Visual Question Answering (VQA) is a challenging task due to the vision-language biases which restrict the model to sufficiently learn the multi-modal knowledge from visual image and natural language simultaneously. Several recent works attempt to alleviate this problem via weakening language prior but ignore vision prior, hindering further performance improvement. In this paper, we propose a novel Deliberately Dual-Channel Adversarial Learning (DCAL) to make "V" and "Q" inseparable, which aims to weaken prior from both vision and language. Specifically, DCAL introduces in-batch random negative sampling to force the model to be wrong when given the wrong questions or images. DCAL maximizes the likelihood of correct answers for the original question-image pairs and minimizes it for random negative samples. In order to solve the problem of false negatives, DCAL exploits a deliberate training strategy to utilize the sampled question-image pairs. The proposed DCAL is model-agnostic and can be applied to various VQA models. Experiments demonstrate that our proposed DCAL method improves the performance of existing robust VQA models on the sensitive VQA-CP dataset while performing robustly on the balanced VQA v2 dataset.

Vision-and-Language Navigation tasks require an agent to navigate to a destination following natural language instructions. We focus on a challenging VLN dataset, Aerial Vision-and-Dialog Navigation, which encompasses a diverse array of environments and includes an additional altitude variable. Significant spatial and scale variations in the aerial agent's view make destination visual grounding a crucial capability for the navigation task. However, the existing frameworks not only have insufficient attention to the vision model, but also lack the correlations between visual and textual modalities. To address this, we propose a model that aligns destination visual images with navigation instructions, featuring three innovative components. Firstly, we propose a multi-stage pre-training pipeline that enhances the model's ability to associate language instructions with top-view images of destinations. Secondly, trajectories are augmented elastically to simulate the noise of the controlling process. Finally, a polygon regression loss function is introduced for rotated object detection, which significantly enhances the accuracy of altitude and orientation estimation. Experiments demonstrate the effectiveness of our approach, which achieves state-of-the-art advancements with improvements of 2.9% in the val unseen dataset and 3.0% in test unseen dataset in success weighted by path length.

Recent object re-identification (Re-ID) methods gain high efficiency via lightweight student models trained by knowledge distillation (KD). However, the huge architectural difference between lightweight students and heavy teachers causes students to have difficulties in receiving and understanding teachers' knowledge, thus losing certain accuracy. To this end, we propose a refiner-expander-refiner (RER) structure to enlarge a student's representational capacity and prune the student's complexity. The expander is a multi-branch convolutional layer to expand the student's representational capacity to understand a teacher's knowledge comprehensively, which does not require any feature-dimensional adapter to avoid knowledge distortions. The two refiners are 1×1 convolutional layers to prune the input and output channels of the expander. In addition, in order to alleviate the competition accuracy-related and pruning-related gradients, we design a common consensus gradient resetting (CCGR) method, which discards unimportant channels according to the intersection of each sample's unimportant channel judgment. Finally, the trained RER can be simplified into a slim convolutional layer via re-parameterization to speed up inference. As a result, we propose an expanding and refining hybrid compressing (ERHC) method. Extensive experiments show that our ERHC has superior inference speed and accuracy, e.g., on the VeRi-776 dataset, given the ResNet101 as a teacher, ERHC saves 75.33% model parameters (MP) and 74.29% floating-point of operations (FLOPs) without sacrificing accuracy.

Abstract Object re‐identification, such as vehicle re‐identification or pedestrian re‐identification, plays a significant role in intelligent video surveillance systems for public security. Due to viewpoint variations and appearance changes, both pedestrians and vehicles usually have complex intra‐class variations. However, most existing object re‐identification methods often use a sample‐level triplet loss function cooperating with a single‐proxy softmax loss function, which could not handle complex intra‐class variations well. In this paper, a sample‐proxy dual triplet (SPDT) loss function is proposed, which works with a multi‐proxy softmax (MPS) loss function. The MPS loss function is in charge of learning multiple proxies to represent a class. The SPDT loss function is responsible for enlarging inter‐class distances as well as shrinking intra‐class distances on both sample and proxy levels. Therefore, the method not only handles multi‐proxy intra‐class variations but also fully learns discrimination on samples and proxies. Experiments on two large datasets, that is, VeRi776 and DukeMTMC‐reID, demonstrate that the method is superior to state‐of‐the‐art object re‐identification approaches.