Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group. By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make better assessment which in turn helps to learn a better generator. The proposed RankGAN is optimized through the policy gradient technique. Experimental results on multiple public datasets clearly demonstrate the effectiveness of the proposed approach.

As an extension of traditional visual single object tracking (SOT) task [2, 3, 4], VLT can harness the complementary advantages of multiple modalities. Therefore, vision-language trackers (VLTs) have the potential to achieve more promising tracking performance, which has recently attracted widespreadattention[5,6,7,8].

In this paper, we introduce the CityRefer dataset for city-level 3D visual grounding.

With the proposed novel sampling strategy, our model achieves instance-aware colorization in diverse and complex scenarios.

We use decision trees to convey this reasoning information, as they can be easily represented in natural language, effectively providing knowledge from prior experiments ( i.e., the impact of the generated features on performance) to

In this work, we propose InteractTraj, the first language-driven traffic trajectory generator that can generate interactive traffic trajectories.

Recent development in vision-language approaches has instigated a paradigm shift in learning visual recognition models from language supervision. These approaches align objects with language queries (e.g. "a photo of a cat") and thus improve the models' adaptability to novel objects and domains.

We automatically annotate the aligned motions with language descriptions that depict the action and the unique interacting objects in the scene;e.g., sit on the armchair near the desk. HUMANISE thus enables a new generation task,language-conditioned human motion generation in 3D scenes.The proposed task is challenging as itrequires joint modeling of the 3D scene, human motion, and natural language.