We evaluate our ArtPERL on various datasets ranging from synthetic point cloud to real-world multi-hinged object. Experiments demonstrate the superior performance and robustness of our ArtPERL. Our work provides a new perspective on category-level articulated object 9D pose estimation and has the potential to be applied in many fields, including robotics, augmented reality, and autonomous driving.

Lingfeng Li, Gangming Zhao, Yizhou Yu, Jinpeng Li 0002

Avascular necrosis of the femoral head (AVNFH) is a common orthopedic disease that seriously affects the life quality of middle-aged and elderly people. Early AVNFH is difficult to diagnose due to its complex symptoms. In recent years, some works have applied deep learning algorithms to find traces of early AVNFH in X-rays or magnetic resonance imaging (MRI). However, X-rays are difficult to reflect hidden features due to the tissue overlap; MRI is sensitive but requires more time for imaging and is expensive. This study aims to develop a computer-aided diagnosis system for early AVNFH based on computed tomography (CT), which provides layer-wise features and is less costly. To achieve this, a large-scale dataset for AVNFH was collected and annotated by experienced doctors. We propose the Dynamic Triple Reweighting Network (DTRNet) that integrates the AVNFH classification and weakly-supervised localization. DTRNet incorporates nested multi-instance learning as the first and second reweighting, and structure regularization as the third reweighting to identify diseases and localize the lesion region. Since nested multi-instance learning is inapplicable in situations with few positive samples in the patch set, we propose a dynamic pseudo-package module to compensate for this limitation. Experimental results show that DTRNet is superior to the baselines in AVNFH classification. In addition, it can locate lesions to provide more information for assisting clinical decisions. The desensitized data and codes has been made available at: https://github.com/tomas-lilingfeng/DTRNet.

Lianyu Hu 0003, Liqing Gao, Zekang Liu, Chi-Man Pun, Wei Feng 0005

Raw videos have been proven to own considerable feature redundancy where in many cases only a portion of frames can already meet the requirements for accurate recognition. In this paper, we are interested in whether such redundancy can be effectively leveraged to facilitate efficient inference in continuous sign language recognition (CSLR). We propose a novel adaptive model (AdaBrowse) to dynamically select a most informative subsequence from input video sequences by modelling this problem as a sequential decision task. In specific, we first utilize a lightweight network to quickly scan input videos to extract coarse features. Then these features are fed into a policy network to intelligently select a subsequence to process. The corresponding subsequence is finally inferred by a normal CSLR model for sentence prediction. As only a portion of frames are processed in this procedure, the total computations can be considerably saved. Besides temporal redundancy, we are also interested in whether the inherent spatial redundancy can be seamlessly integrated together to achieve further efficiency, i.e., dynamically selecting a lowest input resolution for each sample, whose model is referred to as AdaBrowse+. Extensive experimental results on four large-scale CSLR datasets, i.e., PHOENIX14, PHOENIX14-T, CSL-Daily and CSL, demonstrate the effectiveness of AdaBrowse and AdaBrowse+ by achieving comparable accuracy with state-of-the-art methods with 1.44X throughput and 2.12X fewer FLOPs. Comparisons with other commonly-used 2D CNNs and adaptive efficient methods verify the effectiveness of AdaBrowse. Code is available at https://github.com/hulianyuyy/AdaBrowse.